Tania Khatun
Papers
2
Total Citations
44
H-Index
2
About
Tania Khatun is a researcher at the forefront of applying computer vision and deep learning to agricultural science, with a primary focus on post-harvest quality assessment and maturity classification of fruits. Her work centers on developing robust, real-world image datasets that enable automated grading systems, directly addressing the critical need for non-destructive, efficient quality control in the food supply chain. Her most impactful contribution is the creation of a comprehensive dragon fruit image dataset for detecting maturity and quality grading, which has garnered 34 citations and serves as a foundational resource for researchers working on tropical fruit classification. She has further extended this methodology to tomatoes, producing an extensive real-world field dataset for maturity classification and defect recognition (10 citations), a vital tool for reducing food waste and ensuring consumer safety. By prioritizing in-field, realistic imaging conditions over controlled lab settings, Khatun’s datasets bridge the gap between academic research and practical agricultural deployment, making her work highly relevant for students and engineers developing smart farming solutions.
Research Focus
Key Achievements
Top Papers
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