Mashood Mohammad Mohsan
Papers
1
Total Citations
7
H-Index
1
About
Mashood Mohammad Mohsan is a researcher at the forefront of agricultural artificial intelligence, with a primary focus on computer vision, tactile sensing, and deep learning for food quality assessment. His most notable contribution is the development of SwishFormer, a novel neural network architecture designed for robust firmness and ripeness recognition in fruits using visual-tactile imagery. This work, published in 2025 and already garnering 7 citations, addresses a critical challenge in the agricultural industry: replacing subjective human judgment and invasive sampling with non-destructive, automated assessment. Mohsan’s research directly impacts fruit quality control, shelf-life prediction, and consumer satisfaction by enabling precise, real-time ripeness estimation. His approach integrates visual and tactile data, pushing the boundaries of multimodal sensing in precision agriculture. By advancing deep learning methods for agricultural applications, Mohsan is helping to modernize food supply chains and reduce waste. His work represents a significant step toward scalable, AI-driven solutions for sustainable farming and post-harvest management.
Research Focus
Key Achievements
Top Papers
- 1