Fruits and Pest Diseases Detection using Deep Learning-Based Approach

Current agricultural environments present threats from fungal and bacterial pathogens which pose grave threats to both crop production and food security, necessitating timely detection of plant pathogens as a priority. Computerbased techniques that employ deep learning methodologies have been devised to effectively detect plant diseases by analysing indicators on stems and leaves. We focus on three primary detection systems that include YOLO, region-based Fully Convolutional Networks (R-FCNs) and faster Region-based Convolutional Neural Networks (Faster R-CNNs) as “deep learning meta-architectures, including Residual Network and VGG Net. To enhance precision and minimize false positives in training time, we have devised a technique which combines global and local class tagging as well as feature extraction. To do this, we used an immense database called Pests and Diseases of Fruit Information that contains various images of pests and diseases as well as details like inflammation severity and root location - this data served to train and assess our systems extensively; its results demonstrate its ability to precisely identify nine pests/diseases even in challenging soils. At the core of it all lies our research: deep-learning meta-architectures and attribute concentrators demonstrate their value in disease detection. By employing cutting edge technologies, we hope to enhance plant health management while optimizing crop yield.