WSVAS: A YOLOv4 -based phenotyping platform for automatically detecting the salt tolerance of wheat based on seed germination vigour

Xiuqing Fu; Bing Han; Shouyang Liu; Jiayi Zhou; Hongwen Zhang; Hongbiao Wang; Hui Zhang; Zhiqian Ouyang

doi:10.3389/fpls.2022.1074360

WSVAS: A YOLOv4 -based phenotyping platform for automatically detecting the salt tolerance of wheat based on seed germination vigour

Front Plant Sci. 2022 Dec 20:13:1074360. doi: 10.3389/fpls.2022.1074360. eCollection 2022.

Authors

Xiuqing Fu^{1

2}, Bing Han¹, Shouyang Liu³, Jiayi Zhou¹, Hongwen Zhang⁴, Hongbiao Wang⁵, Hui Zhang¹, Zhiqian Ouyang¹

Affiliations

¹ College of Engineering, Nanjing Agricultural University, Nanjing, China.
² Key laboratory of Intelligence Agricultural Equipment of Jiangsu Province, Education Department of Jiangsu Province and is managed by the College of Engineering of Nanjing Agricultural University, Nanjing, China.
³ Academy For Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing, China.
⁴ School of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China.
⁵ College of Mechanical and Electrical Engineering, Tarim University, Alar, China.

Abstract

Salt stress is one of the major environmental stress factors that affect and limit wheat production worldwide. Therefore, properly evaluating wheat genotypes during the germination stage could be one of the effective ways to improve yield. Currently, phenotypic identification platforms are widely used in the seed breeding process, which can improve the speed of detection compared with traditional methods. We developed the Wheat Seed Vigour Assessment System (WSVAS), which enables rapid and accurate detection of wheat seed germination using the lightweight convolutional neural network YOLOv4. The WSVAS system can automatically acquire, process and analyse image data of wheat varieties to evaluate the response of wheat seeds to salt stress under controlled environments. The WSVAS image acquisition system was set up to continuously acquire images of seeds of four wheat varieties under three types of salt stress. In this paper, we verified the accuracy of WSVAS by comparing manual scoring. The cumulative germination curves of wheat seeds of four genotypes under three salt stresses were also investigated. In this study, we compared three models, VGG16 + Faster R-CNN, ResNet50 + Faster R-CNN and YOLOv4. We found that YOLOv4 was the best model for wheat seed germination target detection, and the results showed that the model achieved an average detection accuracy (mAP) of 97.59%, a recall rate (Recall) of 97.35% and the detection speed was up to 6.82 FPS. This proved that the model could effectively detect the number of germinating seeds in wheat. In addition, the germination rate and germination index of the two indicators were highly correlated with germination vigour, indicating significant differences in salt tolerance amongst wheat varieties. WSVAS can quantify plant stress caused by salt stress and provides a powerful tool for salt-tolerant wheat breeding.

Keywords: convolutional neural network; germination percentage; phenotypic platform; salt tolerance; wheat seeds.