M. Kumarasamy

Papers

Total Citations

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About

M. Kumarasamy is a researcher focused on the intersection of precision agriculture and artificial intelligence, with a particular emphasis on computer vision for sustainable farming. Their key research areas include deep learning for weed detection, automated crop monitoring, and environmentally friendly agricultural practices. Kumarasamy’s most notable contribution is the design and evaluation of a deep convolutional neural network (CNN) algorithm for detecting farm weeds, published in 2023. This work addresses a critical environmental challenge: the overuse of herbicides, which are often sprayed indiscriminately across wide areas, harming ecosystems and non-target organisms. By enabling precise, targeted weed identification, Kumarasamy’s algorithm reduces the need for both chemical spraying and manual labour, offering a more efficient and eco-friendly alternative. Although early in its citation history with 3 citations to date, this research has already demonstrated significant potential for real-world impact in smart farming. Kumarasamy’s work represents a promising step toward integrating AI into agricultural decision-making, helping to minimize environmental harm while improving crop management efficiency.