Automatic freezing-tolerant rapeseed material recognition using UAV images and deep learning

Lili Li; Jiangwei Qiao; Jian Yao; Jie Li; Li Li

doi:10.1186/s13007-022-00838-6

Automatic freezing-tolerant rapeseed material recognition using UAV images and deep learning

Plant Methods. 2022 Jan 13;18(1):5. doi: 10.1186/s13007-022-00838-6.

Authors

Lili Li¹, Jiangwei Qiao², Jian Yao^{1

3}, Jie Li⁴, Li Li⁵

Affiliations

¹ School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China.
² Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture and Rural Affairs, Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences, Wuhan, China.
³ GongQing Institute of Science and Technology, Jiujiang, Jiangxi, China.
⁴ School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan, China.
⁵ School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China. li.li@whu.edu.cn.

Abstract

Background: Freezing injury is a devastating yet common damage that occurs to winter rapeseed during the overwintering period which directly reduces the yield and causes heavy economic loss. Thus, it is an important and urgent task for crop breeders to find the freezing-tolerant rapeseed materials in the process of breeding. Existing large-scale freezing-tolerant rapeseed material recognition methods mainly rely on the field investigation conducted by the agricultural experts using some professional equipments. These methods are time-consuming, inefficient and laborious. In addition, the accuracy of these traditional methods depends heavily on the knowledge and experience of the experts.

Methods: To solve these problems of existing methods, we propose a low-cost freezing-tolerant rapeseed material recognition approach using deep learning and unmanned aerial vehicle (UAV) images captured by a consumer UAV. We formulate the problem of freezing-tolerant material recognition as a binary classification problem, which can be solved well using deep learning. The proposed method can automatically and efficiently recognize the freezing-tolerant rapeseed materials from a large number of crop candidates. To train the deep learning network, we first manually construct the real dataset using the UAV images of rapeseed materials captured by the DJI Phantom 4 Pro V2.0. Then, five classic deep learning networks (AlexNet, VGGNet16, ResNet18, ResNet50 and GoogLeNet) are selected to perform the freezing-tolerant rapeseed material recognition.

Result and conclusion: The accuracy of the five deep learning networks used in our work is all over 92%. Especially, ResNet50 provides the best accuracy (93.33[Formula: see text]) in this task. In addition, we also compare deep learning networks with traditional machine learning methods. The comparison results show that the deep learning-based methods significantly outperform the traditional machine learning-based methods in our task. The experimental results show that it is feasible to recognize the freezing-tolerant rapeseed using UAV images and deep learning.

Keywords: Deep learning; Freezing injury recognition; Machine learning; Rapeseed; UAV images.

Abstract

Grants and funding