Sonia Aribam

National Institute of Technology Manipur

Papers

Total Citations

H-Index

About

Sonia Aribam is a researcher whose work lies at the intersection of agricultural technology and artificial intelligence, with a primary focus on precision weed management. Her most notable contribution is the development and application of deep learning techniques for automated weed detection, a critical challenge in sustainable agriculture. In her 2021 paper, "Improving Weed Detection Using Deep Learning Techniques," Aribam explores how convolutional neural networks can be optimized to distinguish crops from weeds with higher accuracy, reducing the need for broad-spectrum herbicides. While her citation count is currently modest, her work addresses a pressing global need for environmentally friendly farming practices. Aribam’s research is particularly valuable for its potential to enable real-time, drone-based weed monitoring, making precision agriculture more accessible to small-scale farmers. By bridging computer vision and agronomy, she is contributing to a future where AI-driven tools can help reduce chemical runoff and improve crop yields. Her dedication to this niche but impactful field positions her as an emerging voice in the application of deep learning to agricultural sustainability.