Transforming Agriculture through Artificial Intelligence: Advancements in Plant Disease Detection, Applications, and Challenges
Lipikant Sahoo *
Department of Plant Pathology, Odisha University of Agriculture and Technology, India.
Deepali Mohapatra
Department of Plant Pathology, Odisha University of Agriculture and Technology, India.
Himendra Raj Raghuvanshi
Department of Plant Pathology, Chandra Shekhar Azad University of Agriculture and Technology, Kanpur, 208002, U.P, India.
Sonal Kumar
Department of Plant Pathology, Mahatma Gandhi University of Horticulture and Forestry Durg Chhattisgarh, India.
Ravinder Kaur
School of Allied Sciences, Dev Bhoomi Uttarakhand University, India.
Anshika
School of Agriculture, Dev Bhoomi Uttarakhand University, India.
Sapna
ICAR- Indian Institute of Wheat & Barley Research, Karnal132001, India.
Ritik Chawla
Department of Fruit Science, College of Horticulture, Dr. Yashwant Singh Parmar University of Horticulture and Forestry, Nauni, Solan, Himachal Pradesh, 173 230, India.
Nadiya Afreen
Research Floor Society of India, India.
*Author to whom correspondence should be addressed.
Abstract
Artificial intelligence (AI) has emerged as a revolutionary tool in agriculture, particularly in the realm of plant disease detection. This article provides an overview of AI-powered plant disease detection methods, their applications, and the limitations associated with their implementation. By leveraging AI, farmers can enhance crop management practices, optimize resource utilization, and mitigate yield losses caused by plant diseases. However, challenges such as data scarcity, model interpretability, and deployment in resource-constrained environments remain significant barriers to widespread adoption. Addressing these limitations is crucial for maximizing the potential of AI in revolutionizing agriculture and ensuring global food security. Artificial intelligence (AI) has emerged as a pivotal tool in modernizing agriculture, particularly in the domain of plant disease detection. This article presents a comprehensive examination of AI-driven methodologies for plant disease detection, exploring their applications and inherent limitations. Through the utilization of machine learning and computer vision techniques, AI facilitates early disease identification, precision agriculture, disease surveillance, and decision support systems. Despite these transformative capabilities, challenges such as inadequate data availability, model interpretability, and implementation in resource-constrained settings impede widespread adoption. Addressing these obstacles is imperative for fully harnessing the potential of AI in agricultural innovation, thereby safeguarding global food security and sustainability.
Keywords: Agriculture, innovations, challenges, global good, security, environment, climate change