Sylvain Villette

A lumped water balance model to evaluate duration and intensity of drought constraints in forest stands

This paper presents a daily water balance model where the main aim is to quantify drought intensity and duration in forest stands. This model requires daily potential evapotranspiration and rainfall as input climatic data. Required site and stand parameters are only maximum extractable soil water and leaf area index, the latter controlling (i) stand transpiration; (ii) forest floor evapotranspiration; and (iii) rainfall interception. Other informations, like root distribution and soil porosity, can be used if available, improving the simulation of short term soil water recharge. Water stress is assumed to occur when relative extractable soil water (REW) drops below a threshold of 0.4 under which transpiration is gradually reduced due to stomatal closure. The model was calibrated using sap flow measurements of stand transpiration in oak and spruce stands during several successive dehydration–rehydration cycles. Validation of the model was performed by comparing predicted soil water content to weekly neutron probe measurements in various forest stands and climatic conditions. The model simulated accurately the dynamics of soil water depletion and recharge, and predicted the main components of forest water balance. Day-to-day estimates of soil water content during the growing season allows to quantify duration and intensity of drought events, and to compute stress indexes. A dendroecological application is presented: a retrospective analysis of the effects of drought on radial tree growth, based on long term climatic time series, is shown. Some limitations and potential applications of the model are discussed.

Simple imaging system to measure velocity and improve the quality of fertilizer spreading in agriculture

The management of mineral fertilization using centrifugal spreaders calls for the development of spread pattern characterization devices to improve the quality of fertilizer spreading. In order to predict spread pattern deposition using a ballistic flight model, several parameters need to be determined, in particular, the velocity of the granules when they leave the spinning disc. We demonstrate that a motion-blurred image acquired in the vicinity of the disc by a low-cost imaging system can provide the three-dimensional components of the outlet velocity of the particles. A binary image is first obtained using a recursive linear filter. Then an original method based on the Hough transform is developed to identify the particle trajectories and to measure their horizontal outlet angles, not only in the case of horizontal motion but also in the case of three-dimensional motion. The method combines a geometric approach and mechanical knowledge derived from spreading analysis. The outlet velocities are deduced from outlet angle measurements using kinematic relationships. Experimental results provide preliminary validations of the technique.

Optimizing hough transform for fertilizer spreading optical control

In Europe, centrifugal spreading is a widely used method for agricultural soil fertilization. In this broadcasting method, fertilizer particles fall onto a spinning disk, are accelerated by a vane, and afterward are ejected into the field. To predict and control the spread pattern, a low-cost, embeddable technology adapted to farm implements must be developed. We focus on obtaining the velocity and the direction of fertilizer granules when they begin their flight by means of a simple imaging system. We first show that the outlet angle of the vane is a bounded value and that its measurement provides the outlet velocity of the particle. Consequently, a simple camera unit is used in the vicinity of the spinning disk to acquire digital images on which trajectory streaks are recorded. Information is extracted using the Hough transform, which is specifically optimized to analyze these streaks and to measure the motion of the particles. The optimization takes into account prior mechanical knowledge and tackles the problem of Hough space quantization. The method is assessed on various simulated images and is used on real spreading images to characterize fertilizer particle trajectories.

Field radiometric calibration of a multispectral on-the-go sensor dedicated to the characterization of vineyard foliage

Development of a multispectral on-the-go system (visible and NIR) to characterize vineyard in natural light.Implementation of a radiometric method to calibrate the images using linear and spatial interpolation.Computation of vegetation index (NDVI) from reflectance images and strong correlation with Greenseeker data.Proximal multispectral imaging system can be used to assess foliage vigor at green berry stage. The accurate assessment of the vigor and disease impact is a major challenge in precision viticulture. It is essential for managing phytosanitary treatments. Up to now, some remote sensing techniques such as aerial imagery and handheld optical sensors have been applied to grapevine characterization. However each technique provides limited, specific information about foliage. To broaden the characterization of the foliage, we developed a proximal integrated, multispectral imaging sensor that operates in the visible and near-infrared bands. It is mounted on a track-laying tractor equipped with a Greenseeker-RT-100, coupled with a GPS-RTK. As the sensor is very sensitive to the ambient light, a radiometric calibration is required: it allows producing absolute reflectance images, using a color chart. If the chart is hidden by leaves, for instance, the images are corrected using the linear interpolation method. The adaptive radiometric method is evaluated as a function of the number of neutral patches selected on the color chart during the linear regression process and the efficiency of the spatial interpolation method is assessed using a leave-one-out-cross-validation (LOOCV) method.The radiometric calibration is validated by comparison of NDVI maps produced by imagery and by the Greenseeker, a commercial system. In the early stage of berry formation, we examined and quantified the spatial patterns and demonstrated a low-cost imagery method that is capable of analyzing correctly the vigor. This corroborates the efficiency of the calibration method encouraging the use of multi-spectral imagery for other vineyard applications, such as the characterization of physiological status.

Unsupervised Classification Algorithm for Early Weed Detection in Row-Crops by Combining Spatial and Spectral Information

In agriculture, reducing herbicide use is a challenge to reduce health and environmental risks while maintaining production yield and quality. Site-specific weed management is a promising way to reach this objective but requires efficient weed detection methods. In this paper, an automatic image processing has been developed to discriminate between crop and weed pixels combining spatial and spectral information extracted from four-band multispectral images. Image data was captured at 3 m above ground, with a camera (multiSPEC 4C, AIRINOV, Paris) mounted on a pole kept manually. For each image, the field of view was approximately 4 m × 3 m and the resolution was 6 mm/pix. The row crop arrangement was first used to discriminate between some crop and weed pixels depending on their location inside or outside of crop rows. Then, these pixels were used to automatically build the training dataset concerning the multispectral features of crop and weed pixel classes. For each image, a specific training dataset was used by a supervised classifier (Support Vector Machine) to classify pixels that cannot be correctly discriminated using only the initial spatial approach. Finally, inter-row pixels were classified as weed and in-row pixels were classified as crop or weed depending on their spectral characteristics. The method was assessed on 14 images captured on maize and sugar beet fields. The contribution of the spatial, spectral and combined information was studied with respect to the classification quality. Our results show the better ability of the spatial and spectral combination algorithm to detect weeds between and within crop rows. They demonstrate the improvement of the weed detection rate and the improvement of its robustness. On all images, the mean value of the weed detection rate was 89% for spatial and spectral combination method, 79% for spatial method, and 75% for spectral method. Moreover, our work shows that the plant in-line sowing can be used to design an automatic image processing and classification algorithm to detect weed without requiring any manual data selection and labelling. Since the method required crop row identification, the method is suitable for wide-row crops and high spatial resolution images (at least 6 mm/pix).

Hybrid centrifugal spreading model to study the fertiliser spatial distribution and its assessment using the transverse coefficient of variation

A virtual twin-disc spreader is modelled combining theoretical motion equations and statistical information.Statistical distributions of input parameters are deduced from experimental measurements.A Monte Carlo process reproduces the random variability of fertiliser spread pattern deposition.Simulations demonstrate the influence of the application rate and the collection tray size on transverse CV. Studying centrifugal spreading by carrying out field or in-door experiments using fertiliser collection trays is tedious and labour intensive. This is particularly true when several implementation methods need to be compared, numerous replications are required or fertiliser sample characterisation is required. To circumvent cumbersome experiments, an alternative approach consists in performing in silico studies. In order to reach this objective, a hybrid centrifugal spreading model is designed by combining theoretical fertiliser motion equations with statistical information. The use of experimental measurements to characterise fertiliser properties, outlet velocity, angular mass flow distribution and spread pattern deposition, ensure a realistic calibration of the model. Based on this model, static spread patterns and transverse distributions are computed for a virtual twin-disc spreader. The number of fertiliser granules used to compute a spread pattern is deduced from the target application rate while the granule properties and their motion parameters are randomly selected from pre-established statistical distributions. This Monte Carlo process reproduces the random variability of fertiliser spread pattern depositions. Using this model, simulations demonstrate the mean and standard deviation of CV value decrease with the application rate. The CV mean value also decreases with the collection tray surface, while the standard deviation decreases with the collection tray length. Mathematical relationships are deduced from simulation results to express the mean and standard deviation of the CV as functions of the application rate and collection tray surface or length. The simulation model is also used to compare spreader test methods and study the influence of some fertiliser particles properties on the transverse distribution.

« On-the-go » multispectral imaging system to characterize the development of vineyard foliage with quantitative and qualitative vegetation indices

Over the last years, the literature presents new technologies to optimize vineyard management. In the proximal sensing context, optical sensors are mainly developed to characterize the vegetation and the most famous one is the Greenseeker RT-100 (Trimble, Germany), that provides NDVI. The interpretation of its measurements is complex because it overlaps quantitative and qualitative information. However, it is a robust active sensor especially dedicated to characterize vineyard at early growth stage. To overcome these limits, we developed a multispectral (RGB, NIR) imaging system. We present a first application of spectral imagery, in proximal sensing conditions, to characterize the vine foliage of three grapevine varieties (Meunier, Pinot Noir and Chardonnay) at four phenological stages. The imaging system is embedded on a ground vehicle acquiring images with natural light, and an original radiometric calibration is proposed. From images, three agronomic indices (NDVIimage, NDVIvegetation and “foliage occupation”) are defined. They are computed from entire images and from the area of the grapes. These indices are compared to Greenseeker ones at the beginning of berry formation to be assessed. Whatever the grapevine variety the NDVIimage is in agreement with the index provided by Greenseeker (NDVIGS). At the other stages, the comparison of NDVIGS to the other indices leads to a new interpretation of NDVIGS depending on the phenological stage. The new indices provide a better understanding on the part of quantitative and quantitative information in Greenseeker index and lead to a more accurate leaf quantity estimation (from entire images), or specific physiological status characterization.

Validation of a crop field modeling to simulate agronomic images

In precision agriculture, crop/weed discrimination is often based on image analysis but though several algorithms using spatial information have been proposed, not any has been tested on relevant databases. A simple model that simulates virtual fields is developed to evaluate these algorithms. Virtual fields are made of crops, arranged according to agricultural practices and represented by simple patterns, and weeds that are spatially distributed using a statistical approach. Then, experimental devices using cameras are simulated with a pinhole model. Its ability to characterize the spatial reality is demonstrated through different pairs (real, virtual) of pictures. Two spatial descriptors (nearest neighbor method and Besags function) have been set up and tested to validate the spatial realism of the crop field model, comparing a real image to the homologous virtual one.

Velocity measurement using motion blurred images to improve the quality of fertiliser spreading in agriculture

The management of mineral fertilisation using centrifugal spreaders requires the development of spread pattern characterisation devices to improve the quality of fertiliser spreading. In order to predict the spread pattern deposition using a ballistic flight model, several parameters need to be determined and especially the velocity of the granules when they leave the spinning disc. This paper demonstrates that a motion blurred image acquired in the vicinity of the disc with a low cost imaging system can provide the three dimensional components of the outlet velocity of the particles. A binary image is first obtained using a recursive linear filter. Then an original method based on the Hough transform is developed to identify the particle trajectories and to measure their horizontal outlet angles, not only in the case of horizontal motion but also in the case of three dimensional motion. The method combines a geometric approach and mechanical knowledge derived from spreading analysis. The outlet velocities are deduced from the outlet angle measurements using kinematic relationships.

Mapping Vineyard Folliage Density with Multispectral Proxidection Imagery

This paper presents a new multispectral imaging system and a new approach to map vineyard leaf development in the context of Precision Viticulture. It is based on the use of a 2-CCD camera (RGB/NIR). This camera is embedded on a track laying tractor and associated with a GNSS system. It works with natural ambient light. A Green seeker is also embedded to validate the first results of imagery. It is a sensor usually used in viticulture to assess the foliage density of vines. The images are calibrated in order to correct geometric distortion and variations of the natural light. The parameters of the geometric correction are stable over time, whereas the radiometric correction needs to include a color checker in the background of all the images. For each of them, a calibration model is computed and reflectance of objects is obtained. The calibrated images can be compared with each other. During 2013, four datasets of about 5000 images were acquired. For all the images, a vegetation index is performed to retrieve agronomic information. Based on these images, different vineyard maps were generated with calibrated images, with non-calibrated images, and with Green seeker values. We present and discuss some of them in order to assess the relevancy of the approach.