Plant Disease Detection Using Deep Learning

Abstract

Agriculture plays a crucial role in human life, with nearly 60% of the population engaged in some form of agricultural activity. However, traditional agricultural systems often lack technologies for effectively detecting diseases in crops, leading to stagnation in productivity. Timely detection of crop diseases is essential as they can significantly impact crop growth. While many Machine Learning (ML) models have been employed for disease detection, recent advancements in Deep Learning (DL) show promise for improved accuracy. The proposed method utilizes convolutional neural networks (CNNs) and Deep Neural Networks (DNNs) to effectively identify and recognize symptoms of crop diseases. Various efficiency metrics are employed to evaluate the performance of these models. This paper provides a comprehensive overview of DL models used for visualizing crop diseases and identifies research gaps that could lead to enhanced disease detection even before symptoms manifest. The proposed methodology focuses on developing a CNN-based approach for detecting plant leaf diseases.