Objetivo
Crop protection represents one of the highest costs in farm budgets. Farms are under increasing social and economic pressure to reduce pesticide inputs dramatically. Although most weed and disease infestations occur in patches, the most widely used practice is still spraying pesticides uniformly. In this project a ground based real-time remote sensing system will be conceived which it will be possible to detect atomically, during field operation, plant diseases on arable crops in an early stage of the development of the disease, even before the diseases can be visibly detected. The methodology will use differences in reflectance and fluorescence properties, and leaf temperature variations between healthy and diseased plants. An intelligent multisensor fusion decision system based on neural networks will be used to decide on the presence of diseases or plant stresses, in order to treat the diseases in a spatially variable way.
Ámbito científico (EuroSciVoc)
CORDIS clasifica los proyectos con EuroSciVoc, una taxonomía plurilingüe de ámbitos científicos, mediante un proceso semiautomático basado en técnicas de procesamiento del lenguaje natural.
CORDIS clasifica los proyectos con EuroSciVoc, una taxonomía plurilingüe de ámbitos científicos, mediante un proceso semiautomático basado en técnicas de procesamiento del lenguaje natural.
- ingeniería y tecnologíaingeniería ambientalteledetección
- ciencias agrícolasagricultura, silvicultura y pescaagricultura
- ciencias naturalesinformática y ciencias de la informacióninteligencia artificialinteligencia computacional
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Convocatoria de propuestas
Data not availableRégimen de financiación
CSC - Cost-sharing contractsCoordinador
3001 HEVERLEE
Bélgica