Diagnostics system for plant leaf diseases using photo images
Abstract
The subject under consideration is the issue of detecting agricultural crop diseases. The preliminary data for determining the phytosanitary status of cultivated plants are the images of their leaves. To tackle this issue, a model of diagnostic algorithms has been proposed, which involves creating a set of preferred characteristics and making diagnostic decisions based on comparing these features. The process of establishing the model of diagnostic algorithms has been outlined. The effectiveness of the proposed model has been demonstrated in diagnosing wheat diseases based on leaf images. A novel technique for detecting plant diseases has been introduced, relying on digital RGB photographs of leaves in the visible spectrum. The novelty of this method lies in diagnosing visible symptoms of plant leaf diseases from photographic images. There is a significant number of plant diseases that can reduce productivity, resulting in economic and environmental damage. Consequently, the accurate and timely diagnosis of plant diseases is paramount. One widely used approach for detecting plant pathologies involves using convolutional neural networks to diagnose plant leaf diseases from photographic images.