Xuehua Zhao

Xueyan Liu

Daoliang Li

Huiling Chen
email: chenhuiling.jlu@gmail.com

Shuangyin Liu

Xinbin Yang

Shaobin Zhan

Wenyong Zhao

Comparative Study on Metaheuristic-Based Feature Selection for Cotton Foreign Fibers Recognition

Keywords: Metaheuristic, Feature selection, Foreign fibers, Recognition system

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

Introduction

The cotton foreign fibers, such as ropes, wrappers, plastic films and so on, are closely related to the quality of the final cotton textile products [START_REF] Yang | A New Approach for Image Processing in Foreign Fiber Detection[END_REF]. In the recent years, the machine-vision-based recognition systems have been widely used to assess the quality of cottons [START_REF] Li | Classification of Foreign Fibers in Cotton Lint Using Machine Vision and Multi-Class Support Vector Machine[END_REF][START_REF] Yang | Fast Recognition of Foreign Fibers in Cotton Lint Using Machine Vision[END_REF], in which classification accuracy is an key measure to validate the performance of recognition systems. To improve the classification accuracy, finding the optimal feature sets with high accuracy is an efficient way due to because it can improve the accuracy and speed of reconition systems. Feature selection (FS) is a main approach to find the optimal feature sets by reduce the irrelevant or redundant features. Curently, FS has been used to the area of machine learning and data mining [START_REF] Lin | Classifier Design with Feature Selection and Feature Extraction Using Layered Genetic Programming[END_REF]. Since to find the optimum feature sets is a NP problem, the researchers begin to turn to find the near optimal feature set and have proposed many algorithms [START_REF] Bolón-Canedo | A Review Of Feature Selection Methods on Synthetic Data[END_REF][START_REF] Sun | Object Detection Using Feature Subset Selection[END_REF].

Currently, metaheuristic algorithms have attracted so much attention, the representive algorithms are particle swarm optimization (PSO for short), ant colony optimization (ACO for short) and genetic algorithm (GA for short) [START_REF] Bolón-Canedo | A Review Of Feature Selection Methods on Synthetic Data[END_REF][START_REF] Pedergnana | A Novel Technique for Optimal Feature Selection in Attribute Profiles Based on Genetic Algorithms[END_REF][START_REF] Chen | A Novel Bankruptcy Prediction Model based on an Adaptive Fuzzy K-Nearest Neighbor Method[END_REF]. For metaheuristic algorithms, they are firstly given an evaluating measure of the quality of feature sets, and iteratively improve a specific candidate set. Finally, the excellent feature sets are obtained. The metaheuristic algorithms makes few assumptions on the optimal feature sets and find the optimal feature sets in very large search spaces. This is very suitable for the FS problem.

In this paper, three metaheuristic algorithms for FS are presented to find the optimal feature combination of cotton foreign fibers, which are GA for FS (GAFS for short), ACO for FS (ACOFS for short) and PSO for FS (PSOFS for short). Two classifiers, which are the k-nearest neighbor (KNN for short) [START_REF] Cover | Nearest Neighbor Pattern Classification[END_REF] and support vector machine (SVM for short) [START_REF] Xuegong | Introduction to Statistical Learning Theory and Support Vector Machines[END_REF], are used to evaluate the quality of subsets and to identify cotton foreign fibers. The aim of our works is applying these algorithms to the data sets of cotton foreign fibers to discover the new and challenging results. The comparison analysises of these algorithms illustrate the excellent search ability of the proposed metaheuristic algorithms and the feature sets obtained by them can efficiently improve the performance of recognition systems of cotton foreign fibers.

The remainder is organized as follows. The applications context and the proposed FS methods are presented in Section 2. The results and discussion are describled in Section 3. Section 4 describles the conclusion.

2

Materials and methods

Application Background

The cotton foreign fibers usually fall into six groups, that are feather, hemp rope, plastic film, cloth, hair, polypropylene and, respectively. The cotton foreign fibers induce the quality of the cotton textile products [START_REF] Yang | A New Approach for Image Processing in Foreign Fiber Detection[END_REF]. The grade evaluation using machine-vision-based recognition systems is mainly an approach to solve this problem [START_REF] Li | Classification of Foreign Fibers in Cotton Lint Using Machine Vision and Multi-Class Support Vector Machine[END_REF]. These systems in general have three key steps, which are image segmentation, feature extraction and classification of foreign fiber, respectively. Considering real-time problem, the feature set, which includes a few number of features and has high accuracy, is important due to reduction of the detection time and improvement of classification accuracy. As a result, FS is important to the online recognition systems of cotton foreign fibers. In our work, we have applied three metaheuristic algorithms to this area for obtaining the optimal feature sets of cotton foreign fibers.

Data Preparation

Firstly, we obtain the foreign fiber images by our test platform, and 1200 representative images including foreign fibers are selected to extract the dataset. The width of the obtained images is 4000 pixels and their height is 500 pixels. Several examples are shown in Fig. 1. These images are divided into six groups in terms of categories of foreign fibers, and every group contains 200 images. Then, we segment these images into small foreign fiber objects only including cotton foreign fiber. Finally, the 2808 objects are obtained and the number of hair, black plastic film, cloth, rope, polypropylene twines and feather objects is 204, 408, 432, 492, 528 and 744, respectively. The following step is extracting features from these objects.

The color, shape, and texture features can be extracted in cotton foreign fibers. Since accurate classification is difficult in only using one or two features [START_REF] Li | Classification of Foreign Fibers in Cotton Lint Using Machine Vision and Multi-Class Support Vector Machine[END_REF]. Therefore we need extraction of all kinds of features including color, shape and texture features, and find the excellent feature combination by FS approaches.

In our experiment, a total of 80 features are extracted from foreign fiber objects, and the number of color, texture and shape features is 28, 42 and 10, respectively. These extracted features are used to build the 80-dimensional feature vector.

After the data is generated, normalization is implemented to reduce the impact of different dimensions.

2.3

Metaheuristic Algorithms for Feature Selection Fitness Function. Fitness function is important for metaheuristic algorithms, it is used evaluate the quality of each subset. Considering the online classification problem, we expect that the found feature set should has small size and high accuracy. Therefore, fitness evaluation is designed to combine the accuracy of classifier with the length of feature subset. The specific fitness function in this paper is the following Eq. ( 1):

( ) ( ) | | S X J X X   (1)
where X denotes the subset, J(X) is the classification accuracy of subset X, |X| denotes the feature number of the subset X, υ and ψ are used to adjust the relative importance of accuracy and size. In this study, two classifiers, KNN and SVM are adopted to evaluate the quality of subset.

Particle Swarm Optimization for Feature Selection. PSO belongs to populationbased metaheuristics and is proposed by Kennedy and Eberhart [START_REF] Eberhart | A New Optimizer Using Particle Swarm Theory[END_REF]. PSO explores the search space by movements of particles with a velocity and each particle is updated based on its past best position and the current best particle. PSO can efficiently balance the exploration and exploitation and is an efficient optimization algorithm [START_REF] Chen | Support Vector Machine Based Diagnostic System for Breast Cancer Using Swarm Intelligence[END_REF][START_REF] Kennedy | A Discrete Binary Version of the Particle Swarm Algorithm[END_REF]. Supposing the particle i is denoted as ,

i i i d X x x x  r L 1 ,2 , ( , , , ) i
, which has the velocity ,1 ,2 , ( , , , )

i i i i d V v v v  r L . ,1 ,2 ,
( , , , )

i i i i d P p p p  r L
denotes the past best position of the particle i.

, Each bit of the particle only lies in one of two states, i.e. zero or one, which will be changed according to probabilities. To change the velocity from continuous space to probability space, the following sigmoid function is using in algorithm:

, , 1 ( ) , 1, 2, , 1 exp(
)

ij ij sig v j d v   L ( 2
)
The velocity is recalculated in terms of Eq. ( 3):

1 , , 1 1 , , 2 2 , , ( ) ( )
t t t t t t i j i j i j i j g j i j v w v c r p x c r p x          (3)
where w denotes inertia weight and is updated at the iteration t according to Eq. ( 4):

max min max min max () () t tt w w w w t     (4)
where w max denotes respectively the maximal value of the inertia weight, w min denotes the minimum of the inertia weight. The t max is the maximal times of iterations. The parameters c 1 and c 2 denote the acceleration coefficients. The parameters r 1 and r 2 are random numbers varying from 0 to 1. x i , j , p i , j and p g , j belong to zero or one. v max is the maximum velocity.

The new particle position is updated by Eq. ( 5):

, 1 , , 1, ( ) , 1, 2, , 0, ( )
ij t ij ij if rnd sig v x j d if rnd sig v          L ( 5
)
where and rnd denotes the random number in [0, 1] from uniform distribution.

Ant Colony Optimization for Feature Selection. ACO is proposed based on the idea of ants finding food by the shortest path between food source and nest [START_REF] Dorigo | Ant System: Optimization by a Colony of Cooperating Agents[END_REF]. In ACO, the addressed problem is modelled as a graph, in which the ants search a minimum cost path. The good paths mean the emergent result of the global cooperation among ants. In each iteration finding the optimal solutions, many ants construct their solutions by heuristic information and trail pheromone [START_REF] Zhao | Feature Selection Based on Improved Ant Colony Optimization for Online Detection of Foreign Fiber in Cotton[END_REF]. In the ACOFS, every candidate solution is mapped into an ant represented by a binary vector where the bit one or zero respectively means that the corresponding feature is selected or not.

Heuristic information: Heuristic information generally represents the attractiveness of each feature. If heuristic information is not used, the algorithm would be greedy, and the better solution is not found [START_REF] Bolón-Canedo | A Review Of Feature Selection Methods on Synthetic Data[END_REF]. To evaluate the heuristic information [START_REF] Forsati | Enriched Ant Colony Optimization and its Application in Feature Selection[END_REF], the information gain is calculated in this study.

Feature selection: In each iteration, the ant k determines whether the feature i is selected or not according to transition probability p i . The transition probability p i is given as follows:

[ ( )] [ ( )] if [ ] [ ] () 0 otherwise k k ii k uu i uJ tt iJ pt                (6)
where k J represents the feasible feature set, i  is the heuristic desirability of the feature i, i  is the pheromone value of the feature i. α and β is used to adjust the relative importance of the heuristic information and pheromone.

Pheromone update: After all ants have constructed their feature sets, the algorithm trigger the pheromone evaporation, and according to Eq. ( 7) each ant k deposits a quantity of pheromone ()

k i t  
, which is calculated according the following equation:

      () 0 i f otherwise kk k i s t i s t t          (7)
where () k st denotes the found feature set by ant k at iteration t. () x  denotes fitness function.

The pheromone can be updated according to the following rule:

          1 11 m kg i i i i k t t t t               (8)
where (0,1)   denote the pheromone decay coefficient which can avoid stagnation.

m denotes the number of ants and g is the best ant. For all ants, the pheromone is updated according to Eq. ( 8) Genetic Optimization for Feature Selection. GA is proposed by Holland, which is a metaheuristic based on the idea of genetic and natural selection. GA has been used to solve the FS tasks [START_REF] Pedergnana | A Novel Technique for Optimal Feature Selection in Attribute Profiles Based on Genetic Algorithms[END_REF]. In evolution, each species has to change its chromosome combination to adapt to the complicated and changing environment for surviving in the world. In GA, a chromosome denotes a potential solution of problem, which is evaluated by fitness function. In GA, a new generation with better survival abilities is generated by crossover and mutation. GA usually includes coding, selection, crossover, mutation.

Encoding: In the GAFS, a chromosome represents a candidate feature set which consist of genes, and a gene is a feature which are encoded in the form of binary strings. If the gene is coded into '1', the corresponding feature is selected, otherwise, the gene is coded into '0'. The bit i in the chromosome denotes the feature i. Each chromosome is initialized randomly.

Selection: The selection is to select the chromosomes into the new generation among the current population. A certain number of chromosomes are probabilistically selected into the next generation, where the probability of selecting chromosomes i is Eq. ( 9)

1 () Pr( ) () m j Si i Sj    (9)
where S(i) denotes the fitness value of the chromosome i, m is the number of the chromosomes.

Crossover:

The crossover operator is used to exchange genes between two chromosomes. The representative crossover operators are multi-point crossover, doublepoint crossover and single-point crossover [START_REF] Pedergnana | A Novel Technique for Optimal Feature Selection in Attribute Profiles Based on Genetic Algorithms[END_REF]. In this study, double-point crossover is performed. After some chromosomes have been selected into the next generation's population, additional chromosomes are generated by using a crossover operation. Crossover takes two parent individuals, which are chosen from the current generation by using the probability function given by Eq. ( 9), and creates two offspring by recombining portions of both parents.

Mutation: mutation operator is used to determine the variety of the chromosomes, which let local variations into the chromosomes and keeps the diversity of the popula-tion, This can help to find the good solution in search space. Here, the number of chromosomes depends on the mutation rate. Then a gene of the mutating chromosome is selected at random and its value is changed from '1' to '0' or '0' to '1', respectively.

Results and Discussion

In our experiments, the configuration of computer is as follows: CPU 2.66GHz, main memory 4.0GB and Windows 7 system. All the algorithms are coded and run in the Matlab development environment. Two different classifiers, KNN and SVM, are taken for evaluating the quality of solution. To efficiently evaluate the methods, the 10fold cross validation is used in our experiments. The parameters of PSOFS, ACOFS and GAFS is set according to Table 1. These parameters are selected in terms of experiences. Table 2 shows the results of performance comparisons of three algorithms. As we can see, for KNN classifier, ACOFS has the best result among these methods, the selected subset has the smallest size and highest classification accuracy. For SVM, PSOFS can obtain the subset with highest accuracy, but the subset obtained by ACOFS includes the least features.

Fig. 2 intuitively shows the accuracy of the different subsets obtained by three algorithms and the original set with KNN and SVM, respectively. As shown in Fig. 2, for KNN and SVM, all the optimal subsets obtained by three metaheuristic-based algorithms achieve much higher accuracy than the original set without feature selection.

Fig. 2. Results of classification accuracy with KNN and SVM

Conclusions

A key issue in machine-vision-based recognition system of cotton foreign fibers is to find the optimal feature set. In this study, FS based on metaheuristic optimization, namely ACOFS, PSOFS and GAFS, have been proposed to address this FS problem. Two different classifiers, KNN and SVM are taken for evaluating the quality of solution and classification. The experimental results show the presented methods have the excellent ability of finding a reduced set of features with high accuracy in the dataset of cotton foreign fiber. The selected feature set is great significant for machine-visionbased recognition system for cotton foreign fibers. In our future work, we will focus on improving the performance of classifiers used in the recognition rate of recognition systems of cotton foreign fibers.

Fig. 1 .

 1 Fig. 1. Images of cotton foreign fibers: (a) hair image, (b) plastic film image, (c) cloth image, (d) hemp rope image, (e) polypropylene image, (f) feather image.

Table 1 .

 1 Parameter settings for three FS algorithms

	Parameters	PSOFS	ACOFS	GAFS
	υ	0.9	0.9	0.9
	ψ	0.1	0.1	0.1
	m	50	50	50
	Itermax	100	50	100
	c 1	2
	c2	1.5
	w max	0.95
	w min	0.4
	v max	3
	α		1
	β		1.5
	ρ		0.3
	pr			0.9
	pm			0.05

Table 2 .

 2 Comparison of performance of the five algorithms

	Methods	Classifiers	Average accuracy (%)	Average number of feature
	ACOFS		92.7	20
	PSOFS	KNN	92.3	26
	GAFS		91.8	27
	ACOFS		91.5	25
	PSOFS	SVM	91.9	31
	GAFS		91.4	35

Table 3 .

 3 Classification results of the optimal set obtained by ACOFS

	Classes	(1)	(2)	(3)	Results (4)	(5)	(6)	Sample number	Accuracy (%)
	(1) plastic film	396	0	0	0	4	8	408	97.06
	(2) cloth	0	408	0	0	0	24	432	94.44
	(3) hemp rope	0	8	416	0	28	40	492	84..55
	(4) hair	0	0	0	196	0	8	204	96.08
	(5) Polypropylene	0	0	4	20	504	0	528	95.45
	(6) feather	8	48	16	0	12	660	744	88.71
	Average								92.72

Table 4 .

 4 Classification results of the optimal set obtained by PSOFS

	Classes	(1)	(2)	(3)	Results (4)	(5)	(6)	Sample number	Accuracy (%)
	(1) plastic film	396	0	0	0	0	12	408	97.06
	(2) cloth	0	408	0	0	0	24	432	94.44
	(3) hemp rope	0	16	412	0	28	36	492	83.74
	(4) hair	0	0	0	192	4	8	204	94.12
	(5)Polypropylene	0	0	16	8	504	0	528	95.45
	(6) feather	12	44	0	24	0	664	744	89.25

Acknowledgments: This study is funded by the National Natural Science Foundation of China (61402195, 61471133 and 61571444), Guangdong Natural Science Foundation (2016A030310072), Guangdong Science and Technology Plan Project (2015A070709015 and 2015A020209171), the Science and Technology Plan Project of Wenzhou, China (G20140048), Shenzhen strategic emerging industry development funds (JCYJ20140418100633634.

Average

92.34 Tables 3456show the detailed classification results of the subset obtained by three meta-heuristic algorithms and the original set using KNN classifier. As shown in Tables 3-6, the classification accuracy of the cloth and hair is efficiently improved by using the subset selected by three meta-heuristic algorithms, at least increased by 8% and 15%, respectively. The curves of fitness of the three metaheuristic-based algorithms with KNN, SVM in a certain run are shown in Fig. 3, respectively. The curves shown in Fig. 3 are representative according to our preliminary experiments. As we can see in Fig. 3, for KNN and SVM, ACOFS is more efficient and faster than PSOFS and GAFS. On average, PSOFS and GACOFS need about 70 iterations to find the optimum subset, while ACOFS only needs less than 40 iterations.


Yan Xu

Yuepeng Wang

Xing Liu

Dan Luo

Hongman Liu

China Topsoil Stripping Suitability Evaluation Based on Soil Properties

Keywords: Soil type, Topsoil Stripping, Suitability Assessment, Land Evaluation

The purpose of topsoil stripping reuse is to provide a good cultivation platform for agricultural site conditions, namely increasing soil thickness and improving soil fertility levels. Based on soil type, using the thickness of the soil and organic matter content as criteria, the natural suitability of stripped topsoil from different regions were evaluated. The evaluation results were divided into five levels: Most suitable, suitable, moderately suitable, less suitable, and unsuitable. The results show that the highest degree of topsoil stripping natural suitability is mainly distributed in the Northeast region, along the middle and lower Yangtze River, and the region south of the Yangtze River. The most unsuitable regions for topsoil stripping natural suitability are mainly located in the hilly regions of the Sichuan Basin and the eastern part of the Northwest. The topsoil stripping process must be based on the soil properties of both the original site and the transplantation location.

Introduction

With socioeconomic development, resource scarcity issues become increasingly prominent, especially the relative shortage of arable land resources, which has created a bottleneck constraint on China's future social and economic development. There is a shortage of arable land resources in China: per capita arable land area is less than 0.094hm 2 , which is less than 40% of the world average, and on 60% to 70% of the arable land exists erosion, arid and barren conditions, waterlogging, salinization, compaction, gravel, mortar layers, soil gleyization and other factors [START_REF] Li | China Land Resources[END_REF]. Unreasonable fertilization, excessive use of pesticides, industrial waste, and acid rain has significantly worsened soil pollution. Especially in developed areas, around cities and surrounding areas, and along main roads, heavy metals and organic pollutants in soil matter have drastically exceeded standards [START_REF] Zhao | Cultivated land resources and strategies for its sustainable utilization and protection in China [J][END_REF]. Meanwhile, China's arable land reserve resources are scarce; the ecological conditions of the reserve resource regions are fragile and can easily lead to the development of ecological problems. Due to the various needs of construction land occupation, ecological restoration, and restructuring of agriculture, the arable land shortage will continue to be the most critical long-term issue. Therefore, effectively raising the quality of agricultural land and focusing on improving and protecting the ecology and environment has become the key to solving the problem of land use.

Topsoil stripping has become an important way to enhance the quality of agricultural land and improve low-yielding fields. Topsoil stripping refers to removing topsoil, including arable land, humus layers, gardens, woodlands, and grasslands, from construction sites and open-pit mining sites for land restoration, soil improvement, and land reclamation by stripping, storage, transportation, and plough layer construction testing of the topsoil structure and a series of related technologies. Topsoil stripping has the following benefits, such as protecting the surface soil resources, and rapid increase in soil fertility, etc. [START_REF] Yan | Highway borrow pits topsoil stripping engineering technical points[END_REF][START_REF] Xu | A preliminary study on topsoil stripping technology[END_REF].

Currently, topsoil stripping is common in mining site land reclamation, highway construction site reclamation, soil pollution control, major construction projects and other activities. Topsoil stripping research focuses on exploring the role of stripped topsoil in improving soil [START_REF] Patzelt | Restoration of wet fen meadows by topsoil removal: vegetation development and germination biology of fen species[END_REF][START_REF] Tan | Patterns of topsoil stripping for planting use in foreign countries and its enlightenment for China[END_REF][START_REF] Tallowin | Restoration of a Cirsio-Molinietum Fen Meadow on an Agriculturally Improved Pasture[END_REF][START_REF] Van Diggelen | Top soil removal: new hope for threatened plant species [J]. Species dispersal and land use processes[END_REF][START_REF] Verhagen | Restoration success of low-production plant communities on former agricultural soils after top-soil removal[END_REF][START_REF] Hölzel | Restoration of a species-rich flood meadow by topsoil removal and diaspore transfer with plant material[END_REF][START_REF] Klimkowska | Wet meadow restoration in Western Europe: a quantitative assessment of the effectiveness of several techniques[END_REF][START_REF] Hu | Principle and method of soil profile reconstruction for coal mine land reclamation[END_REF][START_REF] Hu | Concept of and methods for soil reconstruction in mined land reclamation[END_REF][START_REF] Fu | Surface soil stripping and its technology in ecological reclamation in mine area[END_REF][START_REF] Jiao | Effects of two reclamation methodologies of coal mining subsidence on soil physical and chemical properties [J][END_REF], the technology and process used in topsoil stripping [START_REF] Dou | Technical System of Topsoil Stripping of Songliao Plain[END_REF][START_REF] Sun | Several measures of soil utilization of highway construction[END_REF][START_REF] Yang | Research on the reclamation technology for the land temporarily used for railway construction[END_REF][START_REF] Wang | Implementation conditions of topsoil stripping of farmland used for construction--Taking shifting soil and fertility betterment of Three Reservoir Area as example[END_REF][START_REF] Wei | The cultivated land fertility of land leveling process to keep the problem[END_REF], and topsoil stripping benefit analysis [START_REF] Chen | Evaluation of topsoil stripping and reuse project performance--Taking removed soil fertilization project in Three Gorges Reseervoir Area as example [J][END_REF] and so on. Research on the suitability of topsoil stripping is limited, only a few scholars have explored this topic, focusing on the thickness of stripped soil [START_REF] Grootjans | Restoration of brook valley meadows in the Netherlands[END_REF][START_REF] Lamers | The restoration of fens in the Netherlands [J][END_REF], topsoil stripping costs [START_REF] Pfadenhauer | Wetland restoration in Central Europe: aims and methods[END_REF][START_REF] Ramseier | Why remove the topsoil for fen restoration? --Influence of water table, nutrients and competitors on the establishment of four selected plant species [J][END_REF][START_REF] Klimkowska | Can we balance the high costs of nature restoration with the method of topsoil removal? Case study from Poland [J][END_REF], as well as fertilization and necessary conditions for topsoil stripping [START_REF] Xu | Prerequisites for preserving the fertility and the soil layer before farmland converted for construction use[END_REF]. The above mentioned research focused mainly on the project scale, and topsoil stripping suitability evaluations, especially regional comparisons, have rarely been reported. This article discusses the suitability of topsoil stripping based on the soil type from different regions in China, in order to provide references for future work in topsoil stripping.

Materials and Methods

Data Sources

Research data includes: 1: 100 million China Soil Type Maps, compiled and digitalized by the Institute of Soil Science, Chinese Academy of Sciences, based on the National Soil Survey Office 1995 data. Soil-related property data, achieved depending on typical soil profiles from different soil types, were recorded by the National Soil Survey Office in Chinese Soil [28] and China Soil Type [START_REF]Office of National Soil Survey, Chinese Soil Types• The First Volume-The Sixth[END_REF].

Research Design

A variety of factors have a combined effect on topsoil stripping, including soil thickness, surface soil properties, soil organic matter content, soil pollution, etc., which determine the quality of the stripped soil, and also affect the quality of the new farm land or improved farmland. Among these factors, the soil pollution factor has a veto effect. Soil already contaminated can no longer be stripped.

Topsoil stripping increases the amount of new arable land and improves low quality farmland mainly by increasing the thickness of the soil, improving soil structure (including surface soil properties) and increasing nutrient levels. Among these factors, increasing the thickness of the soil is most important because it is the soil nutrient source and a repository for mineral elements in the soil. It is also the determining indicator of soil erosion with significant impact on soil nutritional status. Also, the fertile stripping soil can be used to increase the thickness of the soil layer and provide nutrients for crop growth. So soil thickness and soil organic matter content are chosen as measure indicators for topsoil stripping suitability. The Farmland Grading Regulation GB / T 28407-2012 recommended classification method was used to decide grade and scores. Farmland quality grading regulations were determined by consulting specialists. The weighted average method was used to calculate the topsoil stripping natural suitability index, then suitability levels were divided according to specialists' recommendations.

Evaluation Indicators

Soil Thickness Index Processing

Soil thickness has a decisive role in topsoil stripping. Soil thickness determines the crop site's conditions, and also affects moisture reserve and root depth. To a certain extent, soil thickness determines the amount of stripping soil, and in turn determines the size and depth of the soil coverage. For mountainous areas, hills and other soilbarren areas, improving soil thickness is particularly important for increasing the amount of arable land.

Based on the national soil classification and index score from Farmland Grading Regulation GB / T 28407-2012, the productive soil thickness of is divided into five levels, and in turn given scores. Results are displayed in the following table:

Indicator Processing of Organic Matter Content

Soil organic matter is an important component of the soil solid phase, which determines the soil fertility level, and also can improve soil physical properties.

Typically the agriculture soil layer has the highest organic matter content compared to fast-acting fertilizers, and it needs a long time to form, affects the crop better, and is relatively stable. Topsoil stripping can form nutrient-rich organic layers directly over the transplantation site, shortening the soil maturation process.

According to the national indicators of soil organic matter content classification and score from China Farmland Grading Regulation, the soil organic matter content is divided into six levels, and in turn given scores (Table 2).

Natural Suitability Score Calculation

The topsoil stripping suitability score can be calculated by the following formula:

   n i i p i x Z 1
In the formula, Z is the impact score for topsoil stripping and re-use. X i is the indicator score and P i is the index weight.

In topsoil stripping, soil thickness and the size of the stripping area is limited, so the volume of stripped soil is fixed. Stripping soil thickness is restricted by the volume of stripped soil, but the organic matter content of the soil can be achieved through artificial fertilization, therefore, the thickness of soil is more important than soil organic matter content in topsoil stripping. According to specialists, the correct soil thickness weight is 0.6; organic matter content weight is 0.4.

The impact score Z is calculated based on the soil thickness classification score and the soil organic matter content classification score, which shows the different level of soil thickness and organic matter content's impact on topsoil stripping and reuse. The Z value is divided into five levels:

1. Z≥90, topsoil stripping suitability level 1, most suitable; 2. 80≤Z <90, topsoil stripping suitability level 2, suitable; 3. 70≤Z <80, topsoil stripping suitability level 3, moderately suitable, 4. 60≤Z <70, topsoil stripping suitability level 4, less suitable; 5. Z <60, topsoil stripping suitability level 5, unsuitable.

Results

Soil Thickness Classification Results

Based on 1: 100 million soil type map, a soil thickness classification map was produced, shown in Fig. 1:

Fig. 1. Classification of soil thickness

As can be seen from the figure, in China the regions with the thickest soil are mainly concentrated in the Northeast, the Loess Plateau and the North China Plain. Black soil and black earth soil of the Northeast, loess soil of the Loess Plateau, and moisture soil of the North China Plain are all thick soils, more than 150 cm thick. The regions with moderate thickness soil are mainly in the south part of China along the middle and lower Yangtze River, the area south of the Yangtze River, the southern China region and the northern part of China, such as the Inner Mongolia Plateau and regions surrounding the Great Wall. In southern China hydrothermal conditions are better, yellow soil, red soil, brick red soil, latosolic red soil, paddy soil, etc. are thicker.

Classification Results of Soil Organic Matter Content

Based on 1: 100 million soil type map, an organic matter content classification map was produced (Fig. 2).

Fig. 2. Classification of soil organic matter content

As can be seen from the figure, the regions with soil that has the highest content of organic matter are mainly in the Daxinganling Region of the Northeast and the Hengduan Mountainous region of southwestern China, where there is untouched natural forest cover, followed by the hilly areas of southern China, with good hydrothermal conditions and rich biomass. The moisture soil of Huang-Huai region and the loess soil of the Loess Plateau region have less soil organic matter content.

Topsoil Stripping Natural Suitability Assessment

Depending on topsoil stripping impact scores of the different soil types, topsoil stripping suitability evaluation results are displayed according to the different regions in China, shown in Fig. 3:

Fig. 3. Classification of topsoil stripping suitability

The most suitable areas for topsoil stripping are mainly in the northeast region, along the middle and lower Yangtze River, and the region south of the Yangtze River. The black soil, black earth soil, meadow Soil, red soil, brick red soil, and yellowbrown soil types, etc., are all rich in organic matter, suitable for topsoil stripping.

There are additional regions with moderate topsoil stripping natural suitability, those soil types are brown soil, cinnamon soil, irrigation-silting soil, heilu soil, paddy soil, moisture soil etc. The thickness of soil and organic matter content of those are not as good as black soil and black earth soil. Therefore the suitability level is lower than the black soil and black earth soil in the Northeast, as well as the red soil and yellow-brown soil in the region south of the Yangtze River.

Moderately suitable topsoil stripping natural suitability areas are mainly located in the northwest regions and the Inner Mongolia Plateau regions. The main soil types are yellow Soil, torrid red soil, red clay soil, brown desert steppe soil, etc. These types of soil have average layer thicknesses and low organic matter content; therefore suitability for topsoil stripping is average.

Less suitable areas for topsoil stripping are mainly scattered in the mountain regions, such as the volcanic ash soil, regosols, cracked soil, etc. These soils have thin layer thicknesses and low organic matter content.

Unsuitable areas for topsoil stripping natural suitability are mainly in the hilly regions of the Sichuan Basin and the eastern regions of northwestern China. These soil types are purple soil, gray-brown desert soil, alpine frost desert soil, cold desert soil, rocky soil, etc.

Discussion

Fertile Topsoil Improves Transplant Site Nutrient Content

Fertility levels of black soil and black earth soil are relatively high, therefore only these types of stripped topsoil can achieve the goal of improving the organic matter and nutrient content.

Therefore, purely from the perspective of topsoil stripping organic matter or nutrients, not all kinds of soils are worth stripping for the purpose of improving soil nutrient levels. Stripped topsoil from black soil and black earth soil regions with high organic matter content are suitable for nutrient enhancement. On the other hand, soil types, such as loessial soil and purple soil, are often subjected to erosion and lose rich nutrients; the use of fertilization and cultivation on these soil types is also adversely subject to erosion. These soils have low organic matter content so stripping the topsoil for soil culture cannot achieve the goal of enhancing soil nutrients.

Barren Surface Soil can be used for Thickening the Soil Layer

China is vast in territory, and there are regional differences in soil thickness. According to the second soil survey with data of 1627 soil profiles, the country has either significant isolated sections or continuous distribution characteristics: the North China Plain, the Yangtze River Plains, and the Loess Plateau, Inner Mongolia Grassland soils are relatively thick; mountainous and hilly soils are relatively thin; soil layers in Tibet, Qinghai, Xinjiang, Yunnan Plateau and parts of the Sichuan Basin are the thinnest [START_REF] Klimkowska | Wet meadow restoration in Western Europe: a quantitative assessment of the effectiveness of several techniques[END_REF]. In these areas the productive thickness of the soil layer is the limiting factor for crop rooting and growth.

Therefore, when considering improving the thickness of the soil and improving site cultivation conditions, even though construction sites are nutrient-barren land, its topsoil and even the soil under the arable layer can be stripped and used for increasing the soil thickness at transplantation areas and improves its water storage drought capacity. For example, hilly and mountainous regions are typically areas where the soil determines the arability and fertilizer determines the productivity. In order for the new site conditions to meet the requirements of increased crop growth and yield productivity depends mainly on whether or not the thickness of the soil can meet the needs of the crop root system, while yield productivity depends on how much soil nutrients are added later, along with other factors. Therefore barren soil areas should emphasize increasing the soil layer thickness with the assistance of the amount of added nutrients.

Stripping Fertilizing Based on Local Conditions

Stripping topsoil for increasing new arable land and fertilizing barren areas, must based on farmland soil fertility status and productive soil thickness, and soil fertility conditions of the stripped soil to do comparative analysis, then combined with the actual local situation, making the supply of the stripped areas and the needs of the transplant areas match. In general, if soil thickness is a major limiting factor for new arable land, cultivated soil layers below the arable layer can also be used for covering, without considering fertility; if the transplant site itself has enough thickness and soil nutrient content is the only limiting factor, then it must be covered by a soil with a relatively high level of fertility; if the topsoil stripping area has low soil fertility, or the surface fertility is similar with the lower layer, then conducting topsoil stripping is unnecessary.

Other Noticeable Issues

Factors affecting topsoil stripping include natural, social, economic, geographical, laws and regulations etc. In this article, only the natural factors were chosen for evaluation and the combined impact from social, economic, transportation, laws and regulations factors were not addressed. If these factors are also addressed with comprehensive and quantitative analysis, the results will be more accurate, objective, and easier to guide practical application.

Table 1 .

 1 National soil thickness grading and scores

	Level	Productive Soil Thickness (cm)	Score
	1	≥150	100
	2	100-150	90
	3	60-100	70
	4	30-60	50
	5	<30	10

Table 2 .

 2 National Soil Organic Matter Content and Grading Scores

	Level	Soil Organic Matter Content (g/kg)	Score
	1	≥40	100
	2	30-40	90
	3	20-30	80
	4	10-20	70
	5	6-10	60
	6	<6	50

Acknowledgements

Funding from National Natural Science Foundation of China, NO. 41301614.


Lili Yang

Jiafeng Chen

Jing Hua

Mengzhen Kang

Qiaoxue Dong

Interactive Pruning Simulation of Apple tree

Keywords: apple tree, pruning, interactive, simulation

Pruning treatment is one of the important management practices of perennial fruit tree cultivation. Combining fruit tree growth theory with mathematics model and software development is an effective way in agricultural research. Virtual apple tree is built as a case to study pruning treatment in this paper. First, architectural development of apple tree was analyzed and stored in xml file. Then, the simulation software of apple tree pruning is build based on the Qt framework and OpenGL graphics library. The interactive pruning and automatic pruning with setting conditions are realized in the software. Reaction laws of apple tree pruning are extracted from the analysis on the experiment data. Stochastic 3D apple tree architectures after being pruned also can be simulated. Result indicates that simulation is efficient, accurate and timely judgment of pruning reaction is possible. This work is the foundation of future research, which will simulate the apple tree development of architecture and biomass after pruning treatment over time.

Introduction

Tree architecture plays a key role in foliage distribution and consequently in light interception and carbon acquisition, which in turn strongly affect the reproductive growth of fruit trees. Pruning treatment is one of common measures for architecture study in perennial fruit tree cultivation.

Reasonable pruning plays a key role in tree fruit quantity and quality [START_REF] Lauri | Patterns of size and fate relationships of contiguous organs in the apple(Malus domestica) crown[END_REF].It had been found that removal of flowering shoots at a young stage of growth with less pruning of old branches tends to stimulate growth of the remaining shoots and fruit set of adjacent inflorescences [START_REF] Lauri | From tree architecture to tree tranining-an overview of recent concepts developed in apple in France[END_REF][START_REF] Lauri | Relationship between the early development of apple fruiting branches and the regularity of bearing-an approach to the strategies of various cultivars[END_REF][START_REF] Lauri | Vegetative growth and reproductive strategies in apple fruiting branches-an investigation into various cultivars[END_REF], Jean Stephan studied the effect of pruning strategies on shoot demography and development during 2 years based on experiment and demographic approach [START_REF] Stephan | Area in Apple(Malus domestica)[END_REF]. A biological model of apple tree production and a biomechanical model of apple tree development using mixed statistics are built successively [START_REF] Hester | A Biological Model of Apple Tree Production[END_REF][START_REF] Smith | Simulation of Apple Tree Development Using Mixed Statistical and Biomechanical Models[END_REF]. The perennial nature of apple tree make modeling complex, but the simplifications and codes description of tree topology presented by E.Costes make apple tree model possible [START_REF] Costes | Exploring Within-tree Architectural Development of Two Apple Tree Cultivars Over 6 Years.[J][END_REF][START_REF] Costes | Tree Architecture and Production[END_REF]. Many models are focus on the apple tree growth and development under the natural state rather than after pruning [START_REF] Costes | Modelling branching patterns on 1-year-old trunks of six apple cultivars[END_REF][START_REF] Seleznyova | Application of Markovian models to study root/interstock effects on flowering of young apple tree[END_REF]. Pruning research based on virtual apple tree model can be meaningful and efficient. However, few interactive virtual platform of apple tree pruning are found [START_REF] Xia | Evaluation of Importance of Pruning on Branching Structures in Nectarine Tree Using the Model of Hidden Semi-Markov Chain[END_REF][START_REF] Atkins | Virtual Reality in Horticulture Education, Pruning Simulated fruit Trees in a Virtual Environment[END_REF]

Overall design

The technical framework of apple tree pruning simulation is established on visual apple tree model combined with computer graphics technology. First, the hierarchical data organization which describes apple tree's morphological structure is formed based on the geometry structure of the apple tree.

Second, the three-dimensional model of apple tree is build based on OpenGL. Finally, an interactive interface of apple tree pruning simulation based on QT framework is developed. The work is showed in figure 1.

The Model

It is commonly accepted that plants are modular organisms that develop by the repetition of elementary botanical entities [START_REF] Barthelemy | Plant architecture: a dynamic, multilevel and comprehensive approach to plant form, structure and ontogeny[END_REF]. Here, apple tree architecture is described in Growth Units (GU) level, which were divided into latent bud, medium shoot (5-15cm), short shoot (<5cm) and long shoot (>15cm) according to the length. Topology structure of apple tree, which describes the connection relationship among all units, is modeled using the dual-scale automata approach [START_REF] Xing | Dual-Scale Automation Model for virtual Plant Development[END_REF][START_REF] Hong | A Review of Functional-Structural Tree Model[END_REF][START_REF] Ferraro | toward a quantification of set-similarity in plants[END_REF]. In this way, apple tree is divided into hierarchy according with self-similarity and branching order during apple tree growth.

Different years of germination branches reflect difference vigor, which is called physiology age (PA).

Except the trunk, primary branch on trunk is defined as PA1, and secondary branch on primary branch is defined as PA2 and so on. Each branch belongs to a certain GU according to the branch length. In commercial orchards apple tree usually are pruned short and less branch level. According to dual-scale automata model, four macro states are applied to simulate a young apple tree topology development. A local-to-global approach is taken in building apple tree 3D model. Apple tree is decomposed into four level structure unit: node、internode、branch、organ. Node refers to the key point of apple tree skeleton. Node information, including space coordinates and distance to internode surface, is calculated by extracting a certain rule based on some measured data. The adjacent nodes constitute an internode and a series of internodes constitute a branch. Organ refers to apple tree leaves and apple fruit. Single organ geometric structure is built by 3d scanning. All organs, whose space position, orientation and size are simulated by using the information of measured data in special coordination system, are assumed only having differences in position, angle and size for same type.

Data Organization

Two categories of data are included in apple tree model: one is node information, another is organ information. Both are saved as a standard XML file with different suffix. Only the key node information of tree skeleton is stored in model, not including the surface node information of the branches in order to reduce the data storage.

The information of function topology is added into geometry topology based on the three-dimensional model structure of apple tree mentioned in 3.1. Taking the branch for example, the model data include PA, POS and all the node information on the branch. The coordinates of the pruning point located the branch where the pruning point is on, then, the function topology information of selected branch like PA is obtained from the node information XML file. POS is designed as a sequence which stores the position order from trunk to branch or organ in an even number. For example, the list<10,1,8,3>, means a piece of leaf or a shoot born on the 3th small branch of the 8th node , which is on the first branch of the 10th node of the trunk. A branch and an organ data are organized as below. All nodes are described by 3D space coordinates x,y,z and the diameter of the node to the branch. The organ is described by a transfer matrix that can generate different size and direction organ by organ transformation. Vector is used to store the data of apple tree model by using the C++ programming language. The node vector corresponds to a node which contains the node coordinates and diameter of the branch which the node is on. The branch vector takes a single node vector as a basic item, and the whole apple tree vector takes a single branch vector as a basic item. The branch position vector consists of POS of each branch, and so as branch age vector.

Interactive pruning simulation

Interactive pruning includes deterministic point pruning and combined conditional pruning.

Deterministic point pruning means pruning the branch from the mouse clicked point. The following steps are needed: (1) Coordinates transformation. It refers to transform the pruning point coordinates given by mouse click to the coordinates in apple tree model space. In OpenGL. First, transform 2d coordinates on screen to 3d view-part coordinates. Then, transform view-part coordinates to the model coordinates.

(2) Considering that the coordinates processed in (1) is on the surface of the branch and may not be contained in the file of node information, a method based on distance is applied to establish a mapping between clicked-point and points in the apple tree model. The method tries to find the closest node to the pruning point and then get the POS character of the branch with pruning point.

(3)The last step is to find all the related branches based on the POS character of matching point, and deletes point data and organ data of corresponding branch.

There are many pruning treatments in orchard management, not only including shape pruning and conditional pruning determined by branch or organ types, angles or PA, but also including different level pruning based on branch length, branch ratio and so on. Combined pruning refers to interactive pruning of apple tree in the way that combines mouse clicking with pruning parameters input.

For example, make the apple tree spindle shape and cut off 1/3 of the branches whose physiology age is 2, the steps of interactive pruning are as follows: (1) Click the key point on the screen to define spindle shape.(2) Expand 2d spindle shape to 3d spindle body.(3) Pruning judgment. First, delete the related nodes which are not in the scope of 3d spindle body. Second, find all the branches whose physiology age is 2 and then find the nearest nodes of the point at a third of the branch; finally delete the related information based on the found node coordinates. ( 4) Redraw the apple tree model based on the updated data information.

Running time of interactive pruning simulation

Interactive pruning simulation on apple tree is running on an ordinary computer ( ③ Prune to make the tree look like sphere shape 1

④ Cut off all the branches of PA = 3 3

⑤ Cut off all the branches whose length < 0.1m 1

Pruning with conditions ⑥ Cut off 1/3 of the branches of PA = 2 2

⑦ ④+⑤ 3 ⑧ ④+⑥ 3 ⑨ ④+⑤+⑥ 3
The results show that the pruning system makes it easy for users to select the branches need to be cut by mouse clicking or keyboard input with a single condition or several conditions combined. The simulation time with the average response time is ms level, which is independent of the complexity of pruning. The simulation of natural pruning could be achieved on ordinary PC hardware. The efficiency of the simulation could be probably improved if the GPU technology is applied. The system comes into the view mode in default after the data of the tree model loaded. In this mode, mouse and keyboard are used to move, scale or rotate the visual tree model, then the exact position of pruning is easily to locate. The branches and organs are selected to be cut off by different ways in pruning mode after mode switching. Pruning is done before growth resumed in the spring when the leaves have not come out yet. Figure 3b, 3c clearly show the rendering of two different pruning cases.

3D results of interactive pruning simulation

The pruning effect can be clearly observed.

Discussion

Apple tree geometric model is established by the method that dividing the model into different structural units and levels, which reduces the complexity of the apple tree three-dimensional modeling.

The topological relationship between the branches is well documented by reasonable way of data organization so that it is easily to realize the pruning operation that all the child branches also are cut off when the mother branch is cut off, which simplify pruning realization and improve simulation efficiency.

Both shape pruning and combined conditional pruning are considered in the view of actual planting.

Two interactive ways of pruning, i.e.by mouse or keyboard and by parameters input, are provided for the convenient user operation. Stochastic architecture after pruned also is simulated according experiment data. If we can get more nodes information on pruned branch from base to top, discrete-time and discrete-state-space stochastic processes can be used to illustrate the branching architecture for any pruning measure. Now stochastic branch is limited to batch pruning for same physiology age branch.

The method of interactive pruning on visual apple tree in this paper can provide a reference for pruning simulation of other species. It also can be used in entertainment, education or tree research.

Meanwhile, this work provides an intuitive, flexible auxiliary tool for the further research on the tree pruning effect combined with the function model of the apple tree. The stochastic architecture showed in Fig 3 cannot be well explained, if tree structure model is considered independently. Pruning causes apple tree architecture changed. New organs will create, which change apple tree production (source) and demand (sink) ability. New local and global source-sink ratio is closely related to pruning bud fate.

Function-structural plant models (FSPM) have more advantages to explain it [START_REF] Lin | Fitting a Functional Structural Plant Model based on Global Sensitivity Analysis[END_REF][START_REF] Wang | Simulating Plant Plasticity under Light Environment: a Source-Sink Approach[END_REF][START_REF] De Reffye | Stochastic modelling of tree annual shoot dynamics[END_REF][START_REF] Vos | Functional-structural plant modeling: a new versatile tool in crop science[END_REF]. With more field experiment and model developed, different virtual pruning treatment can be done to minimize the vegetative branch. Optimal pruning strategy can be got by analyzing source-sink ratio of reproductive branch. Optimization of simulation algorithm, the extensibility of the program simulation and verification of the simulation can be further studied.

  . The work in this paper include: (a) Build a virtual apple tree model based on architectural development of apple trees. (b) Design and realize 3D interactive pruning simulation on general PC. Pruning strategies commonly used in real planting are considered in this platform. (c) Stochastic 3D architectures of virtual apple tree were modeled after pruning. The work is the foundation of further work, which focuses on the relationship between structure change which is reduced by pruning of apple tree and function change regarding as physiological character of fruit tree.

Fig 1 :

 1 Fig 1: Workflow of pruning simulation

Fig 2 .

 2 Fig 2. Dual-scale automata model of a 5 years old apple tree

Fig 3 .

 3 Fig 3. Renderings of two pruning cases with interactive interface.

4. 3 Fig 4 .

 34 Fig 4. Stochastic architecture of pruned tree

Table 1 .

 1 Running time of different pruning modes

	INTEL CORE

Acknowledgements

This study was supported by the National Natural Science Foundation of China (#31200543).


Shuping Tang

Dean Zhao
email: bdazhao@ujs.edu.cn

Weikuan Jia

Yu Chen
email: dchenyu@ujs.edu.cn

Wei Ji
email: jiwei@ujs.edu.cn

Chengzhi Ruan

Feature Extraction and Recognition Based on Machine Vision Application in Lotus Picking Robot

Keywords: Picking Robot of lotus, Feature Extraction, Invariant moment, Principal component analysis, K-Means clustering

Recently the picking technology of high value crops has become a new research hot spot, and the image segmentation and recognition are still the key link of fruit picking robot. In order to realize the lotus image recognition, this paper proposes a new feature extraction method combined with shape and color, and uses the K-Means clustering algorithm to get lotus recognition model. Before the feature extraction, the existing pulse coupled neural network segmentation algorithm, combined with morphological operation, is used to achieve nice segmentation image, including lotus, lotus flower, lotus leaf and stems. Then in the feature extraction processing, the chromatic aberration method and the moment invariant algorithm are selected to extract the color and shape features of the segmented images, in which principal component analysis algorithm is selected to reduce the dimension of the color and shape features to achieve principal components of lotus, lotus flower, lotus leaf and stems. In the experiment, K-Means clustering algorithm is used to get lotus recognition model and four clustering centers according to above principal components of training samples about lotus, lotus flower, lotus leaf and stems; then the testing experiment is applied to validate the recognition model. Experimental results

shows that the correct recognition rate is 90.57% about 53 testing samples of lotus, and the average recognition time is 0.0473s, which further indicates that the feature extraction algorithm is applicable to lotus feature extraction, and K-Means algorithm is simple, reliable and feasible, providing a theoretical basis for positioning and picking of lotus harvest robot.

Introduction

Recently, the picking technology of high value crops has become a new research hot spot, and lotus belongs to high value crops because of its great medical value, high risk of picking and high labor input required [1~3]. Fresh lotus seeds have great medicinal value in calming nerves, having healthy brain, developing intelligence and removing fatigue, but the high risk and low efficiency of manual picking restrict the lotus market development. Therefore the study of lotus picking robot has broad research prospect.

The image segmentation and recognition algorithms are key link of lotus harvesting robot vision system, and the ability to accurately and quickly identify the lotus directly affects the reliability and timeliness of lotus harvesting robot [4~6].

Then many factors increase the difficulty of lotus recognition, for example, the growth of lotus is changeable and mature lotus, lotus leaf, lotus flower and lotus stem often densely distribute in an area of the image, usual recognition algorithm based on RGB components [START_REF] Qian | Mature apple recognition based on hybrid color space in natural scene[END_REF][START_REF] Shen | Image recognition of green weeds in cotton fields based on color feature[END_REF] is difficult to distinguish the lotus from the complex background because the colors of lotus leaf and lotus stem are closer than lotus.

However it is obvious about color difference between lotus and lotus flower, and shape difference between lotus and lotus flower and lotus stem, so in this study, the chromatic aberration method and the moment invariant algorithm are selected to extract the color and shape features from the divided images[9~13], in which there are seven moment invariant shape characteristics and one color feature (r-g) extracted from each connected domain in divided image. The moment invariant algorithm is simple and reliable, but has too many extracted characteristic variables, that causes the calculation is too big, and the time of recognition is too long, then the principal component analysis (PCA) algorithm is introduced to reduce the dimensionality of color and shape information in order to accelerate the speed of image , is used to achieve nice segmentation image, including lotus, lotus flower, lotus leaf and stems. Then the chromatic aberration method and the moment invariant algorithm are selected to extract the color and shape features of the segmented images, in which principal component analysis algorithm is selected to reduce the dimension of the color and shape features to achieve principal components of lotus, lotus flower, lotus leaf and stems.

Image Acquisition and Segmentation

Image Acquisition

Lotus pictures researched in this paper are taken from the lotus pond of Jiangsu university in Jiangsu province. In here, we choose by CCD color light source with 1024x960 pixels resolution to get lotus color images. Then 120 images, used for training and testing experiments, are closely shot with the method of elevation shooting on the side of lotus live action, in which we adopt 15 to 30 degrees elevation through the experience summary, and each picture include lotus and lotus stem with lotus flower or lotus leaf. Moreover all lotus pictures are shot under natural light conditions, and saved by JPG format with the resolution of 320 x 240 pixels in order to improve the seed of image process. Figure 1 is a lotus real picture.

Image Segmentation Algorithm Introduction

This paper chooses the existing PCNN segmentation algorithm proposed by Ma Yide professor from Lanzhou University to get the segmentation images. The method uses PCNN segmentation algorithm and morphological image processing algorithm to divide the acquired image [START_REF] Wang | Review of pulse-coupled neural networks[END_REF][START_REF] Wang | In-greenhouse cucumber recognition based on machine vision and least squares support vector machine[END_REF]. Neuron model of simplified PCNN is shown in Figure 2.

Connection Impulsator Input W 1+βL ε(Uij-Eij) E Lij Fij Yij(n) Uij Eij Yij(n-1)

Coupling connection subsystem

Feedback input subsystem

Modulation subsystem

Dynamic threshold subsystem

Ignition subsystem

Fig. 2 Neuron model of simplified PCNN Mathematical model of simplified PCNN neurons describes as follows: [START_REF] Zhao | Design and control of an apple harvesting robot[END_REF] In

ij ij S n F  ) ( (1) ) 1 ( ) (     n Y W V n L kl ijkl L ij (2) )) ( 1 )( ( ) ( n L n F n U ij ij ij    (3) ) 1 ( ) 1 ( ) (        n Y V n E e n E ij E ij ij E  (4)         )) 1 ( ) ( ( 0
)) 1 ( ) ( ( 1 ) ( n E n U n E n U n Y ij ij ij ij ij
D V L   

Image Feature Extraction

Considering the color differences between lotus and lotus flower and the shape differences between lotus, lotus leaf and lotus stem, this paper takes the color and shape feature extraction algorithm to extract the color and shape features of target image.

Color Feature Extraction

This paper uses color difference method to extract the target color feature based on the difference between lotus and lotus flower.

In the RGB color space, colors can be compared directly, and the color characteristic information can be controlled easily, so we can choose RGB color difference method for feature extraction [START_REF] Dun | A new distance color difference formula in RGB color space[END_REF][START_REF] Zhang | Recognition of green apple in similar background[END_REF]. The classic color feature extraction method is selected to extract the color features of target image, in which R component and G component are regarded as color feature according the color difference between lotus and lotus flower. We choose ) ( g r  on behalf of color difference, and ) , ( y x on behalf of arbitrary pixel values in the image, then the formula of color difference is

) , ( ) , ( _ _ y x G y x R g r    (6) Where ) , ( _ y x R is average pixels of red component, ) , ( _ y x G
is average pixels of green component.

Shape Feature Extraction

Classic Hu invariant moments algorithm combined with PCA algorithm is selected to extract the shape features of segmented image considering about the significant shape differences of lotus, lotus leaf, lotus stem, and changeable growth posture of lotus.

Moment invariant algorithm processes the area of the target, and has simple concept, invariant translation, scaling and rotation, and stable and reliable recognition rate, which can avoid the shape deviation caused by changeable growth posture of lotus.

PCA algorithm is used to get the invariant feature extraction to obtain comprehensive shape characteristic descriptions of the target image, which avoids the low recognition rate caused by invariant moment data redundancy.

Hu Invariant Moment Feature Extraction

Hu invariant moments is put into use for extracting the shape features of each connected area in target image, the seven invariant moments are as follows:

] ) ( ) ( 3 )[ ( ) 3 ( ] ) ( 3 ) )[( )( 3 ( ) )( ( 4 ] ) ( ) )[( ( ] ) ( ) ( 3 )[ ( ) 3 ( ] ) ( 3 ) )[( )( 3 ( ) ( ) ( ) 3 ( ) 3 ( 4 )
                                                                                                    
) ( q p  moment of the entire connected domain is   x y q p pq y x y x f m ) , ( (8)
From the above formula, we can get the image barycentric coordinates is Then, we use barycentric coordinates to get the center distance which is as follows

    x y q p pq y x f y y x x ) , ( ) ( ) ( 0 0  (10)
From the above formula, we know that central distance has translation invariance.

Then we need normalize the central distance in order to get the normalization central distance which has rotation, translation and scaling invariance. The formula of normalization central distance is as follows:

2 / ) ( 1 00 q p pq pq       (p+q=2,3,4• • • ) ( 11
)
By the above principle formula, we can get the seven Hu invariant moment shape feature quantities of the lotus, lotus leaf, lotus flower, lotus stem.

PCA Feature Reduction

We can get seven Hu invariant moments which are used to describe the shape feature amounts of the lotus, lotus leaf, lotus flower, lotus stem from the section 3. [START_REF] Yang | Shape-based invariant feature extraction for object recognition[END_REF] To calculate the correlation coefficient matrix

ij r ,            n k n k j kj i ki n k j kj i ki ij a a a a a a a a r 1 1 2 2 1 ) ( ) ( ) )( ( (13)
According to the correlation coefficient matrix

R to construct characteristic equation 0   R I 
, and to calculate the eigenvalue, and sorted them by their value, that is,

p        2 1
. By the eigenvalue, to calculate the principal component contribution rate  and the accumulative contribution rate , ) ,...,

p i i i        (14) ) ,..., 2 , 1 ( 1 1 p i p i i m i i          (15) 2 , 1 ( 1 p i
The front m eigenvalues make η ≥ 85%, to calculate their corresponding eigenvectors, and to calculate the loading ij l of invariant moments

ij i j i ij      (16)
And extract the front m principal components

                                                          7 7 3 3 2 2
a l a l a l a l a l a l a l a l a l a l a l a l a a a l l l l l l l l l z z z Z m m m m m m m m              ( 17
)
The basic steps of optimized Hu invariant moments algorithm based on PCA are as follow:

Step 1 to extract the q p  moments of each segmented image's connected domain, according to equation ( 8) calculate the barycentric coordinate ) , ( 0 0 y x ;

Step 2 according to equation ( 9) calculates the center distance, and normalized by equation [START_REF] Chaki | Plant leaf recognition using texture and shape features with neural classifiers[END_REF];

Step 3 by equation [START_REF] Banerji | New image descriptors based on color, texture, shape, and wavelets for object and scene image classification[END_REF] to calculate the each eigenvalue of invariant moments corresponding with connected domain, and extract the shape features, then obtained invariant moments' information of all samples;

Step 4 aim at the data sample matrix, to calculate its the correlation coefficient matrix R by equation ( 13);

Step 5 to calculate the eigenvalue of matrix R , and sorted them by their value;

Step 6 according to equation (15) determine the front m principal components;

Step 7 by equation ( 16) to calculate the load of principal components, and obtained the principal features Z .

K-means Cluster Analysis

K-means cluster algorithm is one of classic cluster algorithm, it has the advantages of description easy, algorithm simple, operating efficient high, and suitable for large-scale data processing. In this study, using the K-means cluster algorithm can be accurately obtain the lotus clustering center, to achieve the purpose of fast recognition for lotus.

In the clustering process, the selection of parameters is directly influence the reliability and recognition efficiency of the experiment. The number of clusters is K , the K initial clustering centers and the distance parameter d plays a decisive role for K-Means cluster algorithm. The selection of K has a certain human factors, and the selection of K initial clustering centers has a certain randomness, all of these have a directly impact on the result of clustering.

In this study, the city distance is adopted, Suppose the number of samples is n , using the color and shape features to cluster by K-means algorithm, the basic steps are as follow.

... | ' | | ' | | ' |        z z y y x x d (18)
Step 1 the color and shape features of n samples as inputs;

Step 2 to determine

K initial clustering centers   k c c c c , , , 2 1   randomly, the samples is divided into K classes;
Step 3 to calculate the city distance between eigenvalue of each sample to the center of clustering, according the principle of the shortest distance, each sample is divided into the sub-classes of corresponding cluster center;

Step 4 to calculate the average value of each sub-class, and regard as the new cluster center;

Step 5 repeat the step 3 to 4, until the cluster center no longer changes, the algorithm stop, and output the cluster graph and cluster center C .

Image Recognition

The The basic steps of lotus image recognition algorithm are as follow.

Step 1 the captured image is segmented by PCNN algorithm, get the segmented images;

Step 2 to extract the color feature information of segmented images by color difference method;

Step 3 to extract the Hu invariant moments of segmented images, using PCA algorithm to reduce dimension, get the principle component data of sample;

Step 4 the color feature and principle components as the inputs, for the training samples, by K-means clustering algorithm, to determine the four cluster centers, that is, lotus, lotus leaf, lotus flower and lotus stem, and then get the trained recognition model;

Step 5 to calculate the distance between test samples to each cluster center;

Step 6 to judge the distance to lotus cluster center whether is closest, if yes, the sample is the lotus target.

Experiments

Experiment Design

This experiment's operation platform, CPU: Intel(R) Core(TM) 2 Duo E7300 @ 2.66 GHz, RAM: 1.99GB, operating environment: windows XP and matlab R2012a, graphic card: Intel® G33/G31 ECF.

In the experiment, the captured 120 images, all of these images are captured under natural light. In the image process, the pictures are converted to 320×240 pixels to improve the speed of image processing. After the image segmentation, the segmented images are divided into training samples and testing samples.

This experiment firstly uses combination method of PCNN and morphological processing to segment the captured images, obtained segmented images; respectively uses color difference method to extract the color feature of segmented images, uses Hu invariant moments method based on PCA to extract the shape feature of segmented images; and then uses K-means algorithm to cluster the training samples, obtained K cluster center; finally, classifies the test sample according to the distance to cluster center in order to verify the reliability, recognition precision and operating efficiency of the lotus recognition model.

Lotus Image Segmentation

All images are segmented by PCNN method described in section 2, and then do some morphological processing, obtained the segmented images. Segmentation images are pictured above figure 3. Then we select 3×3 circular structure element to etch figure 3 and use 4×4 structural elements to expand corrosion image, and then make '&' operation between expansion image and original image. The final segmentation image as shown in figure 7.

Lotus Image Feature Extraction

Hu Invariant Moments

For all segmented images, using the invariant moment algorithm to extract the shape feature, and get the invariant moment data of all samples, each sample has seven feature variables From table 1, the results show that the value of invariant moments has a descending trend, except lotus stem, this is caused by the particularity of the shape of lotus stem. The scope of invariant moment of lotus is part overlap with lotus flower and lotus leaf, this will bring some interference for lotus recognition. Thus it can be seen, single from the shape feature to recognize lotus, lotus flower, lotus leaf, is very difficult, so in this study, need to combine with the color feature.

PCA Feature Reduction

Invariant moment information is not directly used to recognize the lotus, lotus flower, lotus leaf and lotus stem. The data information is too redundant, is not convenient to cluster analysis. So in this study, in order to using these invariant moment information, need to dimension reduction. PCA algorithm is introduced, and the invariant moment information is integrated into a few principal components.

We do principle component analysis for 200 segmented sample, and the eigenvalue, contribution rate (CR) and accumulative contribution rate (ACR) are listed in table 2. Table 2 shows that, if the amount of information can reach 85%, the solution will not be influenced, in this study need to extract 3 principal components. That is, the dimension of the shape features is reduced from 7 to 3. These 3 principal components represent the shape feature of lotus, lotus flower, lotus leaf and lotus stem.

Combination of Color and Shape Feature

Combination of one color feature and three shape features, using these four variables to describe the characteristics of the sample. Take table 1 as an example, the samples corresponding feature are listed in table 3. Table 3 shows that, these four variables regard as the inputs of the training and recognition model.

Training

In the process of lotus recognition, the interference factors mainly include lotus flower, lotus leaf, lotus stem, so in the training, set . The transfer formula is

       ) ( 01 . 0 )' ( | * . * . | lg ' 3 2 1 g r g r z z z z (20)
The clustering figure is show as figure 6.

c c c c C         (21)
Figure 8 shows that, the 80 samples are clustered into four classes, the cluster area is obvious. It is illustrate that K-means cluster algorithm is suitable for distinguish lotus, lotus flower, lotus leaf, lotus stem. Lotus flower area locate at above coordinate axis ) 0 , (x , it well to distinguish the lotus flower and the lotus, lotus leaf, and to avoid the lotus recognition interference caused by lotus flower. Lotus flower area locate at right coordinate axis ) , 1 ( y  , the cluster area is fragmented, it is verify that the difference of lotus stem shape is bigger, the recognition interference is small. The cluster area of lotus is relatively concentrated, it has obvious boundary with lotus leaf, noninterference with each other. It is further illustrating that K-Mean clustering algorithm for lotus recognition is simple and reliable.

Testing

For 120 test samples, to calculate the city distance of each sample to each cluster center, according the shortest distance to judge the sample belong to which class. The results of recognition are listed in label 4. From table 4, the results show that, using for testing 53 lotus images, the number of correct recognition image is 48, 2 images are misrecognized to lotus flower, 3 images are misrecognized to lotus leaf, the recognition precision of lotus is 90.57%.

For 19 lotus flowers, the recognition precision is 84.21%, 3 images are misrecognized to lotus, because they are un-blooming lotus flower, their shape is very similar with lotus, and color also is very close.

For 22 lotus leaves, the recognition precision is 90.91%, 2 images are misrecognized to lotus, due to the different shooting angle of the camera, and the growth posture of the lotus is variety.

For 26 lotus stems, the recognition precision is 100%, because the shape of lotus is difference with other target.

To test 120 images, the average recognition time of each image is 0.0473s, recognition time include feature extraction and testing time.

Conclusion

In order to achieve the automatic harvesting lotus, this paper carries on some related preliminary study of lotus harvesting robot, and a lotus recognition method for vision system of harvesting robot is designed.

Lotus target image recognition based on machine vision, the target image segmentation by PCNN, and obtained the segmented lotus image, lotus flower image, lotus leaf image, and lotus stem image. Due to the lotus has color difference with lotus flower, has the shape difference with lotus leaf and lotus stem, so the feature extraction method of lotus target adopts the combination of color and shape feature.

In the color feature extraction, color difference method is used to extract the red and green feature difference, to avoid the interference from lotus flower to the lotus and lotus leaf recognition. In the shape feature extraction, Hu invariant moment algorithm is adopted, combine with PCA algorithm, to reduce the dimension of shape features.

The extracted principal components represent the shape feature of lotus, lotus flower, lotus leaf and lotus stem. It reduces the data redundancy, improve the computing speed.

All samples are divided into training sample and testing sample. For training sample, by K-means cluster algorithm to determine four cluster center of lotus, lotus flower, lotus leaf and lotus stem, obtained the lotus recognition model. In the K-means cluster algorithm, city distance is adopted as the criteria of similarity and discriminated. In order to easily observe the cluster figure, make its display in 2d coordinate system, the extracted four features need to be done linear combination.

Finally, the city distance of each testing sample to four cluster center has been calculated, according the shortest distance to discriminate sample of belonging.

By the experiment analysis, from image segmentation and feature extraction, to recognition model training and testing sample recognition, this study adopts the method of lotus image processing, it gets the better effect, and the results are practical and reliable. From the lotus correct recognition rate, this study achieves the desired effect. This paper implements the preliminary discussion on the lotus auto-harvesting, next step of study, we will do some improvement on the recognition accuracy and efficiency.

  recognition[14~17]. K-Means clustering algorithm is simple, reliable and suitable for high dimensional data clustering[18~21]. Due to the higher dimension of sample characteristics and the rapid identification requirement, we choose the most classical K-Means algorithm to cluster the extracted training feature set in the case of ensuring the recognition accuracy and to get four clustering centers of lotus, lotus leaf, lotus flower and lotus stem in order to identify lotus goal. This paper describes a machine vision recognition system of lotus picking robot, and proposes a new shape and color feature extraction method combined with K-Means clustering algorithm to get lotus recognition model. Before the feature extraction, the existing pulse coupled neural network segmentation algorithm(PCNN), combined with morphological operation[22~25]

Fig. 1

 1 Fig.1 Lotus color picture

  figure 1 as an example, and after PCNN segmentation and morphological noise reduction, we can get the initial segmentation figure of figure 1 as figure 3.

Fig. 3

 3 Fig.3 Segmentation Image of Figure 1 by PCNN Then we use the corrosion and expansion morphology method to remove the lotus stem connected with lotus and lotus flower in figure 3.

Firstly

  is normalized central moment, which can ensure the rotation, translation and scaling invariance of Hu invariant moments. We get the normalized center moment through the following steps.

  order and first-order moments of the target image respectively.

  2.1 feature extraction principle. But the calculated amount of Hu invariant moments is bigger and the dimension of feature amount is too high, PCA algorithm is introduced, to get on the feature extraction, in order to reduce data redundancy and avoid curse of dimensionality. The PCA algorithm gets the linear combination for the obtained seven invariant moments, and extracts the principal components which can able to represent the different shape features of lotus, lotus leaf, lotus flower and lotus stem.Suppose, the sample size is n, each sample has p invariant moments, so there is a p n  matrix,

  cluster center and the coordinate point of sample.

  figure 4, the flow chart of model training is show in figure 5, the flow chart of sample recognition is show in figure 6.

Fig. 4 Fig. 5 Fig. 6

 456 Fig. 4 the flow chart of feature extraction

Fig. 7

 7 Fig.7 Final segmentation image From figure 7, most lotus stem has been removed, only leaving a small part connected with lotus and lotus flower for the positioning of the fruit and picking, and the outline of segmentation target is clear, the connected area is obvious. In other words, it is easy to extract the target's features. So the result shows that PCNN used to segment lotus image is feasible and reliable. The captured 120 images, after segmentation, obtained 200 segmented images, among 73 lotus images, 39 lotus flower images, 44 lotus leaf images and 46 lotus stem images. Respectively, selected 20 images from each type segmented images as training sample, the rest of the images as the test sample.

.

  The determination of cluster center is randomly, selected the city distance as the criteria of similarity and discriminated. From 200 samples, respectively, choose 20 lotus, 20 lotus flowers, 20 lotus leaves and 20 lotus stems, so total 80 samples as training sample. The 80 training samples can be divided into four classes by K-means cluster algorithm,

Fig. 8

 8 Fig.8 Clustering chart of K-MeansNote: Abscissa represents the linear combination of principal components

  (

	1	02	20
			2	2
	2	20	02	11
			2			2
	3	30	12		21	03
			2			2
	4	30	12	21	03
							2		2
	5	30	12	30	12	30	12	21	03		21	03
					2		2
		21	03	30	12	21	03
					2		2
	6	20	02	30	12	21	03	11	30	12	21	03
							2		2
	7	21	03	30	12	30	12	21	03		12	30
					2		2
		21	03	30	12	21	03

Table 2

 2 eigenvalue, contribution rates and accumulative contribution rates

	principal component	eigenvalue	CR, %	ACR, %
	1 z	4.99E+00	71.286	71.286
	2 z	1.15E+00	16.429	87.714

Table 3

 3

		feature variables of example samples
	sample	z 1	z 2	z 3	r-g
	a	2.98E-01	-5.36E-02	-6.15E-02	-1.04E+01
	b	3.04E-01	-5.41E-02	-6.26E-02	-1.62E+01
	c	1.83E-01	-3.43E-02	-3.86E-02	6.70E+01
	d	3.53E-01	-6.67E-02	-7.50E-02	2.84E+01
	e	4.07E-01	-8.68E-02	-9.42E-02	-5.24E+01
	f	3.78E-01	-7.70E-02	-8.48E-02	-4.31E+01
	g	2.53E+00	-5.63E+00	-4.92E+00	-1.84E+01
	h	1.90E+00	-1.38E+00	-1.22E+00	-1.93E+01

Table 4

 4 Recognition result of testing images

	indexes	Lotus	Lotus flower	Lotus lesf	Lotus stem
	Number of images	53	19	22	26
	Correct recognition	48	16	20	26
	Misrecognized to Lotus	-	3	2	0
	Misrecognition rate, %	9.43	0	0	0
	Correct recognition rate, %	90.57	84.21	90.91	100
	Recognition time, s		0.0473

Note: Recognition time is the average of total time, include the color feature extraction, shape feature extraction, and test running time of each image.

Acknowledgments

This work is supported by the National Natural 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); Ordinary University Graduate Student Research Innovation Projects of Jiangsu Province (No. KYLX 14_1062).