KI in der Diagnose von Pflanzenkrankheiten

Mohanty and colleagues trained a convolutional image classifier using 54,306 images of diseased and healthy leaves collected under controlled conditions. The paper describes 14 crop species and 26 diseases or their absence; this is not a count of 26 combined disease-and-healthy classification labels. Its evaluation on images from different online sources performed much worse than its held-out controlled-data evaluation, illustrating why target-setting validation matters. A separate multispectral study measured near-infrared and red-edge reflectance in detached tomato leaves inoculated with Botrytis cinerea to investigate presymptomatic gray mold. Those observations do not establish a universal signal for every crop and pathogen. USGS defines NDVI as (NIR − red)/(NIR + red), an indicator of vegetation greenness or condition rather than a pathogen diagnosis. Domain shift means training and deployment image distributions differ, which can change performance. Transfer learning may help when pretrained features are useful for the target images, but its benefit requires evaluation. FAO estimates annual global crop-yield reductions of 20–40 percent from plant pests and diseases together; this is not a loss estimate for diseases alone or a claim about what image-based diagnosis can prevent.