Mohammad Yasin Ud Dowla
Papers
1
Total Citations
23
H-Index
1
About
Mohammad Yasin Ud Dowla is a researcher at the forefront of agricultural automation and intelligent systems. His work centers on applying computer vision and machine learning to solve critical challenges in precision agriculture, with a particular focus on post-harvest quality assessment and robotic crop monitoring. His most cited paper, "Detection, quantification and classification of ripened tomatoes: a comparative analysis of image processing and machine learning" (2020, 23 citations), provides a foundational comparison between traditional image processing and machine learning techniques for identifying ripe and defective tomatoes directly in the field. This study, which employed a camera mounted on a mobile robot, demonstrated the practical viability of cascaded object detectors for real-time agricultural sorting. By systematically evaluating both methods, Dowla has contributed essential benchmarks that guide the development of cost-effective, automated harvesting and quality control systems. His work bridges the gap between theoretical computer vision and deployable agri-robotics, offering scalable solutions for reducing post-harvest losses. For students and researchers in agricultural engineering and AI, Dowla’s research exemplifies how targeted, comparative studies can accelerate the adoption of smart farming technologies.
Research Focus
Key Achievements
Top Papers
- 1