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Rice Plant Counting, Locating, and Sizing Method Based on High-Throughput UAV RGB Images
Rice plant counting is crucial for many applications in rice production, such as yield estimation, growth diagnosis, disaster loss assessment, etc. Currently, rice counting still heavily relies on tedious and time-consuming manual operation. To alleviate the workload of rice counting, we employed an...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AAAS
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10076056/ https://www.ncbi.nlm.nih.gov/pubmed/37040495 http://dx.doi.org/10.34133/plantphenomics.0020 |
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author | Bai, Xiaodong Liu, Pichao Cao, Zhiguo Lu, Hao Xiong, Haipeng Yang, Aiping Cai, Zhe Wang, Jianjun Yao, Jianguo |
author_facet | Bai, Xiaodong Liu, Pichao Cao, Zhiguo Lu, Hao Xiong, Haipeng Yang, Aiping Cai, Zhe Wang, Jianjun Yao, Jianguo |
author_sort | Bai, Xiaodong |
collection | PubMed |
description | Rice plant counting is crucial for many applications in rice production, such as yield estimation, growth diagnosis, disaster loss assessment, etc. Currently, rice counting still heavily relies on tedious and time-consuming manual operation. To alleviate the workload of rice counting, we employed an UAV (unmanned aerial vehicle) to collect the RGB images of the paddy field. Then, we proposed a new rice plant counting, locating, and sizing method (RiceNet), which consists of one feature extractor frontend and 3 feature decoder modules, namely, density map estimator, plant location detector, and plant size estimator. In RiceNet, rice plant attention mechanism and positive–negative loss are designed to improve the ability to distinguish plants from background and the quality of the estimated density maps. To verify the validity of our method, we propose a new UAV-based rice counting dataset, which contains 355 images and 257,793 manual labeled points. Experiment results show that the mean absolute error and root mean square error of the proposed RiceNet are 8.6 and 11.2, respectively. Moreover, we validated the performance of our method with two other popular crop datasets. On these three datasets, our method significantly outperforms state-of-the-art methods. Results suggest that RiceNet can accurately and efficiently estimate the number of rice plants and replace the traditional manual method. |
format | Online Article Text |
id | pubmed-10076056 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | AAAS |
record_format | MEDLINE/PubMed |
spelling | pubmed-100760562023-04-06 Rice Plant Counting, Locating, and Sizing Method Based on High-Throughput UAV RGB Images Bai, Xiaodong Liu, Pichao Cao, Zhiguo Lu, Hao Xiong, Haipeng Yang, Aiping Cai, Zhe Wang, Jianjun Yao, Jianguo Plant Phenomics Research Article Rice plant counting is crucial for many applications in rice production, such as yield estimation, growth diagnosis, disaster loss assessment, etc. Currently, rice counting still heavily relies on tedious and time-consuming manual operation. To alleviate the workload of rice counting, we employed an UAV (unmanned aerial vehicle) to collect the RGB images of the paddy field. Then, we proposed a new rice plant counting, locating, and sizing method (RiceNet), which consists of one feature extractor frontend and 3 feature decoder modules, namely, density map estimator, plant location detector, and plant size estimator. In RiceNet, rice plant attention mechanism and positive–negative loss are designed to improve the ability to distinguish plants from background and the quality of the estimated density maps. To verify the validity of our method, we propose a new UAV-based rice counting dataset, which contains 355 images and 257,793 manual labeled points. Experiment results show that the mean absolute error and root mean square error of the proposed RiceNet are 8.6 and 11.2, respectively. Moreover, we validated the performance of our method with two other popular crop datasets. On these three datasets, our method significantly outperforms state-of-the-art methods. Results suggest that RiceNet can accurately and efficiently estimate the number of rice plants and replace the traditional manual method. AAAS 2023-01-30 2023 /pmc/articles/PMC10076056/ /pubmed/37040495 http://dx.doi.org/10.34133/plantphenomics.0020 Text en Copyright © 2023 Xiaodong Bai et al. https://creativecommons.org/licenses/by/4.0/Exclusive licensee Nanjing Agricultural University. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY 4.0) (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Research Article Bai, Xiaodong Liu, Pichao Cao, Zhiguo Lu, Hao Xiong, Haipeng Yang, Aiping Cai, Zhe Wang, Jianjun Yao, Jianguo Rice Plant Counting, Locating, and Sizing Method Based on High-Throughput UAV RGB Images |
title | Rice Plant Counting, Locating, and Sizing Method Based on High-Throughput UAV RGB Images |
title_full | Rice Plant Counting, Locating, and Sizing Method Based on High-Throughput UAV RGB Images |
title_fullStr | Rice Plant Counting, Locating, and Sizing Method Based on High-Throughput UAV RGB Images |
title_full_unstemmed | Rice Plant Counting, Locating, and Sizing Method Based on High-Throughput UAV RGB Images |
title_short | Rice Plant Counting, Locating, and Sizing Method Based on High-Throughput UAV RGB Images |
title_sort | rice plant counting, locating, and sizing method based on high-throughput uav rgb images |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10076056/ https://www.ncbi.nlm.nih.gov/pubmed/37040495 http://dx.doi.org/10.34133/plantphenomics.0020 |
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