Frédéric Cointault
Agrosup Dijon – UMR Agroécologie, France
Title: Image acquisition and processing for Precision Farming applications
Biography
Biography: Frédéric Cointault
Abstract
Initially developed for technical industrial sectors such as medicine or aeronautics, imaging technics are more and more used since 30 years in agriculture and viticulture. The development of acquisition tool and the decreasing of the calculation time allowed using imagery in laboratory under controlled conditions. At the beginning of the 90’s, the concept of Precision Farming has been developed in the USA, considering a field as a heterogeneous area needing different input in terms of fertiliser or protection product. In the same time, the aperture of the GPS system for civil applications has allowed the development of remote sensing domain. Combining GPS information and imagery conducted also to the emergence of proxy-detection applications, in agriculture and viticulture domains, in order to optimize crop management. A localized crop management needs the use of new technologies such as computing, electronics and imaging, and the conception of a proxy-detection system is motivated by the need of better resolution, precision, temporality and lower cost, compared to remote sensing. The use of computer vision techniques allows obtaining this information automatically with objective measurements compared with visual or manual acquisition. The main applications covered by the computer vision in agriculture are tied to the crop characterization (biomass estimation, leaf area, volume, height of the crop, disease determination etc), the aerial or root phenotyping in the fields or in specific platforms and the understanding of spraying and spreading processes. This presentation will explain the different imaging systems used to characterize the previous parameters, in 2D or 3D. It will also give some details on the dedicated image processing methods developed, related to motion estimation, focus information, pattern recognition and multi –hyper spectral data.