Umesh Kumar Lilhore
Papers
4
Total Citations
47
H-Index
3
About
Umesh Kumar Lilhore is a researcher whose work sits at the intersection of artificial intelligence, deep learning, and smart computing systems. His research spans agricultural technology, Industry 4.0 automation, and the broader societal implications of AI in modern workplaces. Lilhore has made particularly notable contributions to precision agriculture, developing the PFDI (Precise Fruit Disease Identification) model, which leverages context data fusion with Faster-CNN within edge computing environments to accurately detect diseases in delicate citrus fruits — a breakthrough that has garnered 28 citations and demonstrates meaningful real-world applicability. His exploration of deep learning and machine learning within Industry 4.0 frameworks highlights his commitment to advancing smart factory technologies, contributing to the understanding of how AI-driven automation reshapes industrial processes. Beyond technical innovation, Lilhore engages with critical questions surrounding AI's transformation of workplaces, examining both the productivity gains and ethical concerns such as labor displacement and algorithmic bias. With a growing citation record across applied AI, agricultural computing, and workforce studies, his interdisciplinary approach positions him as an emerging voice in the practical deployment of intelligent systems across diverse domains.
Research Focus
Key Achievements
Top Papers
- 1
- 2Impact of Deep Learning and Machine Learning in Industry 4.012 citations · 2021
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- 4