Jean-Noël Paoli

Spatial data fusion for qualitative estimation of fuzzy request zones: Application on precision viticulture

This article deals with a method used to describe and manage spatial knowledge. Each spatial datum is considered as an information element, whose location and value are independently described. The method proposed uses the information elements to infer the value of any request zone. Data can be either qualitative or quantitative. Our approach is based on a fuzzy granulation of available items of information. The fusion of information elements is performed by an aggregation operator based on the Choquet integral, which analyzes the spatial relevancy of the information elements on the request zone. We also describe an application of the method to a precision viticulture data set.

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.

A technical opportunity index based on the fuzzy footprint of a machine for site-specific management: an application to viticulture

This paper describes a method that allows farmers to (i) decide whether or not the spatial variation of a field allows a reliable variable-rate application, (ii) discover if a particular threshold (field segmentation) based on within-field data is technically feasible according to the application machinery and (iii) make an appropriate application map. Our method aims to improve on a previous technical opportunity index (Oi) with a fuzzy technical opportunity index (FTOi). The FTOi considers (i) a fuzzy footprint model of a variable-rate application controller (VRAC), which describes the area within which the VRAC can operate reliably, (ii) the location inaccuracy of the data and (iii) the ability (accuracy) of the VRAC to perform distinct levels of treatments. The originality of our approach is based on the use of a fuzzy estimation process to decide if a level of treatment is reliable or not for each area over which the VRAC operates. A unique feature of the approach is that it does not require data on a regular grid. Only characteristics of the machinery and the treatment to be applied are necessary; interpolation of the data and geostatistics are not required by the end user. Tests on theoretical fields, obtained from a simulated annealing procedure, showed that the FTOi was able to assess the technical manageability of variation in the field. Tests also showed that our approach could take into account problems related to low resolution data. Finally, the approach has been applied to a real situation in a vineyard block. This has highlighted the practical implementation and the ability to generate useful information for managing the within-field variation (optimal thresholding, and application and error maps).

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).

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.