SVM-RBN Model with Attentive Feature Culling Method for Early Detection of Fruit Plant Diseases

Abstract

Accurate disease identification and early disease management strategies are required in India to achieve high production standards and good quality in fruits and vegetables. Image-based evaluations approaches have evolved nowadays as a result of technical developments. However, producing the wrong decision may have a negative impact on productivity. Thus, this study offered hybrid SVM-RBN model with attentive feature culling method for automatically recognizing diseases in apple fruits with high accuracy. As a result, the model generated more effective outcomes with 96% accuracy, 99% precision, 94% recall, and 93% F1 Score. Thus, by employing this technology, one may detect fruit plant illnesses at an early stage, thereby increasing fruit yield.