Please use this identifier to cite or link to this item: http://dspace.aiub.edu:8080/jspui/handle/123456789/2811
Title: Innovative Approaches to Tomato Leaf Disease Detection Bridging Tradition and Technology.
Authors: Imtiaz, Ahmed
Hossain, Sk Muktadir
Rihan, Rahat
Gomes, Dipta
Issue Date: 2-May-2025
Publisher: IGI Global
Citation: Imtiaz, A., Hossain, S. M., Rihan, R., & Gomes, D. J. (2025). Innovative Approaches to Tomato Leaf Disease Detection Bridging Tradition and Technology. In B. Ray, J. Hassan, H. Huang, N. Islam, & Z. Shahadat (Eds.), Intelligent Internet of Everything for Automated and Sustainable Farming (pp. 123-148). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3373-0020-7.ch004
Abstract: This study explores the transformative potential of integrating Artificial Intelligence (AI) with precision agriculture to address key challenges in farming, such as disease detection, resource optimization, and yield improvement. By leveraging the Real-Time Detection Transformer (RT-DETR) framework, the study combines high-resolution imaging, hyperspectral scanners, and real-time data processing to enable efficient and accurate detection of tomato leaf diseases. A robotic system, equipped with autonomous navigation and non-invasive diagnostic tools, was developed to classify diseases and provide actionable insights for sustainable crop management. The study evaluates the performance of object detection models, such as YOLOv8 and RT-DETR, on the PlantVillage and PlantDoc datasets. RT-DETR achieving a mean Average Precision (mAP) of 0.988 on the controlled PlantVillage dataset and 0.402 on the complex PlantDoc dataset. By integrating AI with farming practices, this research highlights a pathway to improving agricultural productivity, environmental resilience, and global food security.
URI: https://www.igi-global.com/chapter/innovative-approaches-to-tomato-leaf-disease-detection-bridging-tradition-and-technology/378260
http://dspace.aiub.edu:8080/jspui/handle/123456789/2811
ISBN: 9798337300207
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