Seema Verma

Papers

Total Citations

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About

Seema Verma is a researcher whose work sits at the intersection of artificial intelligence and agricultural technology, with a particular focus on applying neural networks to solve practical problems in food quality assessment. Her most-cited paper, "Analysis and Detection of Fruit Defect Using Neural Network" (2018), has garnered 13 citations and represents a key contribution to the field of computer vision in agriculture. In this work, Verma developed a neural network-based approach for automatically identifying defects in fruit, offering a non-destructive, efficient alternative to manual inspection. This research has implications for reducing food waste and improving quality control in the agricultural supply chain. While her citation count reflects the early-stage impact of her work, her focus on accessible, applied AI solutions—particularly in resource-constrained settings—positions her as a promising voice in the growing field of smart agriculture. Verma’s contributions demonstrate how machine learning can be harnessed for tangible, real-world outcomes, making her research relevant to students and practitioners interested in the practical deployment of AI in food science and agricultural engineering.