PlantCareNet: an advanced system to recognize plant diseases with dual-mode recommendations for prevention

Abstract

Plant diseases adversely affect the agricultural sector by substantially affecting food security and limiting production. We introduce PlantCareNet, a novel, automated, end-to-end diagnostic system for plant diseases that can also offer interactive guidance to users. The system utilizes a dual mode strategy that integrates advanced deep learning algorithms for precise disease diagnosis with a knowledge-based framework guided by experts for preventive measures. The proposed architecture utilizes a convolutional neural network (CNN) to examine images of plant leaves, with the final block flattened and subsequently forwarded to Dense-100 and ultimately Dense-35 for the precise classification of various plant diseases. Subsequently, PlantCareNet promptly offers two types of recommendations: automated suggestions based on identified symptoms and expert-guided advice for personalized treatment. Both categories of recommendations are accessible immediately. The experimental findings indicate that PlantCareNet can accurately diagnose diseases in five well-known datasets, with an accuracy between 82% and 97%, outperforming notable models like Inception and ResNet in most cases. The overall approach demonstrates advancement by surpassing lightweight CNN models with 97% precision and an average inference time of 0.0021 s, hence offering farmers precise and quick actions for remedy. This study emphasises a novel blend of artificial intelligence-driven recognition and expert consultation, which contributes to the advancement of sustainable agriculture practices.