Hassan Nehme

Clément Aubry

Romain Rossi

Rémi Boutteau

An Anomaly Detection Approach to Monitor the Structured-Based Navigation in Agricultural Robotics

Local perception navigation methods allow agricultural robots to accurately track crop row structures while performing automated farming tasks. The integration of these methods as a part of a fully autonomous navigation solution requires continuous assessment of their reliability since they rely solely on sensor data in a changing and unpredictable environment. This paper presents a data-driven monitoring approach for the task of structure-based navigation in agriculture. The proposed method applies semi-supervised anomaly detection, aiming to learn a model of normal scene geometry that characterizes a domain of reliable execution of the considered task. To this end, a convolutional neural network was trained in one-class classification fashion on Hough representations of LiDAR point clouds. In experimentation, the learned normal model was used to derive a confidence measure for a LiDARbased tracking algorithm allowing its integration as a part of a hybrid navigation solution in vineyards for a commercial robotic platform.

I. INTRODUCTION

Autonomous robots are essential for improving farming productivity, with autonomous navigation being a key feature in their design for precision agriculture applications. Automated farming tasks are generally performed while accurately tracking parallel crop rows. A current research interest is to develop hybrid navigation methods [START_REF] Kanagasingham | Integrating machine vision-based row guidance with GPS and compass-based routing to achieve autonomous navigation for a rice field weeding robot[END_REF] that integrate local perception systems (e.g., LiDAR, vision) along with Global Navigation Satellite Systems (GNSS). In this setup, local perception exploits geometric patterns and provides accurate tracking with respect to the perceived structure in the field e.g. crop rows. Therefore, a hybrid navigation method reduces the accuracy requirements of global navigation systems that will manage global aspects of the task such as transitioning from one row to another, while leaving accurate structure tracking for local perception.

Local perception navigation methods are interesting since they eliminate the effort of creating a predefined path while providing local awareness and responsiveness with respect to the actual structure. However, this increased flexibility means that the structure-based navigation function will rely solely on sensor data acquired in an unpredictable environment under uncontrolled conditions. This raises concerns about the viability of local perception methods as a whole solution for near production robots since a perception algorithm is as reliable as its input data. A way to respond to these 1 SITIA, 7 rue de l'halbrane, 44340 Bouguenais, France f.last@sitia.fr 2 Normandie Univ, UNIROUEN, ESIGELEC, IRSEEM, 76000 Rouen, France first.last@esigelec.fr 3 Normandie Univ, UNIROUEN, UNILEHAVRE, INSA Rouen, LITIS, 76000 Rouen, France. first.last@univ-rouen.fr concerns is by providing an assessment of input data quality through data-driven monitoring. Such an assessment avoids failures due to the application of a perception algorithm in anomalous situations: ranging from low level anomalies such as corrupted data due to poor acquisition conditions (e.g., low light, shadows, a leaf covering the sensor) to high level anomalies caused by the perceived scene not complying with the base assumptions of the tracking algorithm. Moreover, a quantitative assessment providing a confidence measure defines guidelines for integrating the perception algorithm as a part of a complex or hybrid solution with deliberation and planning functions.

This work proposes a monitoring method for the general local structure-based navigation task in agriculture. As a main contribution, we show that the Hough transform representation of LiDAR point clouds can be exploited as an abstraction of the field's geometry that allows a convolutional neural network model to capture a normal1 criterion about the traversability of the structure. The monitoring is done through anomaly detection on the Hough representation of input data. A fully convolutional one-class classifier is trained on data acquired during normal execution of the task. The goal of the classifier is to provide an anomaly score reflecting the conformity of a test observation with a learned model of normal execution. The anomaly score can be treated as a confidence measure to be used to decide whether to rely on the execution of the monitored task or to switch to another behavior and report an error.

The proposed method was illustrated in experimentation where monitoring the task of structure-based navigation of an agricultural robot in vineyards was considered. The monitoring provided a fusion rule defining the integration guideline of a LiDAR-based structure tracking method as a part of a hybrid navigation solution. The derived fusion rule defined the domain of application of the LiDAR-based method based on pure assessment of input data quality. Moreover, the fusion rule provided a smooth transition to GNSS-based Uturns near the end of each vine row where less information about the structure is available for the perception algorithm impacting the quality of its tracking signals.

The remainder of the paper is organized as follows: section II provides preliminaries on the structure tracking task and reviews related anomaly detection methods, section III states the problem and explains the steps of the proposed method, an evaluation of the proposed method in an experimental case study is provided in section IV and finally section V concludes the paper.

II. PRELIMINARIES AND RELATED WORK

This work proposes a monitoring approach for the general structure-based navigation task in agriculture. We are interested in the viability of methods and how they fit in the overall solution rather than the specific technique implemented. The questions to be asked are: how reliable is the perception-based algorithm and how well does it fit in a complex solution ready for commercialization (e.g. integration in a hybrid navigation solution). Not enough attention is given to these issues in the literature since the main concern is usually to develop a new tracking technique or algorithm. Yet, they are important when deciding which technique to deploy in a production robot. To this end, integrating a structure tracking algorithm as a navigation solution in a system requires defining a confidence measure whether it is implemented as a sole solution or as part of a hybrid solution (based on sensor fusion). Prior attempts to define the confidence measure used algorithm-specific [START_REF] English | Learning crop models for vision-based guidance of agricultural robots[END_REF] or sensor-specific information [START_REF] Rovira-Mas | Fuzzy logic model for sensor fusion of machine vision and gps in autonomous navigation[END_REF]. In this work, we follow a data-driven monitoring paradigm [START_REF] Pettersson | Execution monitoring in robotics: A survey[END_REF], that is performing anomaly detection on a set of data without the use of explicit details or knowledge about the navigation algorithm.

This section provides context about the task to be monitored, i.e. structure-based navigation (which is an input not an element of the solution) and the considered paradigm considered for the monitoring, i.e. anomaly detection. First, we review structure tracking using Hough transform since it is considered as the data representation for the proposed monitoring approach. Then, we review the anomaly detection problem in the context of mobile robot navigation with a focus on one-classification methods.

a) Structure-based navigation and Hough transform: Local structure-based navigation is the task of tracking crop rows using local perception sensors (LiDAR scanners and cameras). The core of the solutions proposed in the literature is line fitting techniques. Hough transform [START_REF] Duda | Use of the hough transformation to detect lines and curves in pictures[END_REF] is one of the most common line fitting techniques employed for the task of identifying crop rows; it was applied to identify crop rows from vision data in [START_REF] Rovira-Más | Hough-transformbased vision algorithm for crop row detection of an automated agricultural vehicle[END_REF], [START_REF] Gée | Crop/weed discrimination in perspective agronomic images[END_REF] and from LiDAR data in [START_REF] Hamner | Improving orchard efficiency with autonomous utility vehicles[END_REF], [START_REF] Reiser | Crop row detection in maize for developing navigation algorithms under changing plant growth stages[END_REF], [START_REF] Winterhalter | Crop Row Detection on Tiny Plants With the Pattern Hough Transform[END_REF]. Hough transform maps input data from their feature space (image space or 3d metric space for LiDAR scans) to a parameter space called Hough space. Parametric models, e.g. a line, can be identified in the parameter space through a voting procedure. For the task of crop rows detection, the goal is to search for a pattern of parallel lines in the Hough space. To facilitate the search, additional hypotheses about the field have been made such as row spacing [START_REF] Åstrand | A vision based row-following system for agricultural field machinery[END_REF] or more generally exploiting row parallelism criterion [START_REF] Gée | Crop/weed discrimination in perspective agronomic images[END_REF], [START_REF] Jones | Modelling agronomic images for weed detection and comparison of crop/weed discrimination algorithm performance[END_REF].

The above mentioned hypotheses were made after observing invariants in the Hough representation of input data. Therefore, the presence of these invariants is conditioned to the presence of a parallel pattern in the perceived scene which is the base assumption of any structure tracking algorithm. The work presented in this paper is a generalization of this reflection: we assume that these invariants capture a normal criterion about the perceived structure that can be modeled through unsupervised representation learning without the need of explicit hypotheses. b) Anomaly detection: Anomaly detection is the task of identifying samples in data that do not conform to a well-defined notion of normal behavior [START_REF] Chandola | Anomaly detection: A survey[END_REF]. Depending on the task, the notion of normal behavior can be defined by gathering anomaly-free data. In this context, an anomaly detection algorithm consists of three major steps: first, modeling an anomaly-free background model from a representative dataset. Then, defining an anomaly score as a distance or a measure of deviation from the background model. Finally, defining an appropriate decision rule based on the anomaly score for the final binary decision (anomaly or not). This formalization was synthesized in a recent survey on image anomalies [START_REF] Ehret | Image anomalies: A review and synthesis of detection methods[END_REF] where anomaly detection methods were classified based on the assumptions made in each of the three aforementioned steps. An illustration of a generalized anomaly detection pipeline is provided in Figure 1.

Background model

Decision rule Data

Anomaly score Decision Fig. 1: The anomaly detection pipeline considered in this work.

For applications in mobile robot navigation, a semisupervised anomaly detection approach is often considered. In such an approach, the background model is learned from a set of normal data only. This makes semi-supervised anomaly detection a practical solution since it avoids gathering data representing all the anomalies that can occur which can be a dangerous and costly process. This approach was applied for traversability assessment [START_REF] Hirose | Gonet: A semi-supervised deep learning approach for traversability estimation[END_REF] and rare obstacle detection in outdoor environments [START_REF] Wellhausen | Safe robot navigation via multi-modal anomaly detection[END_REF] where normal data were fairly accessible and were gathered from a safe execution of the navigation task.

Semi-supervised anomaly detection is regarded as oneclass classification. One-class Support-Vector Machine (OC-SVM) [START_REF] Schölkopf | Estimating the support of a high-dimensional distribution[END_REF] is a classical method that defines the background model as a domain in feature space where normal data lie. OC-SVM does not scale well for high dimensional data (such as images) and large training sets. [START_REF] Ruff | Deep one-class classification[END_REF] introduced a deep one-class classification method called Deep Support Vector Data Description (Deep SVDD). Deep SVDD is an adaptation of the domain-based training criterion to deep learning that jointly learns to extract features that are enclosed in a hypersphere of minimal volume in feature space.

This work adopts a semi-supervised anomaly detection approach with deep SVDD to monitor the normal execution of the task of structure-based navigation in agriculture. Hough transform is considered as the representation of input data.

The assumption is that a background model learned from Hough space captures specific features defining a normal criterion of the structure.

III. MATERIALS AND METHODS

The monitoring method presented in this paper is developed as a part of the navigation solution for Trektor 2 , a multi-purpose agricultural robot developed by SITIA. The robot, shown in two vineyard configurations in Figure 2, is designed to intervene on different crops with their variety of features; ranging from tiny plants (e.g. market gardening) to dense small trees (e.g. viticulture). A parallel structure is common for all of these fields whether it is provided by crop rows or man-made structures (e.g wheel tracks). Thus, it is worthy to study the integration of a structure tracking algorithm in the overall navigation solution. This section explains the motivation and development of the anomaly detection method to monitor an existing structure tracking algorithm.

A. Navigation framework

One of the main applications of our robotic platform is autonomous navigation in vineyards. The task requires global planning in order to cover the entire field: i.e. a global GNSS path defining the rows that need to be worked and in what direction and order. Hence, the robot is equipped with an accurate global navigation system based on the fusion of RTK-GNSS and high accuracy inertial navigation systems. Leveraging this accuracy in practice requires the global path to be in high resolution which requires a significant effort in the task preparation phase. Hence comes a compromise with local perception that significantly reduces the resolution requirements of the global path by providing accurate tracking with respect to the perceived crop rows. In this setting, only the start and end points of the desired rows need to be georeferenced in order to create the global path that handles the transition between rows while leaving accurate row tracking for the local perception algorithm.

Figure 4 provides an illustration of the navigation task in a vineyard. The local perception for this task is provided by a 16-channel 3D LiDAR sensor (Ouster OS1-16) mounted on top of the robot, as shown in Figure 3, which allows the perception of multiple rows. As the robot approaches the end of a row, a less portion of the rows is perceived by the LiDAR sensor making its tracking less reliable; until a location where no structure information is available (noted as a red cross in Figure 4), here the transition to global navigation should be 2 www.sitia.fr/en/innovation-2/trektor/ triggered in order to handle the U-turn and enter the next row. This is one of the main scenarios that motivate a datadriven monitoring of the structure tracking algorithm. By doing so, a perception-based algorithm becomes more viable by providing an indicator of its reliability.

B. Monitoring via anomaly detection

The goal is to provide an indicator of the reliability of a structure tracking algorithm based solely on its input. This can be achieved following an anomaly detection paradigm (Figure 1) with three major elements: background model, anomaly score and decision rule. In this setting, the learned background model will provide an anomaly score reflecting a deviation of the inputs from its domain of reliable execution. Learning a background model is followed by an empirical evaluation of the produced anomaly score's statistics on a validation dataset with the goal of deriving the final decision rule.

As it has been stated in section II, the background model is conditioned to a notion of normal behavior that is defined by a set of data. Therefore, it is essential to carefully consider beforehand an assumption about the definition of the desired normal behavior and how to represent it with a dataset since the background model will learn patterns that illustrate this assumption. The following subsections describe the different elements of the proposed solution.

1) Data representation: The task to be monitored is the structure tracking algorithm. This algorithm applies pattern analysis on the Hough representation of point clouds in order to identify a pattern of parallel lines. Hough transform maps input points from their 2D metric space to the parameter space of the model to be identified; e.g. a line in the considered application. In details, Hough transform defines a line using the polar parameters (ρ, θ) where θ ∈ [0, π] is the normal angle and ρ ∈ R is the signed distance from the origin [START_REF] Duda | Use of the hough transformation to detect lines and curves in pictures[END_REF]. A point (x 0 , y 0 ) in the input point cloud maps to the sinusoid:

ρ = x 0 cosθ + y 0 sinθ (1)
in the polar parameter space that defines all the possible lines that pass through it. Thus, each point votes for the parameter of candidate lines. After considering all points, the Hough representation of the input cloud is a heatmap in the 2D parameter space where the coordinates of high heat define the most likely model candidates. Figure 5 shows a sample point cloud acquisition of a vineyard canopy along with the corresponding Hough transform. Four dense sinusoid intersections can be noticed in the Hough space. These intersections define the parameters of the four vine rows in the input cloud. This observation has been studied and it is exploited by most Hough-based crop rows detection methods [START_REF] Gée | Crop/weed discrimination in perspective agronomic images[END_REF], [START_REF] Winterhalter | Crop Row Detection on Tiny Plants With the Pattern Hough Transform[END_REF].

While the raw inputs of the structure tracking algorithm are point clouds, further preprocessing followed by the application of Hough transform are performed prior to pattern analysis. Hence, the Hough representation of an input point cloud is the direct input to pattern analysis. Since most crop rows detection algorithms exploit different properties in the Hough space in order to detect a pattern of parallel rows, we consider that Hough transform abstracts defining geometric features of crop row structures, and thus, it is considered as the representation of input data to the proposed anomaly detection method. This consideration is intended to reduce the capacity requirements of the subsequent background model by exploiting an already-computed feature extraction. This results in a light monitoring module that is compatible with a wide range of Hough-based tracking algorithms.

2) Defining an anomaly -Data collection: The aim is to learn a model of typical scene structures that characterizes a known and reliable structure tracking. With Hough transform as input representation, we collect structure data samples from previous safe navigation where we ensured that the field is suitable for structure tracking; meaning that no obstacles were present and the overall structure was visually consistent. We collected data from two different vineyards, each of which has its unique geometric characteristics (e.g., row spacing, height, weed pressure). The considered fields cover two vineyard configurations: narrow vineyard where the robot is centred on a vine row (Figure 2a) and wide vineyard where the robot passes between two vine rows (Figure 2b). This ensures that the collected data samples are diverse and cover the most likely scenarios that the robot will face.

The desired normal behavior is when the perceived scene allows the structure algorithm to provide pertinent results. Therefore, we define the normal behavior with samples collected in the in-row portion of the field (as opposed to the Uturn portion where no structure information is available). We used location information along with the global path (Figure 4) to annotate the collected data. The normal data zones were defined by shifting the geo-referenced end-points by 5 meters; this ensured that normal data do not include samples from the transition between structure tracking and U-turns as these samples contain little to no structure information.

Data samples are collected as 128×128 Hough images. Figure 6 shows examples of normal and anomalous data samples. The normal sample was collected in the in-row portion of the field and it shows clearly the sinusoid intersections pattern discussed in subsection III-B.1. Whereas the anomalous sample collected during a U-turn does not show this pattern, and, consequently, it does not contain structure information. The collected dataset is split into a training set that contains 14K normal samples, and a validation set that contains equally 4K of normal and anomalous samples. The aim of the balanced validation set is to monitor the evolution of the learned model using binary classification metrics such as the area under the receiver operating characteristic curve (ROC-AUC). The validation set will also be used to derive statistics about the learned model that are useful to define a decision rule in the consequent step of the anomaly detection pipeline.

3) Background model: After collecting training data, the next step in the anomaly detection pipeline (Figure 1) is learning a background model that captures the notion of normal behavior. We employ the background model as a oneclass classifier to be learned from the set of normal Hough images. The considered classifier follows the approach proposed by Ruff et al. [START_REF] Ruff | Deep one-class classification[END_REF]. In this approach, the one-class classifier is considered to be a neural network φ(.; ω) with weights ω that transforms input images to feature vectors. The model is trained to produce feature vectors that are enclosed in a hypersphere of minimal volume in the feature space. The volume of the hypersphere is jointly minimized along with learning the neural transformation weights ω. We adopt the One-Class Deep SVDD training loss proposed in the original paper [START_REF] Ruff | Deep one-class classification[END_REF]:

L = φ(X; ω) -c 2 + λ ω 2 ( 2
)
where X is an input image, c is the hypersphere center and ω represents the network weights. The second term in the training loss is a regularizer on network weights which is controlled by a hyperparameter λ. This training criterion forces the learned feature vectors of normal samples to be enclosed in a hypersphere of minimal volume. The hypersphere center c is set arbitrarily from an initial forward pass of the untrained network and it is fixed during training. This initialization technique follows the recommendation of Ruff et al. [START_REF] Ruff | Deep one-class classification[END_REF] to choose an arbitrarily center c = 0 in order to avoid trivial solutions (in which, the network weights collapse to zero). The proposed method is designed to monitor a real-time navigation application. Moreover, the representation of input clouds as Hough images reduces the capacity requirements of the background model φ(.; ω). These reasons motivate the choice of a light convolutional network. To this end, we propose a light fully convolutional network architecture with seven downsampling convolutional blocks. The proposed architecture is illustrated in Figure 7; it produces a 1×1×128 feature vector as an output for a 128 × 128 × 1 input image. Each downsampling block consists of a convolutional layer followed by batch normalization and leaky ReLU activation function (with leakiness α = 0.2). All the layers are defined with no bias term since it was shown in [START_REF] Ruff | Deep one-class classification[END_REF] that networks with bias term collapse to a constant function mapping when trained to minimize the deep SVDD loss.

4) Anomaly score: As an anomaly score for the model, we use the distance φ ω (X test )c 2 between the transformed features of a test sample X test and the hypersphere center c of the learned model. Since the deep SVDD training criterion forces the learned feature vectors of normal samples to be enclosed in a hypersphere, anomalous samples will be mapped to a location in the feature space that is at a larger distance from the hypersphere center than the mapping of normal samples. Fig. 7: The light fully convolutional network architecture adopted for the background model.

5) Decision rule:

After training, the learned background model will produce a raw anomaly score whose scale is not easily interpreted. This motivates the derivation of a decision rule, the final part of the anomaly detection pipeline. The decision rule aims to interpret raw anomaly scores as meaningful scalars that meet the requirements of the considered application. Hence, the decision rule is application specific. In the following, we explain the derivation of a decision rule for the application of monitoring the LiDARbased structure tracking algorithm; allowing its integration, along with GNSS-based navigation, in the hybrid navigation setup introduced in Figure 4.

In practice, each of the two navigation algorithms eventually guide the robot by delivering tracking signals to a control law as lateral deviation ∆l and angular deviation ∆γ. The anomaly score produced by the learned background model can be used to define a fusion rule of the tracking signals by attributing a confidence measure δ ∈ [0, 1] to the structure algorithm. Hence, the fusion rule for the lateral deviation is:

∆l = δ∆l LiDAR + (1 -δ)∆l GNSS (3)
and the same applies for the angular deviation. A confidence measure of 1 means that the tracking relies solely on the LiDAR-based algorithm. This is the desired case when the robot is between crop rows; the opposite is true when the robot is in the overhead performing U-turn where the tracking should rely solely on the GNSS. Therefore, the confidence measure is an inverse of the notion of anomaly assumed by the proposed anomaly detection algorithm. To this end, the decision rule should define the transformation of raw anomaly scores into the desired confidence measure δ.

A simple, yet effective, decision rule consists of defining a suitable threshold th for the anomaly score. This can be achieved empirically from the ROC curve evaluation of the learned background model on the validation set. However, this rule results in hard transitions between the final binary decision for samples with an anomaly score varying around the threshold, which is not suitable for the considered application. To overcome the hard transitioning issue, we propose a soft decision rule as a generalization of binary thresholding. The desired output is a normalized anomaly score s ∈ [0, 1] where the value of s for samples with an anomaly score near the threshold th should be around 0.5. Figure 8 illustrates the definition of the soft decision rule that takes into account the standard-deviations σ n and σ a of the distribution3 of raw anomaly scores on normal and anomalous validation samples The soft decision rule is a saturation function, the limits of saturation and the function slopes are defined empirically using the standarddeviations σ n and σ a . Specifically, the saturation limits are set to th-0.5σ n and th+0.5σ a respectively and the function slope is set to 1 σn for s < th and to 1 σa for s > th. Finally, the confidence measure δ can be defined as 1s where s ∈ [0, 1] is the normalized anomaly score produced by the soft decision rule.

IV. EXPERIMENTAL EVALUATION

As a case study, we consider evaluating the proposed monitoring approach in the hybrid navigation setup, introduced in Figure 4, that combines LiDAR-based structure tracking along with GNSS-based navigation. The goal is to deploy the proposed anomaly detection method to handle the transition between the two navigation algorithms based on the assessment of the Hough representation of input point clouds. To this end, the background model was trained on the collected dataset of normal Hough images and validated on the validation set using ROC-AUC binary classification metric.

The fully convolutional network (Section III-B.3) was trained from randomly initialized weights to minimize the deep SVDD training loss using the Adam optimizer with a learning rate of 10 -5 . The training was implemented in Tensorflow and we trained for 100 epochs. shows the results of the training as the evolution of the training loss and the evaluation of the ROC-AUC metric on the validation set per epoch. The ROC curve is obtained by evaluating the true positive and false positive rates for different thresholds on the anomaly score; an anomaly score above a given threshold means that the sample is classified as an anomaly (i.e. positive in the notation of binary classification). The network achieved a final AUC of 0.988 on the validation dataset, this value indicates that the validation samples are highly separable (between normal and anomalous) thanks to the anomaly score of the learned model. Raw anomaly scores, on the validation set, of the learned background model were used to derive the decision rule as discussed in Section III-B.5. Finally, the complete anomaly detection method delivered the desired monitoring via the confidence measure δ that defines the fusion rule of Equation 3.

We tested this confidence measure during the navigation on a test vine row that was not covered by the training nor the validation datasets. The background model was deployed to provide estimations in real-time (the model ran at the same 10 Hz frequency as the LiDAR sensor). The localization module of the robot provided a reference for the evaluation as a flag indicating when the robot enters the row. Figure 10 shows the results of this evaluation as the evolution of the confidence measure, the in-row flag and the raw anomaly score during the navigation. Before entering the row, the LiDAR perceived a crop row structure that is relatively far ahead, this resulted in a zero confidence measure that gradually increased as the robot approached the row and perceived a more complete structure that complies with the learned background model. The confidence measure was then saturated at 1 a few seconds before the in-row flag, this is logical since the in-row flag is based on the position of the robot's center knowing that the LiDAR sensor is mounted at the front meaning that it will perceive a complete structure before entering the row. The same logic applies near the rowend where the confidence measure transitions before the inrow flag; since at this position, structure perception begins to be incomplete. The figure also shows the evolution of the raw anomaly score (scaled up for visualization) which is consistent with the previous explanation and shows that the learned anomaly detection method performs well in real experimental scenarios allowing reliable integration of a structure-based navigation algorithm. V. CONCLUSION This paper presented a data-driven monitoring approach for the task of structure-based robot navigation in agricultural fields. The proposed method follows a semi-supervised anomaly detection paradigm in order to provide a quantitative assessment of the reliability of a LiDAR-based structure tracking algorithm. Hough transform was shown to be an effective abstraction of the field's geometry, from which a light fully convolutional background model capturing a normal criterion about the structure was learned. The background model was trained on a dataset of normal Hough images collected during previous safe navigation in vineyard fields.

Field experimentations considered the case of hybrid robot navigation in vineyards. The learned background model was used to derive a fusion rule that handles the transition between the structure-based and GNSS-based algorithms. A decision rule was derived empirically by considering the statistics of anomaly scores produced by the background model on a validation dataset. The overall anomaly detection method was validated in a real navigation scenario where it was shown that it defines guidelines for the integration of a perception-based navigation algorithm as a viable part of a complete autonomous navigation solution.

Future work would consider studying the transfer of the proposed anomaly detection algorithm to different agricultural structures such as wheel tracks for market gardening. Another long-term goal is to consider modeling a more complex definition of anomalies that includes obstacles. This requires the investigation of an anomaly detection approach on different input modalities such as images, which motivates background modeling with generative adversarial networks.

  (a) Narrow vineyard. (b) Wide vineyard.

Fig. 2 :

 2 Fig. 2: SITIA's Trektor: the robotic platform used in the experimentation.

Fig. 3 :

 3 Fig. 3: LiDAR-sensor's position on top of the robot.

  (a) Sample input point cloud. (b) Corresponding Hough space.

Fig. 5 :

 5 Fig. 5: A sample input point cloud and the corresponding Hough space.

  (a) Normal sample. (b) Anomalous sample.

Fig. 6 :

 6 Fig. 6: Hough space for normal and anomalous samples from the collected dataset.

Fig. 8 :

 8 Fig. 8: Deriving an anomaly decision rule empirically. The rule is a saturation function that transforms a raw anomaly score into a meaningful decision metric s ∈ [0, 1]. Saturation limits and the function slopes depend on the standarddeviations σ n and σ a of raw anomaly scores on normal (blue distribution) and anomalous (red distribution) validation samples.

Fig. 9 :

 9 Fig. 9: The evolution of the SVDD loss on the training set and the ROC-AUC metric on the validation set during 100 training epochs.

Fig. 10 :

 10 Fig. 10: The results of the evaluation in a hybrid navigation scenario as the evolution of the raw anomaly score (scaled up for visualization), the derived confidence measure of the LiDAR-based algorithm and the in-row flag provided by the GNSS system during the navigation on a vine row. The produced confidence measure saturates a few seconds before entering and exiting the row which correlates with positioning of the LiDAR sensor on the robot.

ACKNOWLEDGMENT

  This work is partially supported by the Agence Nationale de la Recherche ANR through the ROSE challenge, ANR-17-ROSE-0002-01 and by the Association Nationale de la Recherche et de la Technologie ANRT through the CIFRE PhD, ANRT CIFRE N • 2018/0792. This work was performed in part on computing resources provided by CRIANN (Centre Régional Informatique et d'Applications Numériques de Normandie, Normandy, France)

Throughout the paper, normal means not anomalous.

for illustration, gaussian distributions are assumed which is not necessary the case in practice, Nevertheless, this assumption holds true since we only use standard-deviations from empirical evaluation


Eléa Crayol

Frédéric Huneau

Emilie Garel

Viviana Re

Alexandra Mattei

Sébastien Santoni

Vanina Pasqualini

Schist formations Alluvial aquifer Sand bar Biguglia Lagoon Sea Seawater wedge Pesticides Emerging compounds NO 3 -Land use Infiltration Groundwater flow direction

Socio-hydrogeological approach for the identification of polluant fluxes towards Mediterranean lagoon hydrosystems

Coastal Mediterranean lagoons are at the interface between continental and marine environments, they also represent the ultimate collector of hydrological and hydrogeological watersheds.

They are fragile ecosystems and usually groundwater dependent. Coastal hydrosystems are threatened by increasing anthropogenic pressures inducing polluant fluxes within the watershed, damaging water bodies quality.

However coastal Mediterranean lagoons hydrogeological functioning is poorly investigated, damaging their management. Socio-hydrogeology allows, in an inter-and transdisciplinary way to clarify the often complex relationships linking the processes behind pollutant fluxes within the hydrogeological watershed and their impact on the lagoon's water quality. This approach integrates both social and economic components into hydrogeological investigations.

Figure 3 :

 3 Figure 3 : Excerpts from the questionnaire

Figure 1 :

 1 Figure 1 : Aerian view of the Biguglia lagoon watershed

Figure 2 :

 2 Figure 2 : The Biguglia hydrosystem and pollution fluxes


Pascale Blond

Robin Bevernaegie

Ludovic Troian-Gautier

Corinne Lagrost

Julie Hubert

François Reniers

Vincent Raussens

Ivan Jabin

Ready-to-use germanium surfaces for the development of FTIR-based biosensors for proteins

Keywords: Biosensor, Germanium, Calixarenes, FTIR spectroscopy, Fluorescence microscopy

Germanium is particularly suitable for the design of FTIR-based biosensors for proteins. The grafting of stable and thin organic layers on germanium surfaces remains however challenging. To tackle this problem, we developed a calix[4]arene-tetradiazonium salt decorated with four oligoethylene glycol chains and a terminal reactive carboxylic group. This versatile molecular platform was covalently grafted on germanium surfaces to yield robust ready-to-use surfaces for biosensing applications. The grafted calixarene monolayer prevents nonspecific adsorption of proteins while allowing bioconjugation with biomolecules such as Bovine Serum Albumin (BSA) or biotin. It is shown that the native form of the investigated proteins was maintained upon immobilization. As a proof of concept, the resulting calix[4]arene-based germanium biosensors were used through a combination of ATR-FTIR spectroscopy and fluorescence microscopy for the selective detection of streptavidin from a complex medium. This study opens real possibilities for the development of sensitive and selective FTIR-based biosensors devoted to the detection of proteins.

INTRODUCTION

Easy-to-use (bio)sensors are highly sought-after systems in a large number of fields that include medicine, food safety and environment. [1][2][START_REF] Kirsch | Biosensor technology: recent advances in threat agent detection and medicine[END_REF] They enable the detection and sometimes the quantification of an analyte in a complex medium, based on its affinity for receptors immobilized on the sensor. Detection is performed through classical analytical methods including various spectroscopic techniques. In the specific context of proteins detection, Fourier transform infrared (FTIR) spectroscopy is particularly well adapted, notably for identifying the secondary and tertiary structures of the target protein. [4][START_REF] Goormaghtigh | Determination of Soluble and Membrane Protein Structure by Fourier Transform Infrared Spectroscopy[END_REF][START_REF] Goormaghtigh | Protein secondary structure content in solution, films and tissues: Redundancy and complementarity of the information content in circular dichroism, transmission and ATR FTIR spectra[END_REF][START_REF] Goormaghtigh | Evaluation of the Information Content in Infrared Spectra for Protein Secondary Structure Determination[END_REF][START_REF] Kleiren | Development of a quantitative and conformation-sensitive ATR-FTIR biosensor for Alzheimer's disease: The effect of deuteration on the detection of the AB peptide[END_REF] In addition, a multitude of biophysical and chemical information can be obtained from the spectral signature of proteins (e.g. post-translational modifications). Attenuated total reflectance (ATR) FTIR-based biosensors make use of an organic layer, directly grafted onto the internal reflection element (IRE), to which a biological receptor is bound. [START_REF] Kleiren | Development of a quantitative and conformation-sensitive ATR-FTIR biosensor for Alzheimer's disease: The effect of deuteration on the detection of the AB peptide[END_REF][START_REF] Gosselin | Designing a high performance, stable spectroscopic biosensor for the binding of large and small molecules[END_REF][START_REF] Voue | Biochemical interaction analysis on ATR devices: a wet chemistry approach for surface functionalization[END_REF][START_REF] Devouge | Surface functionalization of germanium ATR devices for use in FTIR-biosensors[END_REF][START_REF] Goldzstein | Ligand-receptor interactions in complex media: A new type of biosensors for the detection of coagulation factor VIII[END_REF][13][START_REF] Schartner | Highly stable protein immobilization via maleimido-thiol chemistry to monitor enzymatic activity[END_REF] Common infrared-transparent materials for the IRE include semiconductors such as silicon and germanium. Germanium exhibits a high refractive index and a broader spectral window, [START_REF] Voue | Biochemical interaction analysis on ATR devices: a wet chemistry approach for surface functionalization[END_REF] which makes it particularly suitable for ATR analysis of proteins. However, due to the unstable nature of their surface oxydes, [START_REF] Amy | Passivation and Characterization of Germanium Surfaces[END_REF] only a few methods involving silanes or thiols have been developed for the grafting of organic layers on germanium substrates. [START_REF] Voue | Biochemical interaction analysis on ATR devices: a wet chemistry approach for surface functionalization[END_REF][START_REF] Devouge | Surface functionalization of germanium ATR devices for use in FTIR-biosensors[END_REF][START_REF] Goldzstein | Ligand-receptor interactions in complex media: A new type of biosensors for the detection of coagulation factor VIII[END_REF][START_REF] Schartner | Immobilization of proteins in their physiological active state at functionalized thiol monolayers on ATR-germanium crystals[END_REF][START_REF] Collins | Germanium Oxide Removal by Citric Acid and Thiol Passivation from Citric Acid-Terminated Ge(100)[END_REF] Unfortunately, these methods require laborious preliminary treatments of the germanium surfaces (i.e. oxidation and passivation) and lead usually to a weak reproducibility. Moreover, once modified, the surfaces present a poor stability, limiting their use in the field of biosensing. Another strategy relies on the reductive grafting of aryldiazonium salts, which leads to a strong and durable attachment of an organic layer onto the germanium surface. [START_REF] Girard | SiGe derivatization by spontaneous reduction of aryl diazonium salts[END_REF][START_REF] Collins | Organic Functionalization of Germanium Nanowires using Arenediazonium Salts[END_REF] However, due to the high reactivity of the radicals produced upon the reduction of diazonium cations, disordered multilayers are generally obtained through this method. The preparation of well-ordered monolayers with diazonium chemistry remains thus highly challenging, [START_REF] Combellas | Steric Effects in the Reaction of Aryl Radicals on Surfaces[END_REF][START_REF] Breton | Controlling Grafting from Aryldiazonium Salts: A Review of Methods for the Preparation of Monolayers[END_REF][START_REF] Malmos | Using a Hydrazone-Protected Benzenediazonium Salt to Introduce a Near-Monolayer of Benzaldehyde on Glassy Carbon Surfaces[END_REF][START_REF] Nielsen | Electrochemical Approach for Constructing a Monolayer of Thiophenolates from Grafted Multilayers of Diaryl Disulfides[END_REF][START_REF] Leroux | Efficient Covalent Modification of a Carbon Surface: Use of a Silyl Protecting Group To Form an Active Monolayer[END_REF] though this is a key point when immobilizing receptors in order to warrant an efficient and sensitive sensing. [START_REF] Liu | Diazonium salts: Stable monolayers on gold electrodes for sensing applications[END_REF][START_REF] Jiang | Aryldiazonium salt derived mixed organic layers: From surface chemistry to their applications[END_REF] In this context, we have recently developed a general strategy for surface modification, which consists in the covalent grafting of molecular platforms based on calix [4]arene-tetradiazonium salts. [START_REF] Mattiuzzi | Electrografting of calix[4]arenediazonium salts to form versatile robust platforms for spatially controlled surface functionalization[END_REF][START_REF] Troian-Gautier | Extremely robust and postfunctionalizable gold nanoparticles coated with calix[4]arenes via metal-carbon bonds[END_REF][START_REF] Troian-Gautier | Controlled Modification of Polymer Surfaces through Grafting of Calix[4arene-Tetradiazoate Salts[END_REF][START_REF] Troian-Gautier | Use of calixarenes bearing diazonium groups for the development of robust monolayers with unique tailored properties[END_REF][START_REF] Blond | Grafting of Oligo(ethylene glycol)-Functionalized Calix[END_REF] As demonstrated on various materials or nanomaterials, the unique macrocyclic structure of calix [4]arenes enables the formation of robust, dense and compact monolayers of controlled composition. [START_REF] Santos | One-Pot Electrografting of Mixed Monolayers with Controlled Composition[END_REF][START_REF] Valkenier | Controlled Functionalization of Gold Nanoparticles with Mixtures of Calix[4]arenes Revealed by Infrared Spectroscopy[END_REF] Based on this expertise, we have recently reported that a thin (ca. 3 nm) and robust coating of calix [4]arenes bearing oligo(ethylene glycol) (oEG) chains can be obtained on germanium IRE (Figure 1a). [START_REF] Blond | Grafting of Oligo(ethylene glycol)-Functionalized Calix[END_REF] It was shown that such oEGylated calix [4]arene-based coatings reduce nonspecific adsorption of Bovine Serum Albumin (BSA) by more than 85%. To the best of our knowledge, this was the first study to show such a significant reduction of nonspecific adsorption through the use of a stable and extremely thin coating of organic molecules. The thickness parameter is of crucial importance to ensure optimal IR signal, as sensitivity decreases exponentially with distance from the IRE. Hence, the calixarene-based methodology appears very promising for the preparation of ready-to-use germanium surfaces that can serve to the design of ATR-FTIR-based biosensors.

Herein, we describe the synthesis and incorporation in Ge-based biosensors of a novel calix [4]arene-tetradiazonium salt bearing four oEG chains. Importantly, one of the oEG chains is equipped with a terminal reactive carboxylic acid group for further post-functionalization. After grafting on germanium surfaces, this polyfunctional platform displayed antifouling properties while allowing the bioconjugation of biomolecules under classical conditions such as peptide type coupling (Figure 1b). Besides, with these versatile calixarenes, the ratio of functionalized/non functionalized oEG chains is controlled within the molecular platform itself, avoiding the formation of binary mixed layers, which is particularly challenging. [START_REF] Jiang | Aryldiazonium salt derived mixed organic layers: From surface chemistry to their applications[END_REF][START_REF] Retout | Limits of thiol chemistry revealed by quantitative analysis of mixed layers of thiolated-PEG ligands grafted onto gold nanoparticles[END_REF] The usefulness of this approach for the design of FTIR-based ready-to-use germanium biosensors is illustrated by the specific detection of streptavidin (SA) in a medium containing other proteins. [4]arene-tetradiazonium salt 1. Synthesis of calix [4]arene-tetradiazonium salt 1 was achieved for the development of the targeted ready-to-use germanium surfaces (Scheme 1). The oEG chain bearing a carboxylic acid moiety is longer than the three others in order to facilitate bioconjugation of biomolecules after grafting on a surface. Firstly, p-t Bu-calix [4]arene was alkylated with bromo-tetra(ethylene glycol) monomethyl ether [START_REF] Yuan | Unsurpassed cage effect for the photolysis of dibenzyl ketones in water-soluble dendrimers[END_REF] 2 in the presence of Ba(OH)2/BaO. Under these conditions, which are known to lead to tris-Oalkylated derivatives, [START_REF] Lavendomme | Rational Strategies for the Selective Functionalization of Calixarenes[END_REF] calix [4]arene 3 was isolated in 89% yield. Introduction of the fourth arm was then considered through alkylation of the remaining phenolic group by a tosyl-hexa(ethylene glycol) derivative terminated by a protected carboxylic group. However, this synthetic route led to non-reproducible results due to the deprotection of the carboxylic group under the harsh basic reaction conditions, which hindered complete alkylation of calix [4]arene 3 (see the Supporting Information). Another strategy consisting in the use of the tosyl-hexa(ethylene glycol) derivative terminated by an acetal protecting group 4 was thus developed. Alkylation of calixarene 3 by 4 led to the corresponding tetraalkylated compound whose hydroxyl group was directly deprotected under acidic conditions (94 % overall yield from 3). Further alkylation of the hydroxyl group of 5 by t Bu-bromoacetate followed by ipso-nitration of the aromatic units led to the tetranitro derivative 6 in 51 % overall yield. Reduction of the corresponding nitro groups by tin(II) chloride afforded the corresponding tetraaniline 7 in 66 % yield. Finally, diazotation using NOBF4 in acetonitrile led to the desired tetradiazonium salt 1 in 94% yield. Compound 1 was unambiguously characterized by IR (ν(N2+) = 2268 cm -1 ) and NMR spectroscopy (see the Supporting Information). Scheme 1. Synthesis of calix [4]arene-tris-oEG4-mono-oEG6-acid-tetradiazonium salt 1. [4]arene-based layer on germanium. To achieve the grafting of the calixarene-based layer, we used a previously reported procedure that consists in generating the desired aryl radicals from the aryldiazonium groups under alkaline conditions. [START_REF] Troian-Gautier | Controlled Modification of Polymer Surfaces through Grafting of Calix[4arene-Tetradiazoate Salts[END_REF][START_REF] Blond | Grafting of Oligo(ethylene glycol)-Functionalized Calix[END_REF] All the grafting experiments were repeated at least three times to assess reproducibility (see the Supporting Information). In practice, the germanium substrates were immersed for 2 hours in an aqueous 0.1 M NaOH solution containing calixarene 1 (5 mM) (Scheme 2). The resulting different batches of germanium surfaces Ge-1 were then thoroughly washed with water and acetonitrile under sonication. Scheme 2. Preparation of ready-to-use calixarene-based germanium surfaces. 35 The different batches of Ge-1 were analyzed by ATR-FTIR spectroscopy and by ellipsometry to assess the grafting and to estimate the thickness of the organic layer. The IR absorbance spectrum of Ge-1 exhibit bands that are typical of the calixarene core and of the oEG chains, as shown by the comparison with the spectrum of calix [4]arene 7 (Figure 2) (see the Supporting Information for complete assignment of the spectrum). More specifically, the asymmetric COC stretching from the oEG chains around 1100 cm -1 is visible in both spectra. Also, other bands such as the symmetric COCAr stretching around 1050 cm -1 and the aromatic ring stretching around 1460 cm -1 unambiguously confirm the presence of calix [4]arene moieties on the Ge surface. [START_REF] Devouge | Surface functionalization of germanium ATR devices for use in FTIR-biosensors[END_REF][START_REF] Blond | Grafting of Oligo(ethylene glycol)-Functionalized Calix[END_REF][START_REF] Valkenier | Controlled Functionalization of Gold Nanoparticles with Mixtures of Calix[4]arenes Revealed by Infrared Spectroscopy[END_REF][START_REF] Silverstein | Spectrometric Identification of Organic Compounds[END_REF] It is noteworthy that the typical vibrational bands for carboxylate groups at ~1600 cm -1 and ~1420 cm -1 are too weak to be significantly distinguished, in accordance with the low number of carboxylate groups.

RESULTS AND DISCUSSION

Synthesis and characterization of calix

Covalent grafting of an oEGylated calix

A thickness value of 2.85 ± 0.80 nm was found by ellipsometric measurements. This value corresponds well to the estimated height of calixarene 1 (ca. 3.0 nm estimated from MM2 energy minimizations with ChemBio3D software). This is in agreement with previous results showing that the calixarene-based methodology leads to the formation of monolayers, i.e. through AFM scratching experiments or ellipsometry measurements on various substrates. [START_REF] Mattiuzzi | Electrografting of calix[4]arenediazonium salts to form versatile robust platforms for spatially controlled surface functionalization[END_REF][START_REF] Troian-Gautier | Controlled Modification of Polymer Surfaces through Grafting of Calix[4arene-Tetradiazoate Salts[END_REF][START_REF] Blond | Grafting of Oligo(ethylene glycol)-Functionalized Calix[END_REF] The grafting of calix [4]arene-tetradiazonium salt on germanium surfaces was further assessed through X-ray photoelectron spectroscopy (XPS) analyses. For that purpose, a CF3-containing calix [4]arene was used. as described in previous articles, [START_REF] Mattiuzzi | Electrografting of calix[4]arenediazonium salts to form versatile robust platforms for spatially controlled surface functionalization[END_REF][START_REF] Santos | One-Pot Electrografting of Mixed Monolayers with Controlled Composition[END_REF][START_REF] Troian-Gautier | Controlled Modification of Polymer Surfaces through Grafting of Calix[4arene-Tetradiazoate Salts[END_REF] Indeed, the CF3 groups provide a characteristic chemical tag for XPS studies. The survey scan exhibits photoelectron peaks and Ge-1 (red) and calix [4]arene 7 (blue). All spectra were normalized with respect to the intensity at 1459 cm -1 .

We can conclude from the combination of these IR and ellipsometric measurements that thin films of oEGylated calix [4]arenes decorated with a carboxylate group are grafted on germanium surfaces. The great robustness of the calixarene-coated surfaces is also demonstrated by their resistance to the aggressive washing treatments (i.e. organic solvent under sonication) (see the Supporting Information).

Post-functionalization of the ready-to-use calixarene-based germanium surfaces. Bovine

Serum Albumin (BSA) was used as a model protein to investigate the post-functionalization of the grafted calix [4]arene layer through peptide-like coupling reactions. First, the terminal carboxylate groups present at the surface of Ge-1 were converted to the corresponding activated esters using The resulting surfaces Ge-1-BSA were then thoroughly washed with ultrapure water under sonication. The bioconjugated BSA is characterized by an amide-I band at 1657 cm -1 , which is mainly associated with the C=O(NH) stretching vibrations, and an amide-II band at 1547 cm -1 , which results mainly from in-plane NH bending vibrations (Figure 3, green spectrum). It is noteworthy that an absorption maximum at 1657 cm -1 for the amide-I band corresponds predominantly to proteins whose major secondary structure is an α helix, [START_REF] Goormaghtigh | Determination of Soluble and Membrane Protein Structure by Fourier Transform Infrared Spectroscopy[END_REF] which is the native structure of BSA (Figure 3, black spectrum). Therefore, BSA maintains its native structure after grafting, indicating that the modified germanium support has a protein-friendly character. In a control experiment, a non-activated surface Ge-1 was exposed to the same experimental bioconjugation and washing conditions. No characteristic bands associated with BSA were observed when analyzing the resulting surface Ge-1-control (Figure 3 incubated with drops of 1 µL solution of 100 µg/mL streptavidin in 0.1 M PBS buffer at pH 7.4/ethylene glycol 1/1 v/v. Other incubation experiments were conducted under the same conditions except that BSA (100 µg/mL) was also added. After 3 hours of incubation, all the surfaces were rinsed with ultrapure water. In all cases, streptavidin was efficiently detected by the characteristic amide-I and amide-II bands at 1637 cm -1 and 1535 cm -1 , respectively (Figure 6).

The location of the absorption bands indicates that the streptavidin has a β-sheet structure, [START_REF] Goormaghtigh | Determination of Soluble and Membrane Protein Structure by Fourier Transform Infrared Spectroscopy[END_REF] which corresponds to its native conformation. Very interestingly, no contribution from BSA at 1657 cm - 1 can be detected in the spectra obtained from incubation experiments in presence of this protein.

This demonstrates the remarkable antifouling properties of the calixarene-coated germanium surfaces.

Altogether, these IR and fluorescence microscopy results indicate that the surface modification strategy based on the calix [4]arene-based platform allows on the one hand to immobilize receptors that retain their recognition properties and on the other hand to selectively detect a protein in a medium containing other proteins. Ge-1-biotin in either solutions of streptavidin (blue) or streptavidin/BSA (green). All spectra were normalized with respect to the intensity of the amide-I band.

CONCLUSIONS

The calixarene-based methodology allows a simple and robust functionalization of germanium surfaces. Newly synthesized oEGylated calix [4]arene-tetradiazonium salt 1 was used for the elaboration of ready-to-use surfaces for the immobilization of biomolecules while displaying antifouling properties. For that purpose, the small rim of the calix [4]arene platform was decorated with four oEG chains, one being equipped with a terminal reactive carboxylic group. Monolayers of calix [4]arenes constitute thus versatile molecular platforms that bring simultaneously multiple new properties to a surface. Importantly, the native form of the investigated proteins was maintained upon immobilization on germanium, as evidenced by IR spectroscopy. The oEGylated calixarene-coated germanium surfaces were used for the selective detection by FTIR spectroscopy and fluorescence microscopy of streptavidin in the presence of BSA. This study not only constitutes a proof of concept that FTIR sensing of proteins can be achieved by this approach but also highlights that other analytical techniques, such as fluorescence microscopy, are compatible with the calixarene-based methodology. In addition to qualitative analysis of protein's structures, IR spectroscopy allows quantitative analysis. Calibration models could be developed to quantify the number of receptors on the surface as well as the number of targets to be detected.

EXPERIMENTAL SECTION

Chemicals and Materials All solvents and reagents were at least of reagent grade quality and were purchased either from Alfa Aesar, Sigma-Aldrich, TCI, or Acros Organics. All reactions were performed under an inert atmosphere. Reactions were magnetically stirred and monitored by thin layer chromatography using Merck Kiesegel 60F254 plates. Flash chromatography was performed with silica gel 60 (particle size 35-70 μm) supplied by Merck. Anhydrous dimethylformamide (DMF) was obtained from Acros Organics. Anhydrous tetrahydrofuran (THF)

was obtained from distillation on Na/benzophenone. Anhydrous dichloromethane (DCM) was obtained from distillation on CaH2. Ultrapure water was obtained via a Millipore Milli-Q system (18.2 MΩ•cm). The fluorescent FITC-BSA (fluorescein isothiocyanate-bovine serum albumin conjugate) was purchased from Sigma-Aldrich as well as the BSA used for ATR-FTIR spectroscopy experiments. The fluorescent streptavidin-ATTO655 was purchased from ATTO-TEC and the streptavidin used for ATR-FTIR spectroscopy experiments was purchased from Alfa Aesar. Both polished germanium squares (10 × 10 × 0.5 mm) and germanium single-crystal triangular prisms (base 6.8 mm × 45 mm length and an internal incident angle of 45°) were purchased from ACM (France). Syntheses of compounds 2 and 4 were achieved as previously described [START_REF] Yuan | Unsurpassed cage effect for the photolysis of dibenzyl ketones in water-soluble dendrimers[END_REF][START_REF] Loiseau | Multigram Synthesis of Well-Defined Extended Bifunctional Polyethylene Glycol (PEG) Chains[END_REF] and are given in the Supporting Information. The detailed synthesis of compound 1 is given here.

Synthesis of calix[4]arene-tetra-t

Bu-tris-oEG4 3. Commercial p-t Bu-calix [4]arene (3.4 g, 5.2 mmol) was dissolved in dry DMF (80 mL). Anhydrous barium hydroxide (2.7 g, 15.8 mmol) and barium oxide (3.8 g, 24.8 mmol) were added, followed by compound 2 (10 g, 36.9 mmol). The reaction mixture was stirred at 75°C for 16 hours under inert atmosphere. After reaction, the mixture was brought to room temperature and 5 mL of H2O was added. The mixture was filtered, and the filtrate was evaporated under reduced pressure. The resulting oil was dissolved in DCM (100 mL) and washed three times with water (3 x 70 mL) The organic layer was filtered on WA and the filtrate was concentrated under reduced pressure. The crude oil was purified by flash chromatography on silica gel (EtOAc/MeOH = 100:0 to 97:3) to afford the desired compound 3 as a pale-yellow oil (5.7 g, 4.7 mmol, 89 % yield). 1

Synthesis of calix[4]arene-tetra-t

Bu-tris-oEG4-mono-oEG6-OH 5. Calix [4]arene-tris-oEG4 3

(1.00 g, 0.82 mmol) was dissolved in dry DMF (20 mL). NaH (60% dispersion in oil, 66 mg, 1.64 mmol) was added and the mixture was stirred at room temperature for 5 minutes under inert atmosphere. Then, compound 4 (0.85 g, 1.64 mmol) was added and the reaction mixture was stirred for 16 hours at 75°C under inert atmosphere. After reaction, the mixture was concentrated under reduced pressure. The resulting oil was dissolved in DCM (50 mL) and washed three times with water (3 x 50 mL). The organic layer was filtered on WA and the filtrate was concentrated under reduced pressure. The resulting oil was dissolved in MeOH (10 mL) and 1 mL of HCl (37%) was added. The reaction mixture was stirred at room temperature for 2 hours. After reaction, the mixture was concentrated under reduced pressure. The resulting oil was dissolved in DCM (25 mL) and washed three times with water (3 x 25 mL). The organic layer was filtered on WA and the filtrate was evaporated under reduced pressure to afford the desired compound 5 as a paleyellow oil (1.15 g, 7.75 mmol, 94 % yield). Chemical grafting of calixarene 1 on germanium surfaces. Germanium surfaces were first immersed in ultrapure water and then in ethanol. Sonication was applied each time for 5 minutes.

They were then rinsed with Et2O and dried under argon atmosphere. The surfaces were dipped in a 5 mM solution of the diazonium salt 1 in aqueous 0.1 M sodium hydroxide for 2 hours, without stirring in order to avoid any mechanical damage of the surface (a minimal volume of solution was used). Once the grafting was achieved, all the surfaces were thoroughly washed with ultrapure water and then acetonitrile. Sonication was applied each time for 5 minutes. The surfaces Ge-1

were then dried under argon flow. Fluorescence microscopy experiments were conducted on a Nikon Ti-Eclipse inverted microscope. A filter cube (Semrock) with a 628 nm excitation filter (bandpass 40 nm), a dichroic mirror centered at 660 nm and a 692 nm emission filter (bandpass 40 nm) was employed to match the spectral properties of ATTO 655. A filter cube (B-2A) with a 470 nm excitation filter (bandpass

Preparation of surfaces

Figure 1 .

 1 Figure 1. a) previous work on oEGylated calix[4]arene-based germanium surfaces displaying antifouling properties; 31 b) this work on ready-to-use germanium surfaces for the bioconjugation of biomolecules. 35

Figure 2 .

 2 Figure 2. ATR-FTIR absorption spectra (3100-2700 cm -1 and 1800-800 cm -1 ) of modified surface

Scheme 3 .

 3 Scheme 3. Post-functionalization of the calixarene-based layer with BSA (left) or biotin-oEG2-

1 Figure 3 .

 13 Figure 3. ATR-FTIR absorption spectra (1800-900 cm -1 ) of a) Ge-1-BSA (green) and b) Ge-1-

Figure 4 . 1 '

 41 Figure 4. Bright-field (left) and fluorescence images (right) acquired on germanium surface Ge-

Figure 5 .

 5 Figure 5. Bright-field (a) and fluorescence images (b and c) acquired on surface Ge-1-biotin

Figure 6 .

 6 Figure 6. ATR-FTIR absorption spectra (1800-900 cm -1 ) obtained after incubation of surfaces

Ge- 1 '

 1 . Surfaces Ge-1 were placed in a sealed tube and covered with 80 mM EDC solution in 0.1 M MES buffer at pH 6.0. An equal amount of a 200 mM NHS solution in 0.1 M MES buffer at pH 6.0 was added, giving rise to a final concentration of 40 mM EDC and 100 mM NHS in MES buffer (0.1 M). The mixture was allowed to react for 2 hours under argon atmosphere. The surfaces were then washed with ultrapure water and dried under an argon flow affording surfaces Ge-1'.Preparation of the FITC-BSA microarrays for the fluorescence microscopy studies. The microarrays of fluorescent FITC-BSA were printed with an Arrayjet Marathon non-contact inkjet Microarrayer (ArrayJet, Roslin, UK) on germanium slides Ge-1' or Ge-1 (control experiment). In practice, drops of ca. 100 pL FITC-BSA solutions (0.5, 1, 5 and 10 mg/mL) in 0.1 M PBS buffer at pH 7.4/ethylene glycol 1/1 v/v were deposited to form regular arrays. Each sample was spotted 30 times in X direction (30 replicates). After 3 hours of incubation, the slides were rinsed with ultrapure water and dried under argon flow.Preparation of surfaces Ge-1-BSA and Ge-1-control. Surfaces Ge-1' or Ge-1 were incubated for 3 hours in a 0.1 mg/mL BSA solution in 0.1 M PBS buffer at pH 7.4. After reaction, the surfaces were thoroughly washed with ultrapure water under sonication. The surfaces were then dried under an argon flow affording surfaces either Ge-1-BSA (from Ge-1') or Ge-1-control from (Ge-1).Preparation of Ge-1-biotin spotted surfaces. The microarrays of biotin derivative were printed with an Arrayjet Marathon non-contact inkjet Microarrayer (ArrayJet, Roslin, UK) on germanium slides Ge-1'. In practice, drops of ca. 100 pL biotin-oEG2-NH2 solutions (10 mg/mL) in 0.1 M PBS buffer at pH 7.4/ethylene glycol 1/1 v/v were deposited to form regular arrays. Each sample was spotted 30 times in X direction (30 replicates). After 3 hours of incubation, the slides were rinsed with ultrapure water and dried under argon flow, affording Ge-1-biotin spotted surfaces.Preparation of surfacesGe-1-biotin. Surfaces Ge-1' were incubated for 3 hours in a 10 mg/mL biotin-oEG2-NH2 solution in 0.1 M PBS buffer at pH 7.4. The surfaces were then thoroughly washed with ultrapure water under sonication and dried under argon flow, affording surfaces Ge-1-biotin. Recognition of streptavidin. Before any detection experiment, surfaces Ge-1-biotin or Ge-1biotin spotted were first incubated in a PBS buffer solution at pH 9.3 in order to quench the eventual residual activated ester group. The biotinylated surfaces were then locally incubated under either conditions A or B. Conditions A. The Ge-1-biotin spotted surfaces were incubated with a drop of 0.5 µL of a solution containing 100 µg/mL of fluorescent streptavidin ATTO-655 and 100 µg/mL of fluorescent FITC-BSA in 0.1 M PBS buffer at pH 7.4/ethylene glycol 1/1 v/v. After 2 hours of incubation, the slides were rinsed with ultrapure water and then dried under argon flow. Conditions B. The surfaces Ge-1-biotin were incubated either with drops of 1 µL solution of 100 µg/mL streptavidin in 0.1 M PBS buffer at pH 7.4/ethylene glycol 1/1 v/v or with drops of 1 µL of solution containing 100 µg/mL of streptavidin and 100 µg/mL of BSA in 0.1 M PBS buffer at pH 7.4/ethylene glycol 1/1 v/v. After 3 hours of incubation, the surfaces were rinsed with ultrapure water and then dried under argon flow.ATR-FTIR spectra were recorded at 22°C on a Bruker Equinox 55 FTIR spectrophotometer equipped with a liquid nitrogen-cooled mercury-cadmium-telluride detector. The spectrophotometer was continuously purged with dried air. The target chemicals were deposited in solution on a germanium single-crystal internal reflection element (triangular prism of 6.8 × 45 mm, with an internal incidence angle of 45°), and the solvent was removed with a flow of nitrogen gas. Bare germanium was used for the background spectrum. Opus software (4.2.37) was used to record 128 scans with a nominal resolution of 4 cm -1 . Data were processed and analyzed using the home-written Kinetics package in Matlab R2007a by subtraction of water vapor, baseline correction, and apodization at 8 cm -1 . All spectra were equally normalized on the aromatic ring stretching of the calixarenes around 1460 cm -1 .

The authors would like to thank Joëlle De Meutter and Erik Goorgmaghtigh for performing the printing experiences with an Arrayjet Marathon non-contact inkjet Microarrayer (ArrayJet, Roslin, UK) on the germanium slides. The authors acknowledge Thomas Doneux for assistance with fluorescence microscopy experiments. The "Actions de Recherches Concertées" of the Fédération Wallonie-Bruxelles, the Bridge -Green Chemistry Program of Innoviris (postdoctoral grant to R.B.) and the ULB (Ph.D. grant to P.B.) are acknowledged for financial support. L. T.-G. is a postdoctoral researcher and V.R. is senior research associate of the Fonds de la Recherche Scientifique -FNRS.

40 nm), a dichroic mirror centered at 505 nm and a 515 nm emission filter (Longpass) was employed to match the spectral properties of FITC. The surface exposure time for image recording varies between 100 ms for bright field images and 5000 ms for fluorescence images. 8 scans are recorded for the fluorescence images for the detection of the ATTO-655 dye and for the other images a single scan is performed. The images were acquired with an Andor Clara camera controlled by the Solis software. A 10× magnification objective (NA = 0.30, working distance 16 mm) was employed, providing an imaging density of 1.7 pixels per µm. The images were processed with the ImageJ software.

ASSOCIATED CONTENT Supporting Information. The Supporting Information is available free of charge on the ACS Publications website at DOI: XXX. Supplementary IR Data, Fluorescence image and Variable Angle Spectroscopic Ellipsometric (VASE) Data. Detailed procedure for the preparation and characterization of the synthesized compounds, NMR spectra (PDF).

Conflicts of interest.

L. T.-G. was a postdoctoral researcher for X4C between October 2014 and September 2015. I. J. and C. L. are shareholders of X4C and I. J. is a consultant for X4C. All other authors declare that they have no conflict of interest.

Corresponding Authors

Ivan.Jabin@ulb.be, Vincent.Raussens@ulb.ac.be

ACKNOWLEDGMENT For TOC use only

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