Recent Papers

Plant diseases play a crucial role in agricultural productivity, jeopardizing food security and economic stability. Early and precise detection of these diseases is important to facilitate sustainable agricultural practices and minimize crop losses. Recent developments in Artificial Intelligence (AI) and more specifically Machine Learning (ML) and Deep Learning (DL) have brought forth optimistic methods for the detection and classification of plant diseases. This paper presents a detailed overview and comparison of contemporary methods using image processing, ML classifiers, and deep learning models like CNNs, UNet, and VGG architecture. The comparative assessment indicates that deep learning models tend to be more accurate, scalable, and real- time compatible compared to conventional machine learning methods. In addition, environmental and biological parameters affecting the spread of disease are taken into account to improve model performance. The coupling of computer vision with AI-based models provides robust, scalable, and automated solutions for early diagnosis. This paper integrates existing methodologies, emphasizes field- ready implementations, and outlines areas of further investigation into the formulation of intelligent agricultural systems.

Copyright © 2025 Mahesh Subhash Hol. This is an open access article distributed under the Creative Commons Attribution License.