Chen Liu
email: xmyliuchen@126.com

Qingyan Wang

Wenqian Huang
email: chuangwq@nercita.org.cn

Liping Chen

Baohua Zhang
email: ezhangbaohua126@126.com

Shuxiang Fan
email: ffanshuxiang126@126.com

Comparison of Four Types of Raman Spectroscopy for Noninvasive Determination of Carotenoids in Agricultural Products

Keywords: Carotenoids, Fourier transform Raman spectroscopy, Resonance Raman spectroscopy, Raman microspectroscopy, Spatially Offset Raman spectroscopy

Carotenoids are one class of naturally-occurring pigments with antioxidant properties. They can absorb light energy for use in photosynthesis for plants, and act as antioxidants to reduce risk of cancer for human. Carotenoids are confirmed to exist in agricultural products as the main source for human. Raman spectroscopy is a new technique for determination of carotenoids in agricultural products as it is both noninvasive and rapid. Four types of Raman spectroscopy could be used for contact measurement of carotenoids in fruits and vegetables: (1) Fourier transform Raman spectroscopy;

(2) Resonance Raman spectroscopy; (3) Raman microspectroscopy; (4) Spatially Offset Raman spectroscopy. The experimental setups, advantages and applications of the above-mentioned Raman spectroscopies are discussed.

Introduction

Carotenoids are one class of naturally-occurring pigments with antioxidant properties. They have positive effect on the immune system in animals and plants [START_REF] Blackburn | Quantifying Chlorophylls and Caroteniods at Leaf and Canopy Scales: An Evaluation of Some Hyperspectral Approaches[END_REF]. Most carotenoids cannot be synthesized by human beings directly. They widely exist in agricultural products such as tomatoes, watermelons, grapes and carrots. All carotenoids are produced from eight isoprene molecules and contain forty carbon atoms [START_REF] Viuda-Martos | Tomato and Tomato Byproducts. Human Health Benefits of Lycopene and Its Application to Meat Products: A Review[END_REF]. The best known carotenoids include lycopene and beta-carotene which is the vitamin A precursor. They all belong to the class of carotene which contains only carbon and hydrogen and no oxygen. There is another class called xanthophyll which contains oxygen such as lutein, zeaxanthin and astaxanthin. Carotenoids as natural pigments also contribute to the plants color when chlorophyll is not present [START_REF] Yang | Applications of Raman Spectroscopy in Agricultural Products and Food Analysis: A Review[END_REF]. That means carotenoids abound in ripe fruits and vegetables with the color of yellows, reds, or oranges [START_REF] Zheng | Surface-Enhanced Raman Spectroscopy for the Chemical Analysis of Food [J][END_REF].

At present, chromatography and spectroscopy are two common methods applied to determinate carotenoids in agricultural products [START_REF] Pacheco | Microscale extraction method for HPLC carotenoid analysis in vegetable matrices[END_REF][START_REF] Payyavula | Differential effects of environment on potato phenylpropanoid and carotenoid expression [J][END_REF][START_REF] Fraser | The biosynthesis and nutritional uses of carotenoids [J][END_REF][START_REF] Rodriguez-Amaya D B | Updated Brazilian database on food carotenoids: Factors affecting carotenoid composition [J][END_REF][START_REF] Wright | Improved HPLC method for the analysis of chlorophylls and carotenoids from marine phytoplankton[END_REF]. High-performance liquid chromatography (HPLC) is the representative of chromatography. It is widely used for quantitative analysis due to its low detection limit and high accuracy [START_REF] Stephanie | Single v. multiple measures of skin carotenoids by resonance Raman spectroscopy as a biomarker of usual carotenoid status[END_REF]. However, this method may cost considerable amount of time for sample preparation because this method need to destroy sample. It also requires elaborately extracting the chromophore before experiment. However, it need not take any trouble about preprocessing in spectrum detection. Raman spectroscopy is a well-suited noninvasive method in this context. It is a non-destructive, non-contact analytical technique result in little sample preparation [START_REF] Synytsya | Raman spectroscopy at different excitation wavelengths (1064, 785 and 532 nm) as a tool for diagnosis of colon cancer [J][END_REF][START_REF] Sselhausa | Raman Spectroscopy of Carbon Nanotubes[END_REF][START_REF] Moskovits | Surface-enhanced Raman spectroscopy: A brief retrospective[END_REF]. Based on that, many variations of Raman spectroscopies could be capable of enhancing the sensitivity, acquiring very specific information or improving the spatial resolution [START_REF] Li | Shell-isolated nanoparticle-enhanced Raman spectroscopy[END_REF][START_REF] Macernis | Resonance Raman Spectra and Electronic Transitions in Carotenoids: A DFT Study[END_REF][START_REF] He | Aptamer-based Surface-Enhanced Raman Scattering Detection of Ricin in Liquid Foods[END_REF][START_REF] Burgio | Library of FT-Raman spectra of pigments, minerals, pigment media and varnishes, and supplement to existing library of Raman spectra of pigments with visible excitation [J][END_REF]. Raman spectroscopy has been proved to be a useful and powerful tool for carotenoids analysis.

Raman spectroscopy for carotenoids analysis

A whole Raman spectroscopy instrument includes a laser excitation source, a sample chamber, a determination system and a data analysis system [START_REF] Du | Qualitative and Quantitative Determination of Melamine by Surface-Enhanced Raman Spectroscopy Using Silver Nanorod Array Substrates[END_REF]. The lasers as the excitation sources provide directional monochromatic lights in vast majority of Raman measurements. Monochromaticity, directionality, and coherence are three unique properties of Raman excitation light source. The laser excitation sources are usually provided by lasers at 488, 514, 532, 633, 780/785 or 1064 nm. Compared with the requirement of lasers for fluorescence excitation, the linewidth of lasers for Raman excitation needs to be narrower. Another indispensable part of Raman system is sample chamber, which generally consists of lens, filters and sample holders. The role of lens is to obtain the most effective irradiation on the sample, and to maximize the collection of scattered lights. Filters, normally including notch filters and edge filters, are used to prevent Rayleigh scattering into the detected signals. The specifications of the filters are of great concern to the low wavenumber performance of Raman spectroscopy. A detection system always be divided into two parts: spectrometer and detector. Many technical parameters of spectrometer, such as resolution, spectral range, diffraction efficiency, aberrations, and stray lights, have crucial impact on the whole Raman system. However, the spectrometer would cause great loss of light intensity, thereby resulting in the decrease of sensitivity. It might be a way to improve sensitivity that obtains the required lights directly from narrowband filters without spectrometer. The detector could use a charge-coupled device (CCD) or a photomultiplier detector to collect Raman spectrum. The data analysis system is responsible for data pre-processing, data computing and data storage.

As we known, Raman spectroscopy is characterized by strong bands produced from stretching vibration of specific nonpolar groups. Based on conventional Raman determination, the C-C and C=C bonds in carotenoids just belong to nonpolar groups, and it is easy to observe strong bands within 1150-1170 cm -1 and 1500-1550 cm -1 , respectively. Additionally, a polyene chain with CH 3 groups attached could generate Raman shift in the 1000-1020 cm -1 region. However, there are some deficiencies in the process of in vivo conventional Raman determination: (1) fluorescence of samples is strong during the determination; (2) Raman spectrum is not abundant to distinguish various carotenoids; (3) it is unable to observe the structure of carotenoids in this resolution level; (4) it cannot detect internal Raman spectrum of solid samples. To solve above problems, Fourier transform Raman spectroscopy (FT-Raman), Resonance Raman spectroscopy (RRS), Raman microspectroscopy, and spatially offset Raman spectroscopy (SORS) are introduced into in vivo determination of carotenoids. These techniques will be discussed below. Besides, surface-enhanced Raman spectroscopy (SERS) is another rapid detection method. It depends on enhancement module contacting with samples to enhance the Raman scattering. This paper is mainly concentrated on the introduction of non-invasive and in vivo Raman methods, so SERS method is not discussed in this paper.

Fourier Transform Raman Spectroscopy

Raman spectroscopy relies on Raman scattering which is very weak excited by intense laser light [START_REF] Ferrari | Raman Spectrum of Graphene and Graphene Layers[END_REF]. Normally, the wavelength range of laser is in the visible, near infrared or near ultraviolet range [START_REF] Kelly | Design Considerations for a Portable Raman Probe Spectrometer for Field Forensics[END_REF]. When an intense laser light illuminating on carotenoids, the electrons in molecules of carotenoids would emit light with low energy, leading to both fluorescence emission and Raman scattering in a wide wavelength range. Under this background, FT-Raman is developed to reduce the interference of fluorescence. FT-Raman uses an Nd:YAG laser (1064 nm) to obtain the maximum information available, moreover, the problem of fluorescence background could be circumvented. In addition, a longer integration time and higher power lasers are required at the same time [START_REF] Sampaio Mangolim | Curcumin-βcyclodextrin inclusion complex: Stability, solubility, characterisation by FT-IR, FT-Raman, X-ray diffraction and photoacoustic spectroscopy, and food application[END_REF].Figure 1 shows one type of FT-Raman experimental setup. The monochromatic light (1064 nm) emitting by the laser is collimated by the lens, reflected by the mirror, and irradiated on the sample. The Raman signal is collected by a Michelson interferometer after filtering by a dichroic filter. The Michelson interferometer could produce a relative improvement in signalto-noise ratio. Then, the scattering light passed through a dielectric filter and finally focused into a liquid N 2 cooled detector. The application of FT-Raman is determining special samples with strong fluorescence. FT-Raman eliminates the fluorescence background effectively by using excitation laser in near-infrared wavelength (1024 nm). It is an easy way to distinguish between similar isomers. Compare to Fourier transform infrared spectrum (FT-IR), FT-Raman shows good applicability on aqueous solution. For this reason, FT-Raman could determine carotenoids directly in vivo, such as plant epidermis, fruits, and human skin. H. Schulz et al. demonstrated the feasibility of FT-Raman spectroscopy applied on carotenoids determination in floristic kingdom.It presented an especial way to detect the distribution of several carotenoids and distinguish various quantity of double bonds conjugated carotenoids in the intact plant tissue [START_REF] Schulz | Potential of NIR-FT-Raman spectroscopy in natural carotenoid analysis[END_REF]. Similarly, Vanessa E. de Oliveira studied on natural carotenoids in more than 50 samples of plant tissue by FT-Raman. They recorded the characteristic bands of C-C and C=C stretching and C-CH 3 bending in each sample. They also found that the characteristic key bands were arose from their own molecular interactions. [START_REF] Oliveira | Carotenes and carotenoids in natural biological samples: a Raman spectroscopic analysis[END_REF]. Compared to ATR-IR and NIR reflection spectroscopy, FT-Raman spectrum of tomato products showed significant wavenumber shifts of targeted carotenoids, and it showed a good reliability on lycopene (R 2 =0.91 and SECV=74.34) and beta-carotene (R 2 =0.89 and SECV=0.34) [START_REF] Baranska | Determination of lycopene and beta-carotene content in tomato fruits and related products: Comparison of FT-Raman, ATR-IR, and NIR spectroscopy[END_REF]. Based on Raman images, the spatial distribution of carotenoids in intact carrot roots of different colors was evaluated by Malgorzata Baranska et al. [START_REF] Baranski | Changes in carotenoid content and distribution in living plant tissue can be observed and mapped in situ using NIR-FT-Raman spectroscopy[END_REF]. They also defined tissue specific accumulation of several variants of carotenoids by Raman images in the carrot root cross section [START_REF] Baranska | Tissue-specific accumulation of carotenoids in carrot roots[END_REF]. However, the laser device at 1064nm wavelength is always large and expensive, and this is a limiting factor of FT-Raman to be portable.

Resonance Raman Spectroscopy

The feature of RRS is to define characteristic vibration bands of corresponding model compound. RRS requires a special wavelength of the laser which is used to obtain the selective spectra. The excitation frequency must be close in energy to the electronic transition of the sample. For carotenoids, the optional excitation wavelength range depends on the numbers of C=C in molecules from 410 to 520 nm. Carotenoids like lycopene, alpha-carotene, beta-carotene and their isomers, which own 11 conjugated carbon double-bonds, have approximate Raman shift at 488 nm. However, only lycopene owns special peak position at 514 nm with the maximal difference [START_REF] Darvin | Non-invasive in vivo determination of the carotenoids beta-carotene and lycopene concentrations in the human skin using the Raman spectroscopic method [J][END_REF]. In other carotenoids, the maximum of absorption values are located at 515 and 490 nm for diadinoxanthin and diatoxanthin, respectively [START_REF] Alexandre | Probing the carotenoid content of intact Cyclotella cells by resonance Raman spectroscopy[END_REF]. By comparing C=C band at different excitation wavelength for further study, the peak positions are very similar to those of lutein at 514, 496 or 476 nm. The peak position of violaxanthin is at 502 nm and another variant neoxanthin is at 488 or 458 nm [START_REF] Andreeva | Resonance Raman Studies of Carotenoid Molecules Within Photosystem I Particles[END_REF]. But in practical applications, agricultural products contain more than one type of carotenoids in most instances [START_REF] Withnall | Raman spectra of carotenoids in natural products[END_REF]. Based on that, the optimal excitation wavelength for most carotenoids is determined at 488 or 514 nm. Figure 2 shows a typical RRS experimental setup applied to carotenoids. The constitutive requirement of RRS is similar to FT-Raman. The main distinction is the exciting beam filtered by a BPF (band pass filter) and focused onto the sample, then using a dichroic filter and a LPF (low pass filter) to eliminating the laser. The band of BPF and LPF are according to the sample under test.

Fig.2. Schematic diagram of RRS experimental setup

The main advantage of RRS method is its high sensitivity. It makes the determination of a small quantity of carotenoid molecules in different agricultural products realized. Dane Bicanic et al. used RRS method combined with tristimulus colorimetry to rapidly detect beta-carotene in mango homogenates. At a low concentration of carotenoids, RRS featured the characteristic bands of beta-carotene at 1008, 1158 and 1523 cm -1 . The correlation between beta-carotene content and relative intensity of Raman scattering was linear with R 2 =0.962 [START_REF] Bicanic | Estimating rapidly and precisely the concentration of beta carotene in mango homogenates by measuring the amplitude of optothermal signals, chromaticity indices and the intensities of Raman peaks[END_REF]. Bhosale P et al. also measured the levels of carotenoids contents in farm products especially fruit juice by RRS and compared to HPLC [START_REF] Bhosale | Resonance Raman quantification of nutritionally important carotenoids in fruits, vegetables, and their juices in comparison to high-pressure liquid chromatography analysis[END_REF]. It shows complementation between the two methods: HPLC had a good performance in evaluating juices, while RRS was suitable for determining content of carotenoids in the same maturity of intact tomatoes. Based on resonance Raman effects, Ouyang S L, et al. obtained the content of lycopene and beta-carotene by measuring the second harmonic of stretching vibration of C=C directly in lycopene and beta-carotene, respectively [START_REF] Ouyang | Lycopene and β-Carotene Content in Tomato Analyzed by the Second Harmonic[END_REF].Yan J, et al. also confirm that 9-cis-beta-carotene exists in cytochrome b6f complex of spinach by HPLC and RRS [START_REF] Yan | The presence of 9-cis-beta-carotene in cytochrome b(6)f complex from spinach [J][END_REF]. Compared to chromatography, the measurement of RRS is quick, usually not exceeding 5 seconds [START_REF] Tschirner | Resonance Raman spectra of beta-carotene in solution and in photosystems revisited: an experimental and theoretical study[END_REF]. However, in practical applications, the measurement may maintain for some time to decrease the influences of fluorescence background. In addition, the high universalizable of RRS methods could be another advantage [START_REF] Efremov | Achievements in resonance Raman spectroscopy: Review of a technique with a distinct analytical chemistry potential[END_REF].

Raman Microspectroscopy

Traditionally Raman microspectroscopy is used to measure Raman spectrum of a micro-scale region on a sample. The main application is to observe the molecular concentration profiles. The system is usually equipped with an optical microscope combined with an excitation laser, a sensitive detector and some other optical devices. This method could focus on the distribution of carotenoids in tiny samples with a high spatial resolution of about 250 nm [START_REF] Thygesen | Vibrational microspectroscopy of food. Raman vs. FT-IR[END_REF]. Since the size of laser beam is only several micrometers in diameter, it could reduce the fluorescence during the experiment. For this reason, Raman microspectroscopy is suitable for determining carotenoids in emulsions or homogenates [START_REF] Dane | Correlation of trans-Lycopene Measurements by the HPLC Method with the Optothermal and Photoacustic Signals and the Color Readings of Fresh Tomato Homogenates[END_REF]. To further increase the optical resolution, Confocal Raman microscopy is designed to investigate carotenoids, the principle of which is to eliminate out-of-focus lights by means of adding a spatial pinhole placed at the confocal plane of the lens. At the expense of long exposures, the optical resolution could confine in hundreds of nanometers [START_REF] Piot | Spatial Distribution of Protein and Phenolic Constituents in Wheat Grain as Probed by Confocal Raman Microspectroscopy[END_REF]. A characteristic block diagram of Raman microspectroscopy experimental setup is shown in Fig 3 . A microscope objective is added to a common Raman spectrum measuring system. In the process of detection, the laser (785 or 514 nm) need to pass through the objective and focus on the sample. The scattered signal caused by laser is also collected by the objective. Therefore, a laser rejection filter need to attenuate the laser in a very low level. After that, the scattered light can be collected into the spectrometer. The spectrum is obtained from a CCD camera, analyzed and displayed on the computer similarly. The advantage of Raman microspectroscopy is the nano-scale resolution and high accuracy. Based on that, Pudney P D A, et al. showed the spatial locations of three main carotenoids within tomato cells, and also demonstrated the follow changes of carotenoids [START_REF] Pudney | Confocal Raman microspectroscopic study of the molecular status of carotenoids in tomato fruits and foods[END_REF]. It is confirmed that the confocal Raman microscope is a powerful tool to measure the green tomatoes. It can offer a full-scale Raman spectrum of the organic composition in unripe tomatoes. This method was also applied to detect carotenoids in ripe tomatoes in which the major carotenoid is lycopene [START_REF] Trebolazabala | Use of portable devices and confocal Raman spectrometers at different wavelength to obtain the spectral information of the main organic components in tomato (Solanum lycopersicum) fruits. [J][END_REF]. In addition, the influence factors of external environment were discussed. Cecilia A. Svelander et al. evaluated the microstructure of carotenes in tomato and carrot emulsions under varying degrees of high pressure homogenization. [START_REF] Svelander | High Pressure Homogenization Increases the In Vitro Bioaccessibility of alpha-and beta-Carotene in Carrot Emulsions But Not of Lycopene in Tomato Emulsions [J][END_REF]. Paolo Camorani et al. proposed confocal micro-Raman spectroscopy as a spatially resolved method to evaluate the changes of carotenoid pattern after thermal treatment of carrots. They have used steaming, boiling and microwaving way to cook carrot. The difference before and after are measured by Raman spectroscopy and HPLC [START_REF] Camorani | Raman spectroscopy application in frozen carrot cooked in different ways and the relationship with carotenoids[END_REF]. Combined with density functional theory, the molecular structure are also analyzed by comparing the calculated and determination results [START_REF] Liu | Density Functional Theoretical Analysis of the Molecular Structural Effects on Raman Spectra of beta-Carotene and Lycopene[J][END_REF].

SORS is an unconventional method specialized in obtaining special information which beneath the surface of objects, such as drugs [START_REF] Olds | Spatially offset Raman spectroscopy (SORS) for the analysis and detection of packaged pharmaceuticals and concealed drugs[END_REF], explosives [START_REF] Loeffen | Chemical and explosives point detection through opaque containers using spatially offset Raman spectroscopy (SORS)[END_REF] and plastic [START_REF] Bloomfield | Detection of concealed substances in sealed opaque plastic and coloured glass containers using SORS[END_REF]. Raman spectroscopies above-mentioned have pimping near-surface detection limits. In contrast, SORS has an effective retrieval of Raman spectra at deep subsurface layers. The basic principle of SORS is to obtain the Raman scattering information using two spatially offset points: one is the location of illumination and the other is the location of determination [START_REF] Matousek | Numerical simulations of subsurface probing in diffusely scattering media using spatially offset Raman spectroscopy[END_REF]. There are several millimeters separation distance between two places. The purpose is to avoid the Raman scattering at domination excitation region [START_REF] Pavel | Noninvasive authentication of pharmaceutical products through packaging using spatially offset Raman spectroscopy[END_REF]. The experimental setup of SORS is unlike conventional Raman instruments. The main distinction is the mode of collecting Raman scattering. Figure 4 shows the distinctions of conventional Raman and SORS approach. As shown in Fig 4, the laser excitation point and the sample collection point are at the same location in conventional Raman. In contrast, Raman spectra in standard SORS approach are collected from the spatial regions away from the place of illumination, and the direction of laser irradiation is opposite to the probe which collects the Raman scattering. When detecting transparent or liquid samples, an adapted SORS approach is applied for analyzing variety of samples [START_REF] Eliasson | Noninvasive detection of concealed liquid explosives using Raman spectroscopy[END_REF]. This method enhances the scattering signal of the liquid or transparent sample directly under the Raman detection area. It realizes detecting bottled liquid directly cause of Raman scattering of liquid could not be easily lost within the noise. As the offset distances increasing, however, the Raman signal would be barely discernible. In this case, this method may require adding multivariate analysis. The advantage of SORS method is non-invasive determination of subsurface. Three Raman spectroscopies above can measure near-surface only with a few hundred micrometers depth. By contrast, the determination depth of SORS could reach several millimeters. Due to the complexity of SORS instrument, the application of SORS for carotenoids detection is not widespread. Qin et al. developed a benchtop point-scan Raman chemical imaging system to show the detail distribution of lycopene during postharvest ripening of tomatoes utilizing SORS technique. The tomato samples were selected and cut open from green stage to red stage. The results showed that the SORS technique was capable of obtain internal Raman chemical information in tomato [START_REF] Qin | Investigation of Raman chemical imaging for detection of lycopene changes in tomatoes during postharvest ripening [J][END_REF]. Afseth N K., et al. explored the possibility of determination carotenoids of salmon through the skin [START_REF] Afseth | A novel approach for subsurface through-skin analysis of salmon using spatially offset Raman spectroscopy (SORS)[END_REF]. At an offsets around 5 mm with an 830 nm laser, the results clearly showed that information regarding carotenoids content could be extracting from both sides of the skin. These applications demonstrated the potential of SORS technology as a new method for subsurface determination of carotenoids.

Conclusion

Raman spectroscopy is a powerful tool to research on carotenoids due to its unique capability of fingerprint for identifying and characterizing the structure of carotenoids molecules. Raman spectroscopy also shows great potential in qualitative and quantitative analysis on carotenoids. In recent years, with advances in optical instruments, several variations of Raman spectroscopies is increasingly widespread such as FT-Raman, RRS, Micro-Raman and SORS. The contrast of these four methods is shown in Table 1. However, new technologies and ideas should be provided to improve insufficient. Many features such as background fluorescence, category distinction of carotenoids, interference and absorbance of reaction mixture still affect the detection results. For different agricultural products, the most crucial is to explore proper detection mode of non-invasive, non-destructive or in vivo. In addition, new perspectives such as femtosecond stimulated Raman spectroscopy and transmission resonance Raman spectroscopy are promising for improvement of the precision and applicability of carotenoids study. The combination of Raman spectroscopy and chromatography will be promising for overcoming many defects. It is expected that Raman spectroscopy will applied in on-line fast determination of carotenoids in the future.

Fig. 1 .

 1 Fig.1. Schematic diagram of FT-Raman spectrometer

Fig. 3 .

 3 Fig.3. Schematic diagram of Raman microspectroscopy experimental setup

Fig. 4 .

 4 Fig.4. Schematic diagrams of conventional Raman and SORS

Table . 1

 . . The contrast of four variations of Raman spectroscopy

	Techniques	Principles	Advantages
	Fourier Transform Raman Spectroscopy	Operation with FTIR spectrometer at 1064 nm	Fluorescence suppression
	Resonance Raman Spectroscopy	Special excitation wavelength at 488 or 514 nm	Raman signal enhancement
	Raman Microspectroscopy	Combination with an optical microscope	Higher spectral resolution
	Spatially Offset Raman	Uses two spatially offset points to obtain	More subsurface
	Spectroscopy	subsurface spectra	information

Acknowledgment

This work is supported in part by Research Foundation for Young Scholar of BAAFS (No. QNJJ201423).


Xin Tang

Fengchen Liang

Lijing Zhao
email: czhaolijing008@163.com

Lili Zhang

Jing Shu

Huamei Zheng

Xu Qin

Changyong Shao
email: eshaochangyong68@163.com

Jinkui Feng

Keshuang Du

STIMULATING EFFECT OF LOW-TEMPERATURE PLASMA (LTP) ON THE GERMINATION RATE AND VIGOR OF ALFALFA SEED (Medicago sativa L.)

Keywords: LTP treatment, stimulating effects, alfalfa seeds, germination rate, germination vigor

Stimulating Effect of Low-Temperature Plasma (LTP)

on the Germination Rate and Vigor of Alfalfa Seed (Medicago Sativa L.)

INTRODUCTION

Low-temperature plasma (LTP) as a physical method has been used experimentally in agriculture since the 1970s [START_REF] Hao | Preliminary Study of low temperature plasma seed treatment[END_REF]. The technique was first applied in seed treatments to improve germination rate and vigor in Russia and the Commonwealth of Independent States (CIS), and has been delved further into seed processing in China, Canada and United States. In recent years, some small scale seed enterprises with specialized LTP systems are starting to take shape as new uses of LTPs are developed [START_REF] Ruifu | Plasma machine seed treatment technology[END_REF][START_REF] Yin | Research of magnetized arc plasma on seeds biological effects[END_REF]. Although observations of the interaction of plasma with seed or living tissue have been primarily empirical, it has been reported that the LTP treatment could not only stimulate seed germination and make crops mature earlier, but also increase flower number and yield (Zhang et al , 2002). Our goal in this study was to evaluate the effect of LTP treatment on alfalfa seed germination rate and vigor by subjecting the seed to a simulated outer-space environment within the evacuated and neon-filled glow discharge chamber of a LTP seed processing machine.

MATERIALS AND METHODS

Equipment and Seed Material. An LTP seed processing machine HL-2N (Fig 1), provided by ChangZhou ZhongKe ChangTai Plasma Technology Co., was used to perform the treatment of alfalfa seed. The primary part of the machine was the glow discharge chamber which consisted of a pair of electrodes with horizontal plates parallel to each other, and a conveyer belt system (Fig1-1). The conveyer belt was positioned between the two planar electrodes and capable of horizontal movement driven by a wheel. The electrodes were connected to a high-tension AC power supply to generate a glow discharge for gas plasma. It was capable of treating a large capacity of seed materials in a continuous or batched process.

The seed material used in this study was a material of purple-flowered alfalfa (Medicago sativa L.) with main-season maturity, which was Zhongmu No. 6, bred by the Institute of Animal Sciences (IAS), Chinese Academy of Agricultural Sciences (CAAS). Only plump and viable seeds with a uniform size (2.3 g./1000) were selected for the test.

LTP Seed Stimulation. After the selected alfalfa seeds were loaded in the receiving hopper Chamber I (Fig. 123456), the machine was evacuated by the vacuum air pumps (I, II, III and IV) and filled with neon (an inert gas) as plasma gas. Neon conducts electricity 75 times more efficiently than air and has stronger penetrating force. To obtain uniform and stable glow discharge between the two electrode plates, an internal 2 ~ 8 mm atmospheric pressure within the machine was maintained. Opening the butterfly valve initiated the glow discharge between the two electrodes, which in turn generated the gas plasma of neon which consisted of many plasma-created species, such as: electrons, ions, photons, and neutrals, with a variety of energies and momenta, chemical activities, and transport characteristics. After the seeds went through the first two receiving hopper chambers and landed on the conveyer belt in the glow discharge chamber, they passed through the plasma glowing zone and were affected/energized by the synergistic effects of the multiple plasmacreated species driven by externally-applied electric fields. Ten electric wattages (20W, 40W, 60W, 80W, 100W, 120W, 140W, 160W, 180W, 200W) as discharge power were used as the stimulating levels of LTP treatment, and an untreated (0W) sample of seeds was kept as the control for later seed germination. The seeds were treated with the LTP in the chamber for 20 seconds before they were moved to the seed-discharging hopper chamber (Fig. 123456789101112).

Germination Tests.After the LTP stimulation, the treated seeds were divided into two parts and arranged in a nested design for the germination tests, in which the first half of the seeds was stored on shelf for four days as the first timenest(T1) and the second half for twenty days as the second time-nest (T2). Each nest consisted of ten LTP-treated seed samples and the control. Before the germination test, the stimulated seeds were pre-chilled to break up their dormancy.

The germination test was conducted at 20 o C in a dark germination chamber. 100 seeds from each of the 10 treated levels and the control were laid in 12-cm Petri dishes lined with filter paper. The Petri dishes were irrigated with 4 ml of distilled water and constantly maintained sufficient moisture for the seeds to germinate. Germinated seeds or seedlings were counted at 24-hour intervals for up to 10 days. Since germination rate and vigor are both important indications of seed quality, viability and the potential of emergence, they were adopted to determine the effects of the LTP treatment on the stimulation of alfalfa seed in this study. Germination rate (GR) is the percentage of seeds that germinate in a specified time Because GR and GV are proportional data which rarely produce normal distributed residuals and stable variances, the two values were transformed by logit transformation during data analysis and mean separation.

RESULTS AND DISCUSSION

Effects of Discharge Power levels and Storage Time of LTP Pre-treated Seeds on Germination Vigor. Two discharge power levels (20W and 100W) had the most significant effect, at P = 0.005, on the germination vigor of alfalfa seed (Table 1). Six discharge power levels (40W, 60W, 80W, 120W, 160W and 200W) resulted in a significantly higher range of germination vigor than the control (0W), but no differences among their respective influence. The effect of 180W was low and the seed stimulation of 140W was not significantly different from the control.

When considering the effect of storage time on LTP pre-treated seeds, it was found that the germination vigor of the LTP pre-treated alfalfa seeds which were stored for 20 days was significantly higher than that of seeds stored for 4 days (Table 1). This suggests that LTP pre-treated seeds should be stored longer in order to improve germination vigor before planting. It can be deduced that the synergistic effects of chemical activities, energy momenta, mass deposition and ion implantation of the plasma-created species (electrons, ions, photons and neutrals) on biomolecules are complex and need a relatively long period of time to be revealed through modifying and energizing a series of biochemical reactions in the seeds. More research is necessary to determine whether there were interactions between discharge power levels and storage time of the LTP pre-treated seed influence seed vigor.

Table 1. Effects of discharge power levels and the storage time of LTP treated seeds on the germination vigor of alfalfa seed.

[Z]

Tested by logit-transformed GV data. Mean separation within columns and rows(a, b, c) by least significant different (P = 0.05). The means bearing the same letters were not significantly different at the 5% level.

[W] Germination rate values for T1 ( LTP treated seeds were stored for 4 days) and T2 (LTP treated seeds were stored for 20 days). were significantly better than the two treatments 60W and 140W, which showed no differences from the control.

Effects of Discharge Power levels and the Storage

As the seed germination rates were observed in the 4-day-storing lot and 20-day-storing lot, the two mean rates were 59.46% and 58.36% (Table 2). The results showed that the effects of all the LTP treatments on the mean rates of the two time lots were nonsignificant. The lack of influence of seed storage time levels after the LTP stimulation indicates that certain germination rates can be eventually achieved no matter how long the LTP-treated seeds are stored, so long as adequate time and appropriate conditions are met for germination. In other words, the effect of LTP seed stimulation on germination rate is not correlated with the storage time of pre-treated seeds, although it could make significant differences in germination vigor.

Table 2. Effects of discharge power levels and the storage time of LTP treated seeds on the germination rate of alfalfa seed.

[Z]

Tested by logit-transformed GR data. Mean separation within columns and rows(a, b) by least significant different (P = 0.05). The means bearing the same letters were not significantly different at the 5% level.

[W] Germination rate values for T1 ( LTP treated seeds were stored for 4 days) and T2 (LTP treated seeds were stored for 20 days). 2 that the LTP treatments resulted in higher (at 20W), intermediate (between 80W and 100W), and lower (between 160W and 200W) increments of germination rates and vigor as the discharge power levels increased. Two obvious peaks (11%, 22%) of the increments of germination rates and vigor both occurred at the 20W and 100W levels.

Germination Rate and

The chart also demonstrated that the LTP treatment had higher effects on the germination vigor than rates in the low range of discharge power, but no obvious differences in the high range.

CONCLUSION

The application of new agricultural technologies has resulted in impressive gains in crop productivity. LTP is a technique that will likely play an important role in future gains in the improvement of crop seed production and quality, especially in stimulating germination and seedling emergence, and possible also in increasing crop earliness, yields and resistance to drought and certain diseases. Unlike non-LTP seed stimulating methods discussed in the introduction, the LTP seed treatment for germination stimulation in this experiment could not only be conducted mechanically, but also very effectively. Our study provides that LTP seed treatment significantly increased the germination rates and vigor of alfalfa seed in comparison to an untreated control. More importantly, this experiment has established a powerful model for seed businesses and farmers to stimulate crop seeds for better germination rates, vigor and higher yields.

(

  10 days after sowing for alfalfa seed according to GB/T 3543.4-1995, Rules for Agricultural Seed Testing-germination Test) and germination vigor (GV) is the percentage of seeds that germinate within a short time (4 days after sowing for alfalfa seed in accordance with GB/T 3543., which are defined as: GR (%) = (Number of seeds germinated in 10 days/total number of seeds) × 100% GV (%) = (Number of seeds germinated in 4 days/total number of seeds ) × 100%.

Figure 2 .

 2 Figure 2. Effect of discharge power (W) on the seed germination rates and vigor of alfalfa, expressed as percent of the control (0W).

References 1 .

 1 Marshal Dhayala, Sook-Young Leea, Sang-Un Parkb. Using low-pressure plasma for Carthamus tinctorium L. seed surface modification [J]. Vacuum, 2006.80(5): 499-506. 2. Irina Filatova,Viktor Azharonok,Mikhail Kadyrov.Rf and Microvawe Plasma Application for Pre-Sowing Caryopsis Treatments.

  ., 2005; Shi et al., 2010). Based on current studies from around the world, LTP stimulates crop seeds by ionizing radiation, mass deposition and charge exchange of ion implantation to biomolecules during the glow discharge process (Dhayal, et. al., 2006; Volin et al., 2000; Grzegorzewski, 2011; Grzegorzewski et al., 2009; RDEOFESWLTP, 2008 ). As a result of the LTP treatment,

seed coat is softened, and organic compounds and bio-reactions in embryos are activated, hastening germination and seedling emergence, and increasing crop earliness, yield and resistance to drought and certain diseases (Xu et al., 2011; Liu et al., 2007; Shao et al., 2012; Hu et al., 2007; Yin et al., 2005; Filatova et al., 2010). Various techniques of seed stimulation such as microwave heat radiation, salt stress, water stress, electric-field induction, lanthanum ion radiation, ultraviolet ray irradiation and drought shocks are currently used to treat crop seed, but there has been relatively little research on the application of LTP machines for seed processing, especially alfalfa seed treatment (Fu et al., 2006;; Luo et al., 2012; Li et al., 2006; Huo et al., 2011; Qin, 2004; Zheng et al.

  Time of LTP Pre-treated Seeds on Germination Rate. Of the ten treatments, the 20W, 80W, 180W and 200W had the highest stimulating effect (P=0.05) on the germination rate of alfalfa seed in comparison to the others (Table2). However, none of these four treatments resulted in a significantly different rate from one another. The levels of 40W, 100W, 120W and 160W brought about an intermediate influence on the rate, but

	Power	GV Values [W]
	Levels	T1	T2	Mean [Z]
	0W	43	51	47.00 c
	20W	57	58	57.50 a
	40W	53	54	53.50 abc
	60W	47	57	52.00 abc
	80W	50	56	53.00 abc
	100W	54	57	55.50 ab
	120W	52	54	53.00 abc
	140W	45	50	47.50 c
	160W	50	53	51.50 abc
	180W	47	52	49.50 bc
	200W	50	53	51.50 abc
	Mean	49.82 b	54.09 a

  Vigor Relative to the Control. In comparing the effects of all ten levels (20W, 40W, 60W, 80W,

	displayed in Figure
	Power	GR Values [W]
	Levels	T1	T2	Mean [Z]
	0W	55	56	55.5 b
	20W	63	60	61.50 a
	40W	61	57	59.00 ab
	60W	55	56	55.50 b
	80W	59	62	60.50 a
	100W	62	56	59.00 ab
	120W	61	58	59.50 ab
	140W	57	55	56.00 b
	160W	59	60	59.50 ab
	180W	61	61	61.00 a
	200W	61	61	61.00 a
	Mean	59.46 a	58.36 a
	100W, 120W, 140W, 160W, 18W and 200W) of discharge power to the control (0W), a trend of fluctuating effects is


Shouli Xuan

Chunlin Shi

Yang Liu

Yanhua Zhao

Wenyu Zhang

Hongxin Cao
email: fcaohongxin2003@yahoo.com.cn

Changying Xue

Prediction of the Natural Environmental High Temperature Influences on Mid-Season Rice Seed Setting Rate in the Middle-Lower Yangtze River Valley

Keywords: Middle-Lower Yangtze River Valley, mid-season rice, high temperature, seed setting rate, statistical forecast model

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Global average temperature has increased by 0.74℃ in 1906-2005 and is projected to continue to increase [START_REF] Xue | Climate Change 2007: Impacts, adaptation and vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change[END_REF]. The projected climate change is recognized to increase extremely high temperatures events [START_REF] Wheeler | Temperature variability and the yield of annual crops[END_REF][START_REF] Fulu | Global warming, rice production, and water use in China: developing a probabilistic assessment[END_REF][START_REF] Peiling | Effects of climatic variation and warming on rice development across South China[END_REF][START_REF] Tianyi | Responses of rice yields to recent climate change in China: An empirical assessment based on long-term observations at different spatial scales[END_REF]. China is one of the world's most productive rice regions, contributing ∼ 27.5% of the world rice production [START_REF]Food and Agriculture Organization of the United Nations[END_REF]. The Middle-Lower Yangtze River Valley (MLYRV) is an important rice producing areas in China. Under the background of climate warming, high temperature stress imposes an increasing risk to rice production and becomes one of the major constrains in increasing productivity of rice in the MLYRV [START_REF] Jianchu | Regularity of high temperature and its effects on pollen vigor and seed setting rate of rice in Nanjing and Anqing[J[END_REF][START_REF] Xiaoqun | Impacts of maximum or minimum temperature on yield and yield components of single-season Indica rice in Yangtze-Huaihe area[END_REF][START_REF] Xiaohai | Research on heat stress of rice in China: progress and prospect[END_REF][START_REF] Jiang Min | Occurrence patterns of high temperature at booting and flowering stages of rice in the middle and lower reaches of Yangtze River and their impacts on rice yield[J][END_REF][START_REF] Zhiqing | Impact of high-temperature on single cropping rice over Yangtze-Huaihe river valley and response measures[J][END_REF]. High temperature influence on rice has been studied in many researches [START_REF] Satake | High temperature-induced sterility in Indica Rices at flowering[J][END_REF][START_REF] Matsui | High temperature-induced spikelet sterility of japonica rice at flowering in relation to air temperature, humidity and wind velocity conditions[END_REF][START_REF] Matsui | The difference in sterility due to high temperatures during the flowering period among Janonica-rice varieties[J][END_REF][START_REF] Kim | Relationship between grain filling duration and leaf senescence of temperate rice under high temperature[END_REF]. It has been pointed out that the heat stress damage mainly occurs in the reproductive period of rice. In previous studies, high temperature effect on rice yields were mainly investigated by using model simulations or control experiments [START_REF] Erda | Climate change impacts on crop yield and quality with CO 2 fertilization in China[J[END_REF][START_REF] Krishnan | Impact of elevated CO 2 and temperature on rice yield and methods of adaptation as evaluated by crop simulation studies[END_REF][START_REF] Weichang | Assessment method study on high temperature damage to rice[END_REF][START_REF] Zhang Qian | Study on the impact of high temperature damage to rice in the lower and middle reaches of the Yangtze river[J][END_REF][START_REF] Jagadish | High temperature stress and spikelet fertility in rice[J][END_REF][START_REF] Shi Chunlin | A model to simulate high temperatureinduced sterility of rice[J][END_REF]. Several studies have paid attention to effects of observed climate on rice [START_REF] Longxing | Effects of high temperature stress on super hybrid rice Guodao 6 during flowering and filling phases[END_REF][START_REF] Zhiting | Temperature-driven climatic factors and their impact on the fertility of hybrid rice at anthesis[J][END_REF][START_REF] Peng | Rice yields decline with higher night temperature from global warming[END_REF]. For example, Zhao et al. analyzed the effects of observed mean maximum temperature at the stages of flowering and pre-milk on rice components in 1981-2003 [START_REF] Zhao Haiyan | Correlation analysis of rice seed setting rate and weight of 1000-Grain and agro-meteorology over the middle and lower reaches of the Yangtze river[J][END_REF]. These studies mainly concentrated on the growing-season mean or development stage mean (mostly flowering stage and filling stage) influence of high temperature on rice. Actually, the impact of high temperature largely differed with the local climate, land suitability, cultivated rice variety and time of sowing and harvesting et al. However, the critical period of rice damage caused by natural environmental high temperature in actual rice production is still not clear. In this study, we tried to identify the most sensitive stage of mid-season RSSR to high temperature in the MLYRV in field conditions. Based on the extracted key sensitive period, the influence of high temperature on rice in the MLYRV was predicted by using the statistical forecast models and the high temperature-induced rice sterility simulation model.

2

Data and Methods

Data

This study was based on the rice data (including growth period and seed setting rate) at 6 stations (Yichang, Yueyang, Hefei, Nanchang, Hangzhou, Yangzhou) in the MLYRV from 2004 to 2011, which were obtained from the national regional yield trial of southern China (totally using 615 samples and 69 mid-season late-maturing indica rice varieties). The daily maximum temperature data and the monthly 74 circulation indices in 1984-2013 from the Chinese Meteorological Administration were also used.

Methods

Definition of the heat stress index

In previous studies, the high temperature effects on rice were often investigated by using meteorological indices which were usually defined as the days of daily mean temperature > 30℃ or days of daily maximum temperature > 35℃ [START_REF] Fengmei | Change of relative extreme high temperature events and climate risk in rice growing period in China from 1981 to 2000[J][END_REF][START_REF] Yifang | High temperature damage to paddy rice in Jiangsu Province and its cause analysis[J][END_REF][START_REF] Xie Xiaojin | Spatial and temporal distribution of high temperature and strategies to rice florescence harm in the lower-middle reaches of Yangtze river[J][END_REF]. In the present study, the heat stress index (I hs ) calculated for the high temperature influence assessment was based on the work of Teixeira et al. [START_REF] Teixeira | Global hot-spots of heat stress on agricultural crops due to climate change[END_REF]. It was assumed that (1) rice crop are only sensitive to high temperature during thermal sensitive period (TSP, days); (2) heat damage occurs when maximum temperature (T max ) exceeds the critical temperature threshold (T crit ); and maximum effect occurs when T max exceeds the limit temperature threshold (T lim ). To calculate I hs , "daily" high temperature stress intensity (I hsd ) is firstly estimated as a function of T max (Eq.( 1)) .

                  (1)
The value of T crit and T lim used in this study is 35℃ and 45℃ respectively. The daily values of I hsd are then accumulated and averaged throughout the TSP (Eq.( 2)).

1

TSP hsd i hs I I TSP    (2)
The period from July 11 to August 12 is chosen as the thermal-sensitive period in this paper.

Validation of the forecast model

The normalized root mean square error (NRMSE) was used to evaluate the predictive ability of the statistical forecast models.

2 1 ( ) / 100% n ii i Y X n NRMSE X     (3)
where Y i is the forecasted value, X i the observed value, n the number of observations, and X is the mean of all observed values.

Results

Daily Variation Characteristics of the Maximum Temperature

In order to investigate the daily variation characteristic of the maximum temperature in significant growth stages of rice, averaged daily maximum temperatures (mean of 615 samples) were calculated near the full heading phase. For example, for the full heading day, the averaged maximum temperature was calculated as the mean of maximum temperatures in all the full heading days in 615 samples. As shown in Fig. 1, for the mid-season rice variety in MLYRV, persistent high temperature mainly occurred in the month before full heading (according to the standard of 33℃). After the full heading stage, there was a consistent decrease in the averaged maximum temperature. Thus, the persistent high temperature occurred concomitantly with the booting and heading stages, resulting in serious loss and harm to rice production in this region.

High Temperature Effects on RSSR

To indentify the sensitive stage of RSSR to high temperature in the MLYRV, daily relationships between maximum temperature (5-day running means) and RSSR before and after full heading day were calculated. As shown in Fig. 2, the RSSR is sensitive to T max in the period of 4-36 days before full heading, corresponding to the averaged date from July 11 to August 12. Obviously, the high temperature at about 14 days before full heading had the most significant impact on RSSR. However, the RSSR seemed to be not closely related to the high temperature at the full heading day. A possible reason might be due to the lower maximum temperature at the full heading day (32.2℃,see Fig. 1). After the full heading day, the maximum temperature did not have any influence on RSSR. Therefore, for the mid-season rice variety in MLYRV, more attention should be paid to the heat damage around 2 weeks before full heading. The relationship between RSSR and averaged maximum temperature in 4-36 days before full heading in 2004-2011 was further investigated in Fig. 3. The results indicated that RSSR was decreased significantly with increasing maximum temperature in the sensitive period (4-36 days before full heading). The correlation coefficient between regional averaged RSSR and averaged maximum temperature in 4-36 days before full heading during 2004-2011 was -0.9 (significance above 99.9% confidence level). It was found that all the years with the averaged T max larger (less) 33℃ exhibited below (above) 80% RSSR in the MLYRV. Especially for the year 2004, the MLYRV suffered from a severe heat wave in summer. In this year, the averaged maximum temperature in 4-36 days before full heading day reached above 34℃ with the RSSR of 76%.

Statistical Forecast Models of I hs

As discussed above, the prediction of high temperature influence on rice with several months lead time is valuable to the rice production in the MLYRV, where the higher than normal summertime temperature will directly cause decreases in rice yield. To further predict the impacts of high temperature on mid-season rice in MLYRV, a rice heat stress index (I hs ) was calculated by using TSP as the extracted sensitive period (from July 11 to August 12) and statistical models were presented for predicting the high temperature stress on rice in this section. Presented in Fig. 4 was the inter-annual variation of the I hs in the extracted sensitive period of the mid-season rice in the MLYRV (solid line). It can be seen that the I hs showed a significant ability to describe the rice heat-stress events occurred in the MLYRV (such as the year 2003 and 2013). As is known, local climate variability mainly depends on the large scale atmospheric circulations. Therefore, the correlation coefficients between the rice heat stress index in the MLYRV and 74 circulation indices in preceding 12 months (January to June in current year and July to December in last year) was examined to identify key preceding predictors of the I hs . Statistical prediction models for the I hs in the MLYRV were then constructed by using a multilinear stepwise regression method based on the extracted predictors. We applied yearby-year validation to further verify the prediction models. The year-by-year validation was applied by establishing prediction models using circulation indices in preceding 20 years to forecast the I hs in the following year (for example, using circulation indices in 1984-2003 to establish the forecast model and predict the I hs in 2004; using circulation indices in 1985-2004 to establish the forecast model and predict the I hs in 2005; • • • • • • ; using circulation indices in 1993-2012 to establish the forecast model and predict the I hs in 2013). The year-by-year validation method was used because it was closer to the approach in actual forecasting. Results of the year-by-year validation test during 2004-2013 (Fig. 4) demonstrated that the constructed prediction models shown good predictive abilities for forecasting the I hs in the MLYRV with the correlation between the predicted and observed I hs of 0.95 (significance above 99.9% confidence level) and the NRMSE of 28.4%.

Quantitative Forecast Model of High Temperature Effects on Rice

The heat stress in the MLYRV could be well forecasted with several months lead time by using the presented statistical models. However, the application of large area averaged prediction information was quite limited in production decisions. Therefore, a simulation model of high temperature induced rice sterility at a meiosis phase was used to predict the relative RSSR in MLYRV at the site scale.

According to the research of Shi et al. [START_REF] Shi Chunlin | Quantitative analysis on the effects of high temperature at meiosis stage on seed-setting rate of rice florets[J][END_REF], the daily RSSR could be expressed in terms of temperature in meiosis phase with a quadratic equation:

1 ( ) i i C Y c T T     (4)
where Y i is the daily relative RSSR,T i is the daily T max ,c and T C are variety parameters. The relative RSSR decreases when the maximum temperature in the meiosis stage is higher than T C . Because of the varied daily maximum temperature in actual production, the total influence of high temperature on relative RSSR is expressed in the product of daily relative RSSR:

   1 0 t t i i Y Y (5)
where Y is the total influence of high temperature during meiosis phase on relative RSSR, t0 and t1 is the starting and ending time of the meiosis phase, respectively. In the present study, the year 2009 without heat stress was set as the CK year. The observed rice relative RSSR was defined as rRSSR(i)/rRSSR(2009) ×100%, where rRSSR(i) and rRSSR( 2009) is the relative RSSR in forecast year and in 2009, respectively. As shown in Fig. 3, the MLYRV experienced significantly high temperature stress in the year 2004 and 2007. Therefore, the rice data in 2004 and 2007 was used to calibrate and validate the model, respectively. The daily T max in crucial high temperature sensitive stage (7-19 days before full heading, see Fig. 2) were used to run the high temperature-induced rice sterility simulation model and trail-and-error method was adopted to calibrate the parameters. The result of calibration showed that for the mid-season rice in the MLYRV, the value of parameter c and T C is 0.000246 and 30.6℃, respectively. Fig. 5 presented the simulation and prediction results of the high temperature-induced rice sterility simulation model (observed RSSR is the average of 69 rice varieties). Because of the missing data in Nanchang (2004 and 2009) and Yangzhou (2007), we actually used 5 samples in 2004 and 4 samples in 2007. The results shown that the high temperatureinduced rice sterility simulation model accurately simulated the relative RSSR in 2004 with a NRMSE of 4.74% and forecasted the relative RSSR in 2007 with a NRMSE of 2.84% (the correlation coefficient between the predicted and observed relative RSSR in 2007 reached 0.96 with the significance above 95% confidence level), indicating the good predictive skill of the high temperature-induced rice sterility simulation model in the MLYRV.

Conclusions

Previous studies have found that rice plant was the most sensitive to high temperature at heading time. In this study, daily relationship between RSSR and temperature was investigated by using observed daily maximum temperature data at 6 representative stations in the MLYRV. Our results indicated that for the mid-season rice in the MLYRV, years with the maximum temperature in meiosis stage of rice above than normal may experience significant yield losses in the MLYRV. The results in the present study reinforced the need to plan adaptive strategies for rice production with regard to heat stress in the meiosis phase of mid-season rice in the MLYRV. Based on the revealed high temperature sensitive period, high temperature-induced rice sterility simulation model and statistical models were established to forecast rice heat stress in MLYRV. The results of validation tests further illustrated the good prediction ability of the constructed forecast models.

Fig. 1 .

 1 Fig.1. Daily variation of the T max

Fig. 2 .

 2 Fig.2. Daily correlation between RSSR and T max (5-day running means)(dashed indicate significance at 99% confidence level)

Fig. 3 .

 3 Fig.3. The relationship between RSSR and averaged T max in 4-36 days before full heading

Fig. 4 .

 4 Fig.4. The observed (solid) and forecasted (dashed) I hs values in the MLYRV

Fig. 5 .

 5 Fig.5. The simulated (2004, triangle) and forecasted (2007, circle) results of the relative RSSR

Acknowledgment

Funds for this study was provided by the National Natural Science Fund (31401279), the CMA/Henan Key Laboratory of Agro-meteorological Support and Applied Technique (AMF201201), the Jiangsu Agriculture Science and Technology Innovation Fund (CX(12)5059), the China Meteorological Administration Special Public Welfare Research Fund (GYHY201306035), the China Agricultural Administration Special Public Welfare Research Fund (201203032), and the Jiangsu Province Science and Technology Support Program (BE2012391).


Yu'e Song

Chengguo Wang

Ling Zhu

Xiaofeng Chen

Qiyu Zhang

The Design and Implementation of online Identification of Captcha Based on the Knowledge Base

Keywords: CAPTCHA, online identification, knowledge base, animal

The Completely Automated Public Turing Test to Tell Computers and Humans Apart (CAPTCHA) identification is designed to distinguish between computers and humans and it prevents the web application programs from malicious attacks, so it has been applied widely. However, great challenges must be faced with the development of CAPTCHA identification. In order to improve the safety of the professional system, the CAPTCHA online identification based on the knowledge base, which has high security and bases on semantic questions and the professionalization of professional system, is put forward combining with the recessive CAPTCHA. The specific implementation course of the new online identification method is worked out according to the example of animal identification. The application of the verification code is suitable for people who have the corresponding professional knowledge. Because the computer has great difficulty to answer semantic information questions, which are also professional issues, so the new online identification method based on the verification of knowledge has very high security.

Introduction

With the rapid development of internetwork, security problem of the web application becomes an extremely important issue for us. The HTTP attack based on the form automatically submission is a common way of network attack. According to the HTTP protocol, the attacker can write program to simulate the method of form submission, and submit the abnormal data to site service automatically and rapidly. This constitutes the basic HTTP attacks. An attacker can repeat logging to break a user's password and this will lead to a leakage of users' privacy information. In order to prevent the attacker using program automatic login, Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) technology has been widely used [START_REF] Zhi-Gang | Principles and Prevention of HTTP Attacks Based on Identifying Code Recognization[END_REF] .

The CAPTCHA is a kind of program algorithm to distinguish between computers and humans, so the procedure must be able to generate and evaluate computer test which human can easily pass but not *Corresponding author: Qiyu Zhang,Yantai Academy,China Agricultural University,No.2006,Coastal middle Road,Gaoxin District,Yantai City,Shandong Province,China.E-mail:rcraingo@163.com for computers [START_REF] Xiao-Min | The Research on User Modelling for Internet Personalized Services[END_REF][START_REF] Luis Von Ahn | Telling Humans and Computers Apart Automatically[END_REF] . Because the computer cannot solve CAPTCHA question, the user who answer the question can be considered human [START_REF] Tao | Ambiguity function based on the linear canonical transform[END_REF] .

In order to protect the network, CAPTCHA has been applied widely, such as preventing spam ads in the blog post, protecting website registration and the E-mail address, online polls, preventing dictionary attacks, the search engine robots, worms and spam, etc.

Since CAPTCHA has been proposed, different research institutions and scholars have developed a variety of CAPTCHA. CAPTCHA has different ways of classification [START_REF] Wang Bin-Jun | Research on attach and strategy of CAPTCHA technology[END_REF] . According to the type of information, CAPTCHA can be divided into text CAPTCHA, image CAPTCHA, graphics CAPTCHA, audio CAPTCHA and video CAPTCHA. According to the way of recognition, CAPTCHA can be divided into dominant CAPTCHA and implicit CAPTCHA. According to the interaction, CAPTCHA can be divided into static CAPTCHA and dynamic CAPTCHA. Along with the development of the CAPTCHA, CAPTCHA recognition technology is also developing and some methods have been put forward, such as the matching shape context [START_REF] Mori | Recognizing objects in adversarial clutter: breaking a visual CAPTCHA[END_REF] , template matching [START_REF] Sai-Ping | Recognition and Improvement of Identifying Code[END_REF] and neural network identification methods [START_REF] Zuo Bao-He | A Neural Network Based Approach to Recognizing the Verification Code[END_REF] . This makes the security of the CAPTCHA has a huge challenge. Dynamic CAPTCHA and recessive CAPTCHA have a good security and is the research direction in the future.

The hidden CAPTCHA [START_REF] Wang Bin-Jun | Research on attach and strategy of CAPTCHA technology[END_REF] refers to answering the question of the CAPTCHA expressing according to the semantic of CAPTCHA, for example, CAPTCHA system first randomly generates an expression (5+3)*9/4 and requires the user to answer the expression values; CAPTCHA system picks up a few images from the graphics library and users need to rotate the graphics to the right direction. Though artificial intelligence has a rapid development, the computer has much difficulty to answer semantic information questions, so the hidden CAPTCHA is safe.

In this paper, the CAPTCHA technology is studied deeply. Based on the implicit CAPTCHA and combining with the characteristics of professional system, a new kind of CAPTCHA is proposed based on the knowledge base and the security of the system can be effectively improved using the new kind of CAPTCHA.

Knowledge representation

In the knowledge base, knowledge representation methods are logical notation, production representation, frame representation and object-oriented representation, semantic representation and the XML representation and representation of ontology [START_REF] Liu Jian-Wei | Comparative Study of Knowledge Representation[END_REF] , etc. According to the characteristics of the CAPTCHA, we choose production knowledge representation description. former part of the rule and is any legal logical expressions. It is the prerequisite for reasoning by using the rule. A is called later part of the rule and is the result of reasoning using the rule. [START_REF] Zhang Xuan-Ping | The Rule Representation for Knowledge in Database Style[END_REF] . The rule knowledge representation has many advantages, such as simple and clear reasoning, the reasoning machine design and implementation is simple and has a good characteristics in some specific application environment, etc. IF the animal is mammals and has claw THEN the animal is a hoof animal.

The design of the database and table for knowledge base

According to the rules of reasoning above, we designed the rules table, inferences table and synonym table. Rules table save the atomic conditions of precondition, which are the minimum condition of premise condition. The above animal identification rules are in the rules table as shown in Table 1.

Table 1. Animal identification rules

Serial number Rules

Have hoof

The result of reasoning is text messages. There are different representations for the same text messages and the computer can't recognize it very well, therefore automatic word segmentation can be used for the results and CAPTCHA. In this process, the word which not be used can be removed and the keywords will be extracted, then we can match the keyword. For Chinese word segmentation, IK Analyzer 2012 can be used. The IK Analyzer is an open source lightweight Chinese word segmentation toolkit based on Java language. In the 2012 version, we support configuring IKAnalyzer. CFG.XML file to expand proprietary dictionary and stop using dictionary and dictionary format is utf-8 without BOM in Chinese text files [START_REF] Qi-Yu | Research and Design of Spam Email Filter System based on algorithm spam[END_REF] . Stop using words are not really meaning of function words in both English and Chinese [START_REF] Zhang Xuan-Ping | The Rule Representation for Knowledge in Database Style[END_REF] and can be ignored because they does not affect the understanding of sentence meaning. The stop using dictionaries are built on the basis of the literature [10] and [11]. In order to assist CAPTCHA judgment, two options are increased which must be contained keywords and must not contained keywords. Meanwhile, in order to reduce the complexity of the system reasoning, the result is made as easy as possible. Inferences table is shown in Table 2.

Hoofed animals

In the MySQL database we design different table structures, which are shown in Table 3, Table 4 and Table 5.

CAPTCHA generation algorithm

(1) Reason the total number of records in inferences table and remember to n;

(2) Randomly select the integer between 1 ~ n, remember to k;

(3) Take the kth records in the inferences table and access the premise condition, the result, whether participles, the keywords which must be contained and which must not be contained;

(4) Decompose precondition to obtain the corresponding rule number;

(5) Take corresponding rules in the rules table rules numbering rules;

(6) Generate a CAPTCHA image for each rule.

The algorithm flow chart is shown in Figure 1.

CAPTCHA implementation

The realization of the CAPTCHA is shown in Figure 3.

Conclusion

The CAPTCHA has a variety of forms, but the development of CAPTCHA recognition technology causes a hidden danger for the security of the CAPTCHA. In order to improve the security of the CAPTCHA, a new kind of CAPTCHA based on knowledge base is put forward combining the implicit CAPTCHA, which is based on semantic information question and answer and the professional system.

This new CAPTCHA can significantly improve the security of the professional system. The CAPTCHA designed in this paper is suitable for professional system but not for general system, such as E-mail.

  Shortliffe firstly introduced the concept of production in the famous expert system MYCIN. The structure IF (E1 & E2 &... & En) THEN A is called the rule. It means that if the logical expression of E1 & E2 &... & En established, the conclusion A is right. The expression E1 & E2 &... & En is called

For

  some professional systems, CAPTCHA can be structured based on knowledge base. Because users have the corresponding knowledge and can reason the related results according to the precondition. Let us use a simple animal identification as an example to illustrate how to construct CAPTCHA. We give the following rules about animal identification: IF the animal has hair THEN the animals are mammals IF the animal has milk THEN the animals are mammals IF the animal has feathers THEN the animal is a bird IF the animal can fly AND lay eggs THEN the animal is a bird IF the animal eats meat THEN the animal is a carnivorous animal IF the animal has a canine tooth AND claw AND eyes staring at front THEN the animal is a carnivorous animal.

Figure 2 .

 2 Figure 2. CAPTCHA validation algorithm

Figure 3 .

 3 Figure 3. Authentication code implementation

Table 2 .

 2 Inference table data

	Premise condition	Results	Whether the word is segmented	Must contained keywords	Must not contained keywords
	`1	Mammals
	2	Mammals
	3	Birds
	4,5	Birds
	6	Predators
	7,8,9	Predators
	1,10	Hoofed animals
	2,10

Table 3 .

 3 Rule table

	Field	Data type	Dote
	Id	int	Automatic numbering,
			primary key
	Rule	varchar(100)

Table 4 .

 4 Inferences table

	Field	Data type	Note
	Id	int
	Condition Result Segmentation Key	varchar(100) varchar(100) char(1) varchar(200)	Automatic numbering, primary key
	Antonym	varchar(200)

Table 5 .

 5 Synonym table

	Field	Data type	Note
	Id Key Synonym	int varchar(100) varchar(100)	Automatic numbering, primary key

  Word segmentation? If no, compare the CAPTCHA entered by the user and the results and judge whether they are consistent. If consistent, agree on. If inconsistent, judge whether there is a synonym and whether consistent after replacement. If unanimity, agree on. If inconsistent, not through;(3) If the words need segmentation, do words segmentation to the CAPTCHA entered by the user and results and match the keyword. If they are consistent, agree on. If inconsistent, judge whether there is a synonym and whether consistent after replacement. If unanimity, agree on. If inconsistent, not through. The algorithm flow chart is shown in Figure2.

	start end reasoning, the total number of records in table inferences, remember to n; a random integer between 1 ~ n, remember to k; take the first k in inferences reasoning table records, access to the premise condition, the result, whether participles, must contain the information such as keywords and must not contain the keywords; decomposition precondition to obtain the corresponding rule number; take corresponding rules in the rules table rules numbering rules; for each rule to generate a CAPTCHA image. Figure 1. CAPTCHA generation algorithm 3.3 CAPTCHA validation algorithm (1) The user reasons according to generated CAPTCHA images and enter the CAPTCHA; end the user according to generate several CAPTCHA images reasoning and enter the verification code; Word segmentation? OK Synonym? NO A synonym to replace consistent? consistent? OK NO Word processing keyword matching OK Synonym? NO A synonym to replace consistent? consistent? (2) start OK NO

Acknowledgments

This work was supported by the scientific research fund project of China Agricultural University Yantai academy (YT201311, 201201Ja), the science and technology plan project of Beijing education committee (Grant no. KM201510853006)and key scientific research project of Beijing Polytechnic College (Grant no.bgzykyz201502, bgzykyz201503).


Tian Shen

, Dean

Weikuan Jia

Yu Chen
email: dchenyu@ujs.edu.cn

Recognition and Localization Method of Overlapping Apples for Apple Harvesting Robot

Keywords: overlapping apples, fast dynamic identification, curve fitting. image matching

In order to meet the speed requirements of harvesting robot, a method of tracking and recognition of overlapping apples is proposed in this paper. First of all, the first image should be segmented and denoised, the center and the radius is determined, the template which is used for matching is extracted according to the center and radius. Then, determine the center of ten images which are taken continuously, fit motion path of the robot according to the center of each image and predict subsequent motion path. The next processing area is determined according to the radius and predicted path. Finally, overlapping apples are identified by fast normalized cross correlation match method. Experiments prove that the new method can locate the center and radius of overlapping apples correctly. Besides, matching time is reduced by 48.1% compared with the original one.

Introduction

It is well known that China is a big agricultural country with a long history and fruit and vegetable production plays an important part. While in fruit and vegetable production, harvesting accounts for about 40% of the whole work, which means using robots to harvest fruit and vegetable automatically can not only reduce labor cost and damage rate of fruit picking but also improve harvesting efficiency. American scholars Schertz and Brown proposed to have robots harvest fruit and vegetable in 1960s, since then, a variety of fruit and vegetable picking robot have been studied widely. However, harvesting robots still can not be put into practice because of low recognition and picking rate and recognition problems like occlusion and overlapping fruits [START_REF] Wei | Automatic method of fruit object extraction under complex agricultural background for vision system of fruit picking robot[END_REF][START_REF] Ji | Automatic recognition vision system guided for apple harvesting robot[END_REF][START_REF] Zhang | Overview of research on agricultural robot in China[J[END_REF][START_REF] Chen | A practical solution for ripe tomato recognition and localisation[END_REF][START_REF] Ogawa | Spraying robot for grape production[END_REF][START_REF] Van Henten | An autonomous robot for harvesting cucumbers in greenhouses[END_REF].

As apple growing attitude is varied, the images captured by apple harvesting robot are also various, such as single apple, overlapping apples, foliage occlusion and so on.

Research is relatively mature on the identification and location of single fruit [START_REF] Ji | Automatic recognition vision system guided for apple harvesting robot[END_REF][START_REF] Hayashi | Evaluation of a strawberry-harvesting robot in a field test[END_REF][START_REF] Rajendra | Machine vision algorithm for robots to harvest strawberries in tabletop culture greenhouses[END_REF].

However, the identification and location of overlapping apples is still a problem which restricts the development of harvesting robot. To solve this problem, numerous scholars have carried out a lot of researches on the identification and location of overlapping images and some progress has been made [START_REF] Karvelis | Identifying touching and overlapping chromosomes using the watershed transform and gradient paths[J][END_REF][START_REF] Pastrana | Novel image processing approach for solving the overlapping problem in agriculture[END_REF][START_REF] Xu | Two-stage approach for detecting slightly overlapping strawberries using HOG descriptor[END_REF][START_REF] Qi | Robust segmentation of overlapping cells in histopathology specimens using parallel seed detection and repulsive level set[END_REF][START_REF] Cloppet | Segmentation of overlapping/aggregating nuclei cells in biological images[END_REF][START_REF] Priya | Separation of overlapping bacilli in microscopic digital TB images[END_REF][START_REF] Wang | Recognition and localization of occluded apples using K-means clustering algorithm and convex hull theory: a comparison[END_REF][START_REF] Lü | Fast positioning method of apple harvesting robot for oscillating fruit[END_REF]. Among them, Petros [START_REF] Karvelis | Identifying touching and overlapping chromosomes using the watershed transform and gradient paths[J][END_REF] used watershed transform and gradient paths to segment touching and overlapping chromosomes. After testing on 183 Multiplex Fluorescence In Situ Hybridization images, the success rate for touching chromosomes is 90.6% and for overlapping chromosomes is 80.4%. Julio [START_REF] Pastrana | Novel image processing approach for solving the overlapping problem in agriculture[END_REF] proposed to use ellipse to approximate the leaf shape so that the complexity of it is simplified. After that, active shape models were used to recognize the clustering of the leaves. Shape model of experimental plants with 2, 3 and 4 leaves were tested and the results indicated that this method was able to identify overlapping leaves if less than 32% of the area is overlapped. Xu [START_REF] Xu | Two-stage approach for detecting slightly overlapping strawberries using HOG descriptor[END_REF] used histogram of oriented gradients (HOG) descriptor associated with a support vector machine (SVM) classifier to detect slightly overlapping strawberries. However, these studies are mostly under static condition and use single frame processing method, the process of harvesting robot is dynamic, so these studies can not be fully applicable to the dynamic picking of robot during moving. For dynamic recognition, many scholars begin to pay attention and gratifying results have been achieved [START_REF] Chtcheglova | Nano-scale dynamic recognition imaging on vascular endothelial cells[END_REF][START_REF] Gonzalez | Digital image processing using MATLAB[END_REF]. For example, Lv [START_REF] Chtcheglova | Nano-scale dynamic recognition imaging on vascular endothelial cells[END_REF] made a preliminary research on dynamic recognition of fruits. The results indicated that the correlation of images could effectively reduce processing time. But overall, it seems that there is still much room for the improvement of dynamic recognition of overlapping fruit.

This study mainly focuses on dynamic identification and location of overlapping apples. Firstly, improved Otsu threshold algorithm is used to segment the image, after that, the image is processed by morphology so that relevant features can be extracted.

Then, the center and radius of overlapping fruit are determined by local maxima method and the template can be extracted according to them. Finally, robot motion path is predicted by combining software and hardware. NCC method is used to make sure that tracking target is the same one and to accurately locate the target fruit so that the overlapping fruit can be tracked dynamically. A total of 11 images which are continuously taken by harvesting robot at a constant speed in natural scene are tested and the results show that the new method can locate the center and radius of overlapping apples correctly. Additionally, processing time of the improved method is reduced by 48.1% compared with the original one, which means real time of harvesting robot is improved and it can better meet practical requirements.

Thought of Fast tracking and recognition

Fruit tracking recognition is based on a series of image sequences. Dynamic characteristics of fruits are obtained by searching for the correlation and difference of a range of image sequences, so that the subsequent movement can be predicted and the next range of image processing can be estimated, which reduces the time of robot recognition and improves the picking efficiency. The difficulty of tracking and recognizing overlapping fruits is to determine the center and radius of each fruit. In this paper, a method based on maxima is used to overcome this problem.

The process is shown as follows:

Step 1 Collect eleven overlapping apple images continuously;

Step 2 Segment the images by improved Otsu color difference segmentation algorithm;

Step 3 Perfect the image by mathematical morphology operation;

Step 4 Determine the centers by local maximum method;

Step 5 Determine the radii by minimun distance from the center to the edge of contour;

Step 6 Extract the template according to the centers and radii;

Step 7 Predict the motion path by least square method;

Step 8 Extract subsequent processing region according to the centers and radii;

Step 9 Conduct normalized cross correlation (NCC) fast matching algorithm for image matching.

Image segmentation

Otsu segmentation algorithm

Overlapping fruit segmentation is a major difficulty of tracking and recognizing because segmentation result has a critical influence on subsequent steps. Although fixed threshold segmentation has the advantage of real-time. Considering that the color of fruits and background varies a lot, Otsu method based on the color characteristics is used here.

Otsu method [START_REF] Serra | Mathematical morphology and its applications to image processing[END_REF] was proposed by Japanese scholar Otsu, the basic idea is dividing the image into two categories by a threshold, the optimal threshold is determined according to the variance between class of these two categories and the optimal threshold is maximal variance between class. For a specific image, set the gray-scale range of the image f(x,y) to be [0, L-1], the probability of each gray-scale is Pi. The image is divided into C0 and C1 (namely background part and foreground part). After setting a threshold t, the probability of the foreground is   , the average gray value is 0  and 1  respectively, while total average gray value is

0 0 1 1       , the variance between class of these two parts is 2 2 2 2 0 0 1 1 0 1 0 1 (t) ( ) ( ) ( )                  (1)
The t which makes Although Otsu method can separate the most of the target from the background, it will easily cause over-segmentation phenomenon.

Improved Otsu segmentation algorithm

Although Otsu color difference segmentation algorithm can segment the apples from background to a certain extent, but it will easily cause over-segmentation or under-segmentation phenomenon. For example, Fig. 1 (b) appears over-segmentation phenomenon. To overcome this problem, improved Otsu color difference segmentation algorithm is used in this paper. The main content of this algorithm is to stretch or shrink R component in the image by applying gamma conversion to it, so that the difference between R and G component in the image can be increased.

Gamma conversion is a nonlinear gray-scale transformation, its formula is shown as follows:

() esp y x K   (2) Where [0,1] x  ; [0,1] y  ; esp K  compensation coefficient;   gamma coefficient. When 1
  , the contrast in high gray-scale region is enhanced and the contrast in low gray-scale region is reduced; When 1   , the image is the same; When 1   , the contrast in low gray-scale region is enhanced and the contrast in high gray-scale region is reduced; Fig. 2 shows the results after Otsu color difference segmentation. In Fig. 2 (a),  =0.5; in Fig. 2 (b),  =1; in Fig. 2 (c),  =1.5. After several experiments, segmentation works best when  =0.68.

Image improvement

There may be some holes in the calyx of apple after segmentation because the color of calyx and body of apple varies greatly. Additionally, there may also be some other holes, burrs, noise, etc. Therefore, mathematical morphology operation [START_REF] Kaur | Mathematical morphological edge detection for remote sensing images[END_REF][START_REF] Heo | Robust stereo matching using adaptive normalized cross-correlation[END_REF] should be conducted after segmentation to perfect the image and de-noising.

The basic idea of mathematical morphology is using structural elements with a certain form to measure and extract the corresponding shape in the image, only image features which are similar to the structural elements are kept. Specific steps are:

Step 1 Firstly, the image should be dilated with a 1 radius disk shaped structural elements, so boundary points are expanded and some holes are filled;

Step 2 Then floodfill operation is used to fill the holes remained;

Step 3 After that, the maximum connected region should be obtained so that isolated burrs are removed;

Step 4 Finally, image erosion is operated to eliminate the noise around the boundary.

The results of perfection are shown in Fig. 3.

Template extraction

Apples are almost separated from the background after automatic threshold segmentation, each center and radius of overlapping apples should be obtained after that so that matching template of apples can be extracted from the image.

Determine the center

The center is determined according to the maximum of minimum distance between the point within a circle and the edge of contour. However, calculating all the points within the circle will certainly take up a lot of memory which causes poor real-time performance, so an improved method is used to scan the points within the circle. In Fig. 4, the two maxima of the minimum distance are marked with red circles, which are where the two centers of the overlapping apples are.

Determine the radius

The radius can be determined by the center of circle, however, it can not be determined only according to the maximum distance from the center to the edge of the outline Firstly, work out the distance from the center to the edge of the contour;

then the minimum distance is used as the radius. The steps involved are shown in Fig. 5.

Start

Vector number = 1,take unit vector in any direction at the center Vector reaches the edge? Vector length+1

Vector number is 6? It can be seen from Fig. 6 (b) that the centers and radii can be found accurately by above method. After that, the templates which are used in the subsequent experiments are extracted according to the center, radius and a certain reserve value.

Matching recognition

Robot motion path anticipation

In order to reduce the time of image processing and accelerate the speed of robot picking, motion path is predicted according to the center of apples in collected images and the location of centers in a series of images is used to narrow the processing scope of subsequent image.

Due to the complication of overlapping fruits, the case of two overlapping apples is studied in this paper. The processing steps of two overlapping apples are as follows:

Step1 Determine the centers of two overlapping fruits in the images respectively by the method mentioned in section 4.1. Then robot motion path is fitted by polynomial fitting based on the the two centers respectively. The two centers in the next frame are estimated after prediction which combines robot speed and sampling time.

Step2 The radius is determined by the method mentioned in section 4.2. Find the maximum radius of the two fruits and name it rmax. A(ax, ay), B(bx, by) are the two estimated centers of the next frame. The subsequent processing area is intercepted with starting point C(cx,cy). The size of it is a square with 4*rmax side length. Fig. 7 shows the schematic diagram of cutting the subsequent processing area. Where, cx=min{ax,bx}-rmax-m, cy= max{ay,by}+rmax+m, m is a certain reserve value.

Fig.7 Subsequent processing region extraction

Least square method is used in Step 1 to fit the motion path of the robot. Specific processing steps are: set the square sum of error of the fitting curve is less than 5, then find out the best order of the fitting curve. After that, motion curve is fitted by least square method according to the best order and list the polynomial of the curve. Finally, the coordinates of the centers in the next frame are determined by combing sampling interval of robots.

Normalized cross correlation matching

Subsequent processing region of the image has been extracted after the above steps.

The position of apples can be located after the images conduct normalized cross correlation matching with the templates.

Normalized cross correlation (NCC) matching [START_REF] Saravanan | Algorithm for face matching using normalized cross-correlation[END_REF][24] is the most classical algorithm among matching methods based on gray feature. Its basic principle is comparing the gray matrix of template with image to be searched to get the location of the most relevant match. The algorithm is simple and it eliminates the light sensitive issues which means it has good applicability for apple images under different light intensities. Besides, NCC match applies to the situation when the image is slightly displaced and rotated compared with the original one, which means it is suitable for dynamic image tracking and matching.

I is used to represent the image to be matched whose pixels are M×N and T is used to represent the template whose pixels are m×n. Normalized correlation coefficient is defined as follows: ) , ( y x R is between (0,1), the greater the coefficient is, the higher similarity between two matching templates will be.

                        1 0 1 0 1 0 1 0 2 2 , 1 0 1 0 , ] ) , ( [ ] ) , ( [ ] ) , ( [ ] ) , ( [ ) , ( m n m n y x m n y x T u T I y u x I T u T I y u x I y x R           (3)
However, NCC fast matching algorithm is used in this paper because NCC algorithm needs too much calculation and bad real time. Specific steps are as follows:

Step 1 Set

T u T u T   ) , ( ) , ( '  
, then the numerator of formula (2) can be simplified as

             1 0 1 0 ' 1 0 1 0 , ' ) , ( ) , ( ) , ( m
n m n y x u T I u T y u x I        (6) Where,      1 0 1 0 ' ) , ( m n u T    = 0 ] ) , ( [ 1 0 1 0        T u T m n   
, so the numerator can be simplified as

        1 0 1 0 ' ) , ( ) , ( ) , ( m n numerator u T y u x I y x R     (7)
According to the Fourier transform, the numerator can be rewritten as

  } { * } { ) , ( ' 1 T F I F F y x R numerator    (8)
Step 2 For the denominator, since the template is known, ] ) , ( [

1 0 1 0 T u T m n           is a
given fixed value, which means it does not affect the normalized match to find the optimal solution.

Therefore it does not have to be calculated, denominator of formula (2) can be simplified as

         1 0 1 0 2 , min ] ) , ( [ ) , ( m n y x ator deno I y u x I y x R    (9)
In summary, normalized correlation coefficient can be simplified as

                 1 0 1 0 2 , ' 1 1 ] ) , ( [ * ) , ( m n y x I y u x I T F I F F y x R    (10)
The amount of calculation of NCC algorithm is reduced significantly after simplification and real-time has been improved effectively.

6 Results and analysis

Robot motion path prediction

The operating environment of the experiment is as follows, hardware environment: Take the left apple for an example, set the square sum of error of the fitting curve is less than 5, it turns out that three order curve can fit the robot trajectory, so, it is used in this paper to fit the curve. Find the polynomial of the curve and the coordinates of the two centers of overlapping fruits in the eleventh image can be predicted according to former ten pictures combined with sampling time of robot. In this experiment, the coordinates of the predicted centers are (214,60.6) and (304,56.3) respectively.

Image matching

According to the position of the centers predicted, a square with side length 4*r max is intercepted as the area to be processed. Comparison experiments show that average matching time of two apples is 0.181s before pre-judgment, while after pre-judgment, average matching time is 0.094s, which means the speed of identification is accelerated by 48.1%. These results indicate that it significantly accelerates image matching after pre-judgment and matching accuracy is high.

Different proportion of apple area discussion

In this paper, tracking and recognition problem caused by mutual occlusion of fruits is studied. It is a simple approach, but it is effective. The results indicate that this approach can successfully recognize the overlapping apples and track them in dynamic condition. Also it has good real-time. However, the proportion of apple area has a big influence on the matching time.

Applying this method to Fig. 10 It can be seen from Table 1 that the smaller proportion of apple fruit is, the better accelerating optimization will be.

Different proportion of apple overlapping area discussion

Besides the proportion of apple area, the proportion of apple overlapping area also has great impact on the matching time. Table 2 shows that the bigger proportion of overlapping area is, the less matching time will be needed.

Conclusion

Tracking and recognition of overlapping fruits is studied in this paper. The process of dynamic tracking and recognition is introduced, firstly, improved Otsu method based on color difference is used to segment the image, then morphology method is processed on segmented image. Secondly, the centers of overlapping fruits are determined by local maxima distance, the radii are determined by the centers and the templates are extracted according to the centers and radii. Finally, rapid normalization match is conducted to track and recognize overlapping fruits. It is proved by several tests that matching time is reduced significantly after prediction and real-time has been improved effectively. Additional hardware devices are not required in this method, lower cost is needed and it has generality for spherical fruits and vegetables picking robot. Further study is required for the following problems.

Recognize overlapping fruits in relatively more occlusion situation. The method which is based on local maxima of distance only applies to the situation when the profile of apples is a relatively complete circle. The maxima method can not be used if the profile of apple is not complete.

Improve real-time performance of template matching performance. NCC algorithm has good effect on different light and slight rotation and translation, but it needs much calculation. Although image processing time is reduced by pre-judgment, the computation is still heavy. Researches on real-time improvement can be studied in subsequent studies.

Fig. 1

 1 Fig.1 Otsu segmentation

  a.  =0.5 b.  =1 c.  =1.5 d.  =0.68.

Fig. 2

 2 Fig.2 Improved Otsu segmentation

Fig. 3

 3 Fig.3 Apple image perfectionFig.3shows that apples are almost split from the background after dilation-hole filling-largest connection region getting-erosion and the result is satisfying.

  Define four scanning direction : A(x+,y+), B(x-,y+), C(x-,y-), D(x+,y-): For point E(m,n) which is inside the contour of a image, comparing the distance of its left (m-1, n) and lower (m, n-1) points in direction A; comparing the distance of its right (m+1, n) and lower (m, n-1) points in direction B; comparing the distance of its right (m+1, n) and upper (m, n+1) points in direction C; comparing the distance of its left (m-1, n) and upper (m, n+1) points in direction D. Minimum distances are obtained by the examination of the points within the outline in comparison to its four-neighborhood and the minimum distance function is composed by these distances. The three-dimensional surface chart is shown as Fig.4.

Fig. 4

 4 Fig.4 Three-dimensional map of minimum distance function

Fig. 5 Flow chart of radius determination 4 . 3 Template 2 Fig. 6

 54326 Fig.5 Flow chart of radius determination

  of the top left corner of the sub-graph in the image; value of pixel of template T.

windows 7 FigFig. 8

 78 Fig.8.

Fig. 9

 9 Fig.9 Match identification process Fig.9 (a) is a 320×2 40 pixel image, apples account for approximately 18.5% of the whole image. The results show that the new method can find the apples in the eleventh image accurately.

3 Fig. 10

 310 Fig.10 Images of experiments on influence of proportion of apple area

4 Fig. 11

 411 Fig.11 Images of experiments on influence of proportion of apple overlapping area

Table 1 .

 1 Relationship between the proportion of apple area and matching time

	Image serial number	1	2	3
	fruit area/image area	18.5	55	66
	×100%
	Matching time	0.181	0.185	0.188
	without pre-judgment/S
	Matching time	0.094	0.133	0.148
	after pre-judgment/S
	Matching time acceleration	48.1	28.1	21.3
	×100%

Table 2 .

 2 Relationship between the proportion of apple overlapping area and matching time

	Image serial number	1	2	3	4
	Overlapping area/fruit area	4.75	5.03	7.47	7.69
	×100%
	Matching time	0.105	0.095	0.078	0.071
	after pre-judgment/S

Acknowledgments

Funds for this research was provided by the National Nature Science Foundation of China (No. 31571571), Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), The Specialized Research Fund for the Doctoral Program of Higher Education of China (No. 20133227110024).