Estimating Maize-Leaf Coverage in Field Conditions by Applying a Machine Learning Algorithm to UAV Remote Sensing Images

Applied Sciences. 2019;9(11):2389 DOI 10.3390/app9112389

 

Journal Homepage

Journal Title: Applied Sciences

ISSN: 2076-3417 (Print)

Publisher: MDPI AG

LCC Subject Category: Technology: Engineering (General). Civil engineering (General) | Science: Biology (General) | Science: Physics | Science: Chemistry

Country of publisher: Switzerland

Language of fulltext: English

Full-text formats available: PDF, HTML

 

AUTHORS

Chengquan Zhou (Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences (ZAAS), Zhejiang 310000, China)
Hongbao Ye (Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences (ZAAS), Zhejiang 310000, China)
Zhifu Xu (Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences (ZAAS), Zhejiang 310000, China)
Jun Hu (Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences (ZAAS), Zhejiang 310000, China)
Xiaoyan Shi (Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences (ZAAS), Zhejiang 310000, China)
Shan Hua (Institute of Agricultural Equipment, Zhejiang Academy of Agricultural Sciences (ZAAS), Zhejiang 310000, China)
Jibo Yue (Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture P. R. China, Beijing Research Center for Information Technology in Agriculture, Beijing 100089, China)
Guijun Yang (Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture P. R. China, Beijing Research Center for Information Technology in Agriculture, Beijing 100089, China)

EDITORIAL INFORMATION

Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 11 weeks

 

Abstract | Full Text

Leaf coverage is an indicator of plant growth rate and predicted yield, and thus it is crucial to plant-breeding research. Robust image segmentation of leaf coverage from remote-sensing images acquired by unmanned aerial vehicles (UAVs) in varying environments can be directly used for large-scale coverage estimation, and is a key component of high-throughput field phenotyping. We thus propose an image-segmentation method based on machine learning to extract relatively accurate coverage information from the orthophoto generated after preprocessing. The image analysis pipeline, including dataset augmenting, removing background, classifier training and noise reduction, generates a set of binary masks to obtain leaf coverage from the image. We compare the proposed method with three conventional methods (Hue-Saturation-Value, edge-detection-based algorithm, random forest) and a frontier deep-learning method called DeepLabv3+. The proposed method improves indicators such as <i>Q<sub>seg</sub></i>, <i>S<sub>r</sub></i>, <i>E<sub>s</sub></i> and mIOU by 15% to 30%. The experimental results show that this approach is less limited by radiation conditions, and that the protocol can easily be implemented for extensive sampling at low cost. As a result, with the proposed method, we recommend using red-green-blue (RGB)-based technology in addition to conventional equipment for acquiring the leaf coverage of agricultural crops.