Fruit Detection and Counting for Yield Analysis in Digital Agriculture

Sornalakshmi K., Sayan Majumder, Yash Khandelwal

2024

Abstract

For the cause of evolution of agriculture to its next stage, Artificial Intelligence and Data driven approaches will play a major role in the development of agricultural practices that as per our vision would offer numerous economic, environmental and social benefits. Digital/Precision Agriculture is providing more benefits since the state-of-the-art ICT tools are used for better decision-making process. The other benefits include enhanced productivity in yield, reduced environmental footprints and better resource management. Our solution uses the adoption of Computer Vision and real time monitoring of plants, studying their respective conditions and their autonomous cultivation and harvesting patterns. The proposed system uses YOLO v8 algorithm for the detection of fruits from the Kaggle fruit detection data set and Mango YOLO dataset for four different fruits and returning the count of fruits in the image. The fruits were detected and counted from the images of the respective trees having various other parts like branches, leaves and flowers. Also the images from two data sets were combined to create four classes of fruits. The proposed system uses YOLOv8 and YOLO-NAS for detection and counting. Our results recorded an average confidence score of 92% for fruit detection and recall score of 0.97 for counting considering situations like un-ripe fruit and overlapping of objects. Our model was able to successfully count the accurate number of fruits in the test images with critically overlapping fruit counts in a test environment with a Tesla T4 GPU.

Paper Citation

in Harvard Style

K. S., Majumder S. and Khandelwal Y. (2024). Fruit Detection and Counting for Yield Analysis in Digital Agriculture. In Proceedings of the 1st International Conference on Emerging Innovations for Sustainable Agriculture - Volume 1: ICEISA; ISBN 978-989-758-714-6, SciTePress, pages 50-57. DOI: 10.5220/0012881200004519

in Bibtex Style

@conference{iceisa24,
author={Sornalakshmi K. and Sayan Majumder and Yash Khandelwal},
title={Fruit Detection and Counting for Yield Analysis in Digital Agriculture},
booktitle={Proceedings of the 1st International Conference on Emerging Innovations for Sustainable Agriculture - Volume 1: ICEISA},
year={2024},
pages={50-57},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012881200004519},
isbn={978-989-758-714-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Emerging Innovations for Sustainable Agriculture - Volume 1: ICEISA
TI - Fruit Detection and Counting for Yield Analysis in Digital Agriculture
SN - 978-989-758-714-6
AU - K. S.
AU - Majumder S.
AU - Khandelwal Y.
PY - 2024
SP - 50
EP - 57
DO - 10.5220/0012881200004519
PB - SciTePress