Authors:
Aung Myint Myat, April Thet Su, Hlaing Htake Khaung Tin, M. R. Anitha, S. Sathya
Addresses:
Faculty of Information Science, University of Information Technology, Yangon, Myanmar. Department of Electronics and Communication Engineering, Dhaanish Ahmed College of Engineering, Chennai, Tamil Nadu, India. Department of Computer Science and Information Technology, School of Computing Sciences, Vels Institute of Science, Technology and Advanced Studies, Chennai, Tamil Nadu, India.
Abstract:
Plant diseases continue to pose a serious risk to global food security and agricultural productivity, resulting in significant financial losses every year. If not identified and treated promptly, these diseases, caused by nematodes, bacteria, viruses, and fungi, reduce crop yield and quality. Although they are dependable, traditional diagnostic techniques such as visual inspection, microscopy, and culture-based methods are often labor-intensive and time-consuming. Rapid and precise pathogen identification has been enabled by recent advances in molecular biology, including next-generation sequencing, enzyme-linked immunosorbent assay (ELISA), and polymerase chain reaction (PCR). Simultaneously, scalable solutions for precision agriculture have been enabled by computational methods, particularly image processing, machine learning, and deep learning, which have become effective tools for early disease detection in plant images. Sustainable disease outbreak control has been achieved through integrated disease management strategies that combine biological, chemical, and cultural approaches. Despite these advances, challenges persist, including pathogen variability, resistance development, limited accessibility to advanced diagnostic tools, and environmental influences. This review provides an extensive summary of plant disease types, detection techniques, and management approaches. It emphasizes combining cutting-edge technology with conventional methods to improve early detection, reduce crop losses, and promote sustainable agriculture. Finally, future research directions for precision plant disease management are discussed.
Keywords: Plant Disease; Modern Technologies; Polymerase Chain Reaction; Plant Images; Agricultural Productivity; Deep Learning (DL); Image Processing; Disease Management.
Received on: 28/03/2025, Revised on: 13/05/2025, Accepted on: 16/07/2025, Published on: 01/03/2026
DOI: 10.64091/ATIIR.2026.000277
Ave Trends in Intelligent Informatics Reports, 2026 Vol. 1 No. 1 , Pages: 14–23