Data Science and Intelligent Computing Techniques
Optimizing Agricultural Sustainability: AI-Based Yield Prediction Bot for Precision Agriculture
Authors: Sangeeta Kurundkar, Kuldeep Aher, Shrirang Mahankaliwar, Swayamprakash Mahale, Divya Lothe and Sahil Mandhare
Publishing Date: 19-12-2023
ISBN: 978-81-955020-2-8
Abstract
Yield estimation is a critical task in modern agriculture, as it enables farmers to optimize crop management strategies and offers substantial benefits to stakeholders to optimize their supply chains with informed decisions.In this paper, authors propose a novel approach for yield estimation using image processing techniques, combined with state-of-the-art tracking algorithms and object detection algorithms such as YOLO v8 (You Only Look Once), and BoT-SORT (Deep Learning-based Object Tracking). This research utilizes both image processing and deep learning methods, providing a cost-effective and efficient solution for crop yield estimation. The experiments conducted have demonstrated promising results across various metrics, making it a valuable resource for farmers. By bridging the gap between technology and agriculture, this research not only improves crop yields but also contributes to the sustainability of the food supply in a rapidly growing world population.
Keywords
Crop-Yield estimation, Computer vision, YOLO v8, Object detection.
Cite as
Sangeeta Kurundkar, Kuldeep Aher, Shrirang Mahankaliwar, Swayamprakash Mahale, Divya Lothe and Sahil Mandhare, "Optimizing Agricultural Sustainability: AI-Based Yield Prediction Bot for Precision Agriculture", In: Satyasai Jagannath Nanda and Rajendra Prasad Yadav (eds), Data Science and Intelligent Computing Techniques, SCRS, India, 2023, pp. 837-848. https://doi.org/10.56155/978-81-955020-2-8-71