Plant Disease Recognition: A Comprehensive Mini Review

Abstract

Across the globe, agricultural yield faces numerous challenges, including unpredictable weather patterns, resource constraints, and the ever-present threat of plant diseases. Early and accurate disease detection is crucial for mitigating losses, optimizing resource allocation, and promoting sustainable farming practices. Machine Learning (ML) and Deep Learning (DL) techniques, particularly convolutional neural networks (CNNs), offer immense potential for tackling this challenge. This paper investigates the potential of DL for disease detection in agricultural crops. We delve into data scarcity, a major obstacle, and analyze the suitability of existing datasets like PlantVillage for training robust models. Furthermore, we explore popular CNN architectures, such as LeNet, AlexNet, and VGGNet, along with their strengths and limitations in the context of plant disease detection.