Intelligent multi-sensor system for the detection and treatment of fungal diseases in arable crops

Abstract

The development of a ground-based real-time remote sensing system that can be carried by tractors or robotic platforms is described. This prototype system makes possible the detection of plant diseases in arable crops automatically at an early stage of disease development and during field operations. The methodology uses differences in reflectance between healthy and diseased plants. Hyperspectral reflectance and multi-spectral imaging techniques were developed for simultaneous acquisition in the same canopy. Experimental platforms were constructed, and the advantage of using sensor fusion was demonstrated. An intelligent multi-sensor fusion decision system based on neural networks was developed to predict the presence of diseases or plant stresses, in order to treat the diseases in a spatially variable way. A robust multi-sensor platform integrating optical sensing, GPS (Geostationary Positioning System) and a data processing unit was constructed and calibrated. The functionality of automatic disease sensing and detection devices is crucial in order to conceive a site-specific spraying strategy against fungal foliar diseases. Field tests were carried out to optimise the functioning of the multi-sensor disease detection device. An overview is provided on how disease presence data are processed in order to enable an automatic site-specific spraying strategy in winter wheat. Furthermore, mapping of diseases based on automated optical sensing and intelligent prediction provide a spatially variable recommendation for spraying.