Ben K. Jose

About

Ben K. Jose is a researcher at the forefront of applying computer vision to agricultural technology, with a particular focus on the classification and quality assessment of bird eye chili (also known as 'kantahri mulaku'). His most impactful work, "Computer Vision Assisted Real-Time Bird Eye Chili Classification Using YOLO V5 Framework," has garnered 9 citations, demonstrating its relevance in the emerging field of AI-driven precision agriculture. Jose’s major contribution lies in adapting the state-of-the-art YOLO V5 object detection model for real-time, automated sorting of this economically important spice crop. By developing a framework that can accurately distinguish between different grades or qualities of bird eye chili, he addresses a critical bottleneck in post-harvest processing, reducing reliance on manual labor and improving consistency. His work, further detailed in a companion paper with 2 citations, showcases a practical, scalable solution that bridges deep learning and agricultural engineering. Jose’s research not only advances computer vision applications in the Global South but also provides a template for similar classification tasks in other specialty crops, making him a notable contributor to the growing intersection of AI and sustainable agriculture.

Research Focus

Key Achievements

Top Papers

1. 1
   Computer Vision Assisted Real-Time Bird Eye Chili Classification Using YOLO V5 Framework

   9 citations · 2023
2. 2
   Computer Vision Assisted Bird–Eye Chilli Classification Framework Using YOLO V5 Object Detection Model

   2 citations · 2023

Key Collaborators

Contact & Links