Detection of plant diseases using artificial intelligence techniques

Abstract

Artificial intelligence (AI), and in particular deep learning, is transforming the agricultural sector by enabling automated, fast, and reliable detection of plant diseases. This study explores an image classification approach for leaf images using Convolutional Neural Networks (CNNs), with the aim of automatically diagnosing several plant pathologies. The study is initially based on the standardized PlantVillage dataset, which is used to train and compare different architectures, including both pre-trained models (such as MobileNetV2) and a custom CNN model. The best model achieved an accuracy of 98% while maintaining low computational complexity. To make this solution accessible to users, a web interface was also developed, allowing automated diagnosis from user-uploaded images. This work thus contributes to the design of AI-assisted diagnostic systems for more effective plant disease management. Additionally, an initial series of tests was conducted on images from real-world conditions, marking an exploratory step toward validating the model in uncontrolled environments.