Evidence map›Paper›PMID 34728745›Full record

ArticleScientific reports2021

Estimating and evaluating the rice cluster distribution uniformity with UAV-based images.

Xiaohui Wang, Qiyuan Tang, Zhaozhong Chen, Youyi Luo, Hongyu Fu, Xumeng Li

Abstract readEvaluation Study
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Xiaohui WangCollege of Agriculture, Hunan Agricultural University, Changsha, 410128, People's Republic of China.
Qiyuan TangCollege of Agriculture, Hunan Agricultural University, Changsha, 410128, People's Republic of China. 1623729082@qq.com.
Zhaozhong ChenCollege of Agriculture, Hunan Agricultural University, Changsha, 410128, People's Republic of China.
Youyi LuoCollege of Agriculture, Hunan Agricultural University, Changsha, 410128, People's Republic of China.
Hongyu FuCollege of Agriculture, Hunan Agricultural University, Changsha, 410128, People's Republic of China.
Xumeng LiCollege of Agriculture, Hunan Agricultural University, Changsha, 410128, People's Republic of China. xm.li@hunau.edu.cn.

Funding

Crystallin Genes--structure, Organization, Expression, AZ01AR000126 · NIAMS · ARTHRITIS, MUSCULOSKELETAL, SKIN DIS · PI PIATIGORSKY, JORAM · 2005 to 2005
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Key scientific research project of Hunan Education Department 18A115National Key Research and Development Program of China 2017YFD0301507Natural Science Foundation of Hunan Province 2018JJ3227the earmarked fund for China Agriculture Research System CARS-01-26the Key Research and Development Program of Hunan Province 2020NK2025
6 · The paper itself

Abstract

The uniformity of the rice cluster distribution in the field affects population quality and the precise management of pesticides and fertilizers. However, there is no appropriate technical system for estimating and evaluating the uniformity at present. For that reason, a method based on unmanned aerial vehicle (UAV images) is proposed to estimate and evaluate the uniformity in this present study. This method includes rice cluster recognition and location determination based on the RGB color characteristics of the seedlings of aerial images, region segmentation considering the rice clusters based on Voronoi Diagram, and uniformity index definition for evaluating the rice cluster distribution based on the variation coefficient. The results indicate the rice cluster recognition attains a high precision, with the precision, accuracy, recall, and F1-score of rice cluster recognition reaching > 95%, 97%, 97%, 95%, and 96%, respectively. The rice cluster location error is small and obeys the gamma (3.00, 0.54) distribution (mean error, 1.62 cm). The uniformity index is reasonable for evaluating the rice cluster distribution verified via simulation. As a whole process, the estimating method is sufficiently high accuracy with relative error less than 0.01% over the manual labeling method. Therefore, this method based on UAV images is feasible, convenient, technologically advanced, inexpensive, and highly precision for the estimation and evaluation of the rice cluster distribution uniformity. However, the evaluation application indicates that there is much room for improvement in terms of the uniformity of mechanized paddy field transplanting in South China.

Indexed as

Image Processing, Computer-AssistedOryzaRemote Sensing TechnologySeedlingsStatistical DistributionsUnmanned Aerial Devices

Identifiers

PMID34728745
PMCPMC8563992

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