A Wheat Spike Detection Method in UAV Images Based on Improved YOLOv5

Jianqing Zhao,Xiaohu Zhang,Weixing Cao,Xiaolei Qiu,Jiawei Yan,Yongchao Tian,Yan Zhu,Xia Yao

doi:10.3390/rs13163095

Jianqing Zhao, Xiaohu Zhang + Show 6 more

Open Access

https://doi.org/10.3390/rs13163095

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Abstract

Deep-learning-based object detection algorithms have significantly improved the performance of wheat spike detection. However, UAV images crowned with small-sized, highly dense, and overlapping spikes cause the accuracy to decrease for detection. This paper proposes an improved YOLOv5 (You Look Only Once)-based method to detect wheat spikes accurately in UAV images and solve spike error detection and miss detection caused by occlusion conditions. The proposed method introduces data cleaning and data augmentation to improve the generalization ability of the detection network. The network is rebuilt by adding a microscale detection layer, setting prior anchor boxes, and adapting the confidence loss function of the detection layer based on the IoU (Intersection over Union). These refinements improve the feature extraction for small-sized wheat spikes and lead to better detection accuracy. With the confidence weights, the detection boxes in multiresolution images are fused to increase the accuracy under occlusion conditions. The result shows that the proposed method is better than the existing object detection algorithms, such as Faster RCNN, Single Shot MultiBox Detector (SSD), RetinaNet, and standard YOLOv5. The average accuracy (AP) of wheat spike detection in UAV images is 94.1%, which is 10.8% higher than the standard YOLOv5. Thus, the proposed method is a practical way to handle the spike detection in complex field scenarios and provide technical references for field-level wheat phenotype monitoring.

Highlights

IntroductionPublisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
Compared with standard YOLOv5 and other general object detection methods, the proposed method based on improved YOLOv5 achieves the highest accuracy, with an average precision (AP)
We developed a wheat spike detection method based on the improved YOLOv5 for UAV images

Summary

Introduction

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Wheat is an important food crop in the world, with an annual global yield of about. Wheat is the foundation of world food security [1]. Biological and abiotic adversities have often occurred in the wheat production process in recent years, introducing many uncertainties to wheat yield formation. Using remote sensing to monitor the wheat growth process and predict yield has become a meaningful way to stabilize yield and optimize production management [2,3]. Assessing the production of wheat spikes as the grain-bearing organ is a valuable and practical measure of wheat yield [4,5]. Detecting wheat spikes from remote sensing images has received increased interest recently

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