Arjun Chouriya

About

Arjun Chouriya is a researcher at the forefront of applying deep learning to precision agriculture, with a particular focus on object detection in challenging, real-world environments. His most-cited work, "Detection of Cotton Plants Using the YOLOv7 Deep Learning Model" (2023), tackles the critical problem of recognizing crops under variable lighting and diverse physical attributes. By developing and elucidating a YOLOv7-based model, Chouriya demonstrated how state-of-the-art computer vision can be harnessed for rapid, automated identification of cotton plants—a task essential for smart farming and yield estimation. Though early in his publication career, his work has already garnered attention, with this paper accumulating 3 citations, signaling its relevance to both agricultural technology and deep learning communities. Chouriya’s contributions lie at the intersection of artificial intelligence and sustainable agriculture, offering scalable solutions that reduce reliance on manual monitoring. His research not only advances the field of agricultural robotics but also provides a blueprint for deploying deep learning models in unstructured outdoor settings, making him a promising voice in the growing domain of AI-driven agritech.

Research Focus

Key Achievements

Top Papers

1. 1
   Detection of Cotton Plants Using the YOLOv7 Deep Learning Model

   3 citations · 2023

Key Collaborators

Contact & Links