Research on Tea Trees Germination Density Detection Based on Improved YOLOv5

Abstract

Tea plants are one of the most widely planted agricultural crops in the world. The traditional method of surveying germination density is mainly manual checking, which is time-consuming and inefficient. In this research, the Improved YOLOv5 model was used to identify tea buds and detect germination density based on tea trees canopy visible images. Firstly, five original YOLOv5 models were trained for tea trees germination recognition, and performance and volume were compared. Secondly, backbone structure was redesigned based on the lightweight theory of Xception and ShuffleNetV2. Meanwhile, reverse attention mechanism (RA) and receptive field block (RFB) were added to enhance the network feature extraction ability, achieving the purpose of optimizing the YOLOv5 network from both lightweight and accuracy improvement. Finally, the recognition ability of the Improved YOLOv5 model was analyzed, and the germination density of tea trees was detected according to the tea bud count. The experimental results show that: (1) The parameter numbers of the five original YOLOv5 models were inversely proportional to the detection accuracy. The YOLOv5m model with the most balanced comprehensive performance contained 20,852,934 parameters, the precision rate of the YOLOv5m recognition model was 74.9%, the recall rate was 75.7%, and the mAP_0.5 was 0.758. (2) The Improved YOLOv5 model contained 4,326,815 parameters, the precision rate of the Improved YOLOv5 recognition model was 94.9%, the recall rate was 97.67%, and the mAP_0.5 was 0.758. (3) The YOLOv5m model and the Improved YOLOv5 model were used to test the validation set, and the true positive (TP) values identified were 86% and 94%, respectively. The Improved YOLOv5 network model was effectively improved in both volume and accuracy according to the result. This research is conducive to scientific planning of tea bud picking, improving the production efficiency of the tea plantation and the quality of tea production in the later stage.

Detection of the Metcalfa pruinosa (Hemiptera: Flatidae) pest on the Jujube plant (Ziziphus jujuba) using a sequence of YOLOv5 models

“…They achieved an average recognition accuracy of 96% using the YOLOv5 model. Wang et al (2023) employed a YOLOv5-based algorithm to detect diseases (Leaf blight) and pests (Apolygus lucorum) in tea leaves and obtained positive results. Yang et al (2023) examined nine different diseases and pests in rice plants using a YOLOv5-based algorithm.…”

TS-YOLO: An All-Day and Lightweight Tea Canopy Shoots Detection Model

“…In the practical tea field, however, these conditions are often not met. Among the deep learning methods, Zhu et al [18], Wang et al [48], Li et al [49], Wang et al [50], and Chen et al [51] used Faster RCNN, Mask RCNN, YOLOV3, YOLOv5, and so on, to detect the tea shoots, respectively. Although its detection results are better and the robustness to complex field environments are higher, the large model size and slow inference speed are not suitable to be deployed on movable platforms for the real-time detection of tea canopy shoots.…”

A review on the application of computer vision and machine learning in the tea industry

“…area to reflect the growth status, and there is the problem of tea trees not sprouting but still being able to obtain high growth values by the large canopy. Wang et al (2022) proposed an improved YOLOv5 algorithm to implement a method to detect the number of tea buds at the top of the tree canopy. Since the photographed canopy's field of view remains unchanged, tea buds' growth density can be calculated.…”