Papers
2
Total Citations
4
H-Index
2
About
Deepali M. Bongulwar is a researcher specializing in deep learning and computer vision, with a particular focus on agricultural applications of artificial intelligence. Her work centers on developing automated systems for fruit recognition and classification, leveraging the power of Convolutional Neural Networks (CNNs) to solve real-world challenges in precision agriculture and robotic harvesting. Among her most notable contributions, Bongulwar has designed robust CNN-based models capable of automatically extracting features from fruit images, eliminating the need for manual feature engineering. Her 2021 study utilized the high-quality ImageNet dataset to build and evaluate a reliable fruit classification system, while her 2022 follow-up work further refined deep learning approaches for accurate fruit identification — both works earning recognition within the research community with citations reflecting their utility to peers exploring similar domains. Bongulwar's research addresses a pressing need in modern agriculture: enabling machines to perform intelligent crop identification tasks with minimal human intervention. By bridging machine learning theory with practical agricultural deployment, her contributions offer meaningful groundwork for students and researchers exploring AI-driven solutions in food production, smart farming, and autonomous agricultural robotics.
Research Focus
Key Achievements
Top Papers
- 1Fruit Recognition Using Deep Learning Approach2 citations · 2022
- 2Robust Convolutional Neural Network Model For Recognition of Fruits2 citations · 2021