Poonam Dhiman
Papers
2
Total Citations
30
H-Index
2
About
Poonam Dhiman is a researcher whose work sits at the dynamic intersection of artificial intelligence, deep learning, and agricultural technology. Her most notable contribution is the development of PFDI (Precise Fruit Disease Identification), an innovative model that leverages context data fusion with Faster-CNN architectures deployed within edge computing environments. This work addresses a critical real-world challenge: the early and accurate detection of diseases in citrus and other nutritionally valuable fruits, which are highly susceptible to infection and spoilage. By combining convolutional neural networks with edge computing capabilities, Dhiman's approach enables faster, more efficient disease identification that could be practically deployed in agricultural settings without reliance on centralized cloud infrastructure. Her 2023 publication has garnered 28 citations, reflecting meaningful engagement from the research community working on smart agriculture and precision farming solutions. Dhiman's research demonstrates a strong commitment to applying cutting-edge machine learning techniques to problems with tangible societal impact, particularly in food security and sustainable farming. Her work positions her as an emerging voice in AI-driven agricultural diagnostics, with potential to influence both academic research and practical farming technology development.
Research Focus
Key Achievements
Top Papers
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- 2