Abdur Razzak
Papers
1
Total Citations
10
H-Index
1
About
Abdur Razzak is a leading researcher in agricultural computer vision and precision horticulture, with a focus on leveraging deep learning for crop quality assessment. His work centers on developing intelligent systems for automated fruit maturity classification and defect detection, addressing critical challenges in post-harvest processing and food supply chain efficiency. His most cited study, "An extensive real-world in field tomato image dataset involving maturity classification and recognition of fresh and defect tomatoes" (2023), has already garnered 10 citations, underscoring its immediate impact. This contribution provides a meticulously curated, real-world dataset that enables robust training of machine learning models to distinguish between fresh, defective, and various maturity stages of tomatoes—a crop vital for global nutrition and culinary use. By bridging the gap between controlled laboratory conditions and complex field environments, Razzak’s work empowers farmers and agribusinesses to reduce waste, ensure consistent quality, and enhance food safety. His research not only advances agricultural automation but also supports sustainable practices, making him a pivotal figure in the intersection of AI and horticulture.
Research Focus
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Top Papers
- 1