Unified Plant Disease and Pest Detection System with Integrated Decision Support Framework Using Deep Learning: A Review
Author : Fousiya PU
Abstract :
Plant health management is a critical challenge in agriculture. While deep learning has shown promise in plant disease detection, most systems treat disease and pest recognition separately and lack actionable follow-up mechanisms. This paper proposes a unified convolutional neural network (CNN)-based architecture capable of detecting both plant diseases and pests across multiple crops. Additionally, we integrate a decision support system (DSS) to provide treatment recommendations, enabling end-to-end plant health monitoring and management. The proposed system is evaluated using an extended PlantVillage dataset and achieves a classification accuracy of 96.8% across 35 disease and pest classes. The DSS module demonstrates efficacy in generating reliable treatment suggestions based on agronomic best practices.
Keywords :
Plant disease, crops, agronomics.
