Implementation of Deep Convolution Neuro-Fuzzy Network to Plant Disease Detection, Risk Assessment, and Classification

Indian agriculture drives the economy. Sickness is common in plants exposed to several environmental conditions. This lowers crop quality. Due to recent weather changes, farmers’ biggest issue is maximizing output quality and quantity. Diagnosing and treating crop diseases quickly maximizes agricultural productivity. Illnesses can reduce tomato crop yields, lowering farmers’ income. To treat and control tomato leaf diseases, proper identification is crucial. Deep learning improved recognition accuracy. Artificial intelligence incorporates deep learning. Due to its self-directed learning and feature extraction benefits, academic and industry circles have been discussing it recently. Learning-based methods in plant leaf disease detection can speed up research, improve feature extraction, and loosen disease spot feature selection. To improve identification accuracy, deep learning now uses fuzzy rules to represent and manage fuzzy information. Using a deep neuro-fuzzy neural network, we classify rice plant illnesses. To extract complex features, the neuro-fuzzy network uses a fuzzy inference layer and a fuzzy pooling layer. Then classify them in the fully linked layer. A huge dataset of 8 types of infected and uninfected rice plant photos yielded a 96.9% identification accuracy for the moa del. Three performance indicators were also used. The trials show the benefits of employing NFNHDL to diagnose rice diseases.