Getting simultaneous red and near infrared bands from a single digital camera for plant monitoring applications

International audienceLes images multispectrales incluant une bande rouge et une bande infrarouge ont prouvé leur efficacité pour la discrimination entre sol et végétation et le suivi cultural en télédétection. Mais elles restent rarement utilisées pour l'imagerie au sol ou par drone, du fait de la non disponibilité de capteurs adaptés. Nous proposons ici une solution originale pour obtenir simultanément les bandes rouge et infrarouge à partir d'un appareil photographique couleur ordinaire, après avoir retiré le filtre interne bloquant l'infrarouge. Nous décrivons d'abord l'approche théorique, ainsi que des résultats simulés sur un jeu de données spectrales, pour deux types d'appareils. Des exemples d'acquisition sur le terrain en conditions réelles sont ensuite présentés, et comparés à une acquisition couleur standard pour la discrimination sol/plantes. Dans la plupart des cas notre approche apporte une amélioration significative, ouvrant de nouvelles opportunités pour les applications de suivi de culture. / Multispectral images including red and near-infrared bands have proved their efficiency for vegetation-soil discrimination and agricultural monitoring in remote sensing applications. But they remain rarely used in ground and UAV imagery, due to a limited availability of adequate 2D imaging devices. In this paper, we propose and evaluate an original solution to obtain simultaneously the near-infrared and red bands from a standard RGB camera, after having removed the near-infrared blocking filter inside. First, the theoretical approach is described, as well as simulated results on a set of soil and vegetation luminance spectra with two different still cameras (Canon 500D and Sigma SD14). Then examples of images obtained in real field conditions are given, and compared with standard colour image acquisition for pixel-based plant/soil discrimination, using an automatic thresholding method. It appears that in most cases our new acquisition procedure brings a significative improvement, opening new opportunities for crop monitoring applications

Getting simultaneous red and near-infrared band data from a single digital camera for plant monitoring applications: theoretical and practical study

Multispectral images, including red and near-infrared bands, have proved efficient for vegetation-soil discrimination and agricultural monitoring in remote-sensing applications. However, they remain little used in ground-based and unmanned aerial vehicle (UAV) imagery, due to a limited availability of adequate 2D imaging devices. A methodology is proposed to obtain simultaneously the near-infrared and red bands from a standard single RGB camera, after having removed the near-infrared blocking filter inside. Its ability to provide satisfactory NDVI (normalised difference vegetation index) computation for vegetation and soil has been assessed through spectral simulations. Application in field conditions with Canon 500 D and Canon 350D cameras has then been considered, taking into account signal-noise and demosaicing concerns. The results obtained have proved the practical usability of this approach, opening new technical possibilities for crop monitoring and agricultural robotics

An automatized frequency analysis for vine plot detection and delineation in remote sensing

La mise à disposition d'un outil automatique pour la détection et la caractérisation des parcelles de vigne est un besoin très important d'un point de vue gestion. Un procédé automatique récursif basé sur l'analyse fréquentielle (utilisation de la Transformée de Fourier et des filtres de Gabor) a été développé pour y répondre. Il permet la détermination des contours de parcelle et une estimation précise de leur inter-rang et de leur orientation. Dans l'optique d'une application à grande échelle, les tests et la validation ont été menés à partir de données standard de télédétection à très haute résolution.. Environ 89% des parcelles sont détectées qui correspondent à plus de 84 % de la surface viticole, et 64% d'entre elles avec des contours corrects. L'orientation des rangs et la largeur d'inter-rang sont obtenus avec une précision de 1 degré et 3,3 cm respectivement. / The availability of an automatic tool for vine plot detection, delineation, and characterization would be very useful for management purposes. An automatic and recursive process using frequency analysis (with Fourier transform and Gabor filters) has been developed to meet this need. This results in the determination of vine plot boundary and accurate estimation of interrow width and row orientation. To foster large-scale applications, tests and validation have been carried out on standard very high spatial resolution remotely sensed data. About 89% of vine plots are detected corresponding to more than 84% of vineyard area, and 64% of them have correct boundaries. Compared with precise on-screen measurements, vine row orientation and interrow width are estimated with an accuracy of 1°and 3.3 cm, respectively

From pixel to vine parcel: A complete methodology for vineyard delineation and characterization using remote-sensing data

International audienceThe increasing availability of Very High Spatial Resolution images enables accurate digital maps production as an aid for management in the agricultural domain. In this study we develop a comprehensive and automatic tool for vineyard detection, delineation and characterizationusing aerial images and without any parcel plan availability. In France, vineyard training methods in rows or grids generate periodic patterns which make frequency analysis a suitable approach. The proposed method computes a Fast Fourier Transform on an aerial image, providing the delineation of vineyards and the accurate evaluation of row orientation and interrow width. These characteristics are then used to extract individual vine rows, with the aim of detecting missing vine plants and characterizing cultural practices. Using the red channel of an aerial image, 90\% of the parcels have been detected; 92\% have been correctly classified according to their rate of missing vine plants and 81\% according to their cultural practice (weed control method). The automatic process developed can be easily integrated into the final user's Geographical Information System and produces useful information for vineyard managemen

Hyperspectral images segmentation: a proposal

Hyper-Spectral Imaging (HIS) also known as chemical or spectroscopic imaging is an emerging technique that combines imaging and spectroscopy to capture both spectral and spatial information from an object. Hyperspectral images are made up of contiguous wavebands in a given spectral band. These images provide information on the chemical make-up profile of objects, thus allowing the differentiation of objects of the same colour but which possess make-up profile. Yet, whatever the application field, most of the methods devoted to HIS processing conduct data analysis without taking into account spatial information.Pixels are processed individually, as an array of spectral data without any spatial structure. Standard classification approaches are thus widely used (k-means, fuzzy-c-means hierarchical classification...). Linear modelling methods such as Partial Least Square analysis (PLS) or non linear approaches like support vector machine (SVM) are also used at different scales (remote sensing or laboratory applications). However, with the development of high resolution sensors, coupled exploitation of spectral and spatial information to process complex images, would appear to be a very relevant approach. However, few methods are proposed in the litterature. The most recent approaches can be broadly classified in two main categories. The first ones are related to a direct extension of individual pixel classification methods using just the spectral dimension (k-means, fuzzy-c-means or FCM, Support Vector Machine or SVM). Spatial dimension is integrated as an additionnal classification parameter (Markov fields with local homogeneity constrainst [5], Support Vector Machine or SVM with spectral and spatial kernels combination [2], geometrically guided fuzzy C-means [3]...). The second ones combine the two fields related to each dimension (spectral and spatial), namely chemometric and image analysis. Various strategies have been attempted. The first one is to rely on chemometrics methods (Principal Component Analysis or PCA, Independant Component Analysis or ICA, Curvilinear Component Analysis...) to reduce the spectral dimension and then to apply standard images processing technics on the resulting score images i.e. data projection on a subspace. Another approach is to extend the definition of basic image processing operators to this new dimensionality (morphological operators for example [1, 4]). However, the approaches mentioned above tend to favour only one description either directly or indirectly (spectral or spatial). The purpose of this paper is to propose a hyperspectral processing approach that strikes a better balance in the treatment of both kinds of information....Cet article présente une stratégie de segmentation d’images hyperspectrales liant de façon symétrique et conjointe les aspects spectraux et spatiaux. Pour cela, nous proposons de construire des variables latentes permettant de définir un sous-espace représentant au mieux la topologie de l’image. Dans cet article, nous limiterons cette notion de topologie à la seule appartenance aux régions. Pour ce faire, nous utilisons d’une part les notions de l’analyse discriminante (variance intra, inter) et les propriétés des algorithmes de segmentation en région liées à celles-ci. Le principe générique théorique est exposé puis décliné sous la forme d’un exemple d’implémentation optimisé utilisant un algorithme de segmentation en région type split and merge. Les résultats obtenus sur une image de synthèse puis réelle sont exposés et commentés

"Bacchus" Methodological approach for vineyard inventory and management. Chap.4: Textural and structural analysis

Ce chapitre présente les méthodes qui ont été développées dans le projet Bacchus pour la détection et la caractérisation des parcelles de vigne en imagerie aérienne en se basant sur leur structure. Une analyse texturale est d'abord mise en oeuvre, et complétée par l'introduction de contraintes de régularité des contours pour améliorer la segmentation. Finalement, les parcelles issues de ces premières étapes sont vérifiées et caractérisées au moyen d'une analyse de leur spectre de Fourier. Les résultats obtenus sur diverses zones d'étude du projet Bacchus sont présentés et discutés. / This chapter presents the methodologies that have been developed during the Bacchus project concerning the automatic detection and characterisation of vineyard plots in satellite and aerial images, based on their structural properties. First, a textural analysis has been used. Then shape regularity constraints have been introduced to improve the image segmentation. Finally, the vineyard plots issued from these previous steps are checked and characterised using a Fourier spectrum analysis. Results on various study areas of the Bacchus project are presented and discussed

Hyperspectral image segmentation: the butterfly approach

International audienceFew methods are proposed in the litterature for coupling the spectral and the spatial dimension available on hyperspectral images. This paper proposes a generic segmentation scheme named butterfly based on an iterative process and a cross analysis of spectral and spatial information. Indeed, spatial and spatial structures are extracted in spatial and spectral space respectively both taking into account the other one. To apply this layout on hyperspectral imgages, we focus particulary on spatial and spectral structures i.e. topologic concepts and latent variable for the spatial and the spectral space respectively. Moreover, a cooperation scheme with these structures is proposed. Finally, results obtained on real hyperspectral images using this specific implementation of the butterfly approach are presented and discussed

Fleets of robots for environmentally-safe pest control in agriculture

Feeding the growing global population requires an annual increase in food production. This requirement suggests an increase in the use of pesticides, which represents an unsustainable chemical load for the environment. To reduce pesticide input and preserve the environment while maintaining the necessary level of food production, the efficiency of relevant processes must be drastically improved. Within this context, this research strived to design, develop, test and assess a new generation of automatic and robotic systems for effective weed and pest control aimed at diminishing the use of agricultural chemical inputs, increasing crop quality and improving the health and safety of production operators. To achieve this overall objective, a fleet of heterogeneous ground and aerial robots was developed and equipped with innovative sensors, enhanced end-effectors and improved decision control algorithms to cover a large variety of agricultural situations. This article describes the scientific and technical objectives, challenges and outcomes achieved in three common crops