Wheat Disease Detection Using YOLO and Drone-Captured Images

Authors

  • Km. Neha School of Computing, DIT University, Dehradun, Uttarakhand, India
  • Akash Arya Department of Computer Engineering, G.B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India
  • Rajeev Singh Department of Computer Engineering, G.B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India

DOI:

https://doi.org/10.13052/jgeu0975-1416.1327

Keywords:

YOLOv8, drone imaging, machine learning, precision farming, disease detection

Abstract

Mitigating crop loss and maximizing resource usage in agriculture are contingent upon the timely and precise identification of wheat illnesses. In this paper, we offer a YOLOv8-based method that uses drone-captured images to detect wheat diseases in real time. Four distinctive image classes represent our dataset: Ground, Yellow Rust, Brown Rust, and Healthy. Smart spray drones can target disease management by recognizing non-crop regions in the field, a task made possible in large part by the Ground class. This class allows our model to distinguish between areas that need to be treated and those that don’t, improving chemical spraying accuracy and cutting down on waste. Using data gathered from wheat fields, the model was trained and tested, and it performed excellently in differentiating between crops that were diseased and those that weren’t. The model achieved a highest precision of 0.803 and recall of 0.850 across various classes and the overall performance included a mean average precision of 0.605, demonstrating robust performance in field conditions, which qualifies it for use in agricultural monitoring systems that operate in real-time. This effort is a step toward automated, data-driven precision agriculture, which will assist farmers in allocating resources and managing diseases in a timely and effectively manner.

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Author Biographies

Km. Neha, School of Computing, DIT University, Dehradun, Uttarakhand, India

Km. Neha holds a Master of Technology (M.Tech) degree from G.B. Pant University of Agriculture and Technology (GBPUAT), Pantnagar and she graduated from women institute of Technology dehradun in 2017 in computer science engineering with a strong academic background and a keen interest in the intersection of technology and agriculture, her research area is drone technology in smart agriculture. She is currently serving as an assistant professor at DIT University, where she is actively involved in teaching and research related to emerging technologies in agriculture.

Akash Arya, Department of Computer Engineering, G.B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India

Akash Arya is a dedicated Master of Technology student and researcher in the Department of Computer Engineering at G.B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India. His research interests lie at the intersection of Agricultural Sciences and Artificial Intelligence, with a focus on leveraging AI technologies to address real-world challenges in agriculture. Driven by a commitment to innovation and academic excellence, Mr. Arya contributes significantly to the advancement of AI applications in precision farming, crop prediction, and sustainable agricultural practices. His work exemplifies a strong interdisciplinary approach, and he continues to serve as an inspiration to aspiring researchers in the fields of computer engineering and agricultural technology.

Rajeev Singh, Department of Computer Engineering, G.B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India

Rajeev Singh is currently working as Professor in the Department of Computer Engineering, G. B. Pant University of Ag. & Technology, Uttarakhand (India). He has more than 20 years of teaching experience. He received his Ph.D. Degree from N.I.T. Hamirpur (H. P.) and M. Tech. Degree from Indian Institute of Technology, Roorkee, both in Computer Science and Engineering. His research interest includes information systems, computer networks, network security, IoT, and Drones. He has guided more than 18 M.Tech. students. He has published several book chapters and research papers in journals and conferences of repute.

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Published

2025-08-07

How to Cite

Neha, K., Arya, A., & Singh, R. (2025). Wheat Disease Detection Using YOLO and Drone-Captured Images. Journal of Graphic Era University, 13(02), 411–438. https://doi.org/10.13052/jgeu0975-1416.1327

Issue

2025: Vol 13 Iss 2

Section

Articles