Jitendra Paliwal
Papers
2
Total Citations
55
H-Index
2
About
Jitendra Paliwal is a leading researcher in agricultural automation and precision farming, with a focus on integrating computer vision, machine learning, and robotics to solve critical challenges in post-harvest quality assessment and autonomous harvesting. His work spans two key areas: non-destructive fruit quality evaluation and robotic harvesting of tree crops. Paliwal’s major contributions include developing advanced image processing and spectroscopic techniques for estimating fruit ripeness, as demonstrated in his highly cited 2020 study on Fuji apples, which achieved 28 citations for its novel majority voting method. He has also pioneered deep learning approaches for robotic harvesting, notably creating an attention-guided Faster R-CNN system for detecting coconut clusters under occlusion conditions—a breakthrough addressing the dangerous and declining practice of manual coconut tree climbing. This 2022 work, with 27 citations, directly supports the development of autonomous harvesters for high-risk crops. Paliwal’s research has significant real-world impact, combining sensor fusion with artificial intelligence to improve agricultural efficiency and worker safety. His work is widely recognized for bridging the gap between laboratory techniques and practical, field-deployable solutions, making him a key figure in the future of smart agriculture.
Research Focus
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Top Papers
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