Detection and Classification Rice Plant Quality Through UAV Imagery Using Yolo V5 Algorithm

Adi Suheryadu, A. Sumarudin, Alifia Puspaningrum, Renold N. K. Natasasmita

2022

Abstract

Smart farming is an important technology in supporting agriculture 4.0. Farmers can solve some problems by utilizing this smart farm, including monitoring crops in agricultural areas. The large area of agricultural land owned by farmers makes it difficult for farmers to monitor the quality of rice crops on their land. In overcoming this problem, an intelligent system is needed to detect the quality level of rice crops and classify them appropriately, and the scope of detection is broad. By detecting and classifying rice plants using one of the artificial intelligence methods, namely YOLO, farmers can find out the quality of their rice plants through images taken using UAVs. For this YOLO algorithm to detect the quality level of rice plants from each field, datasets taken through aerial imagery or drone technology are needed, these datasets will be used to train models to detect the quality level of rice plants. The YOLO algorithm can see the quality level of rice crops based on the point of interest in the image uploaded by the farmer to the server. Images processed using the YOLO algorithm produce output from bounding boxes and confidence scores for each detected object. The yield of mAP@0.5 (mean average precision) was 93.69%.

Paper Citation

in Harvard Style

Suheryadu A., Sumarudin A., Puspaningrum A. and N. K. Natasasmita R. (2022). Detection and Classification Rice Plant Quality Through UAV Imagery Using Yolo V5 Algorithm. In Proceedings of the 5th International Conference on Applied Science and Technology on Engineering Science - Volume 1: iCAST-ES; ISBN 978-989-758-619-4, SciTePress, pages 577-581. DOI: 10.5220/0011843200003575

in Bibtex Style

@conference{icast-es22,
author={Adi Suheryadu and A. Sumarudin and Alifia Puspaningrum and Renold N. K. Natasasmita},
title={Detection and Classification Rice Plant Quality Through UAV Imagery Using Yolo V5 Algorithm},
booktitle={Proceedings of the 5th International Conference on Applied Science and Technology on Engineering Science - Volume 1: iCAST-ES},
year={2022},
pages={577-581},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011843200003575},
isbn={978-989-758-619-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 5th International Conference on Applied Science and Technology on Engineering Science - Volume 1: iCAST-ES
TI - Detection and Classification Rice Plant Quality Through UAV Imagery Using Yolo V5 Algorithm
SN - 978-989-758-619-4
AU - Suheryadu A.
AU - Sumarudin A.
AU - Puspaningrum A.
AU - N. K. Natasasmita R.
PY - 2022
SP - 577
EP - 581
DO - 10.5220/0011843200003575
PB - SciTePress