Precise Recognition Method of Cotton Top Buds Based on YOLOv4 Network

doi:10.13304/j.nykjdb.2021.0536

Abstract

Abstract:

In order to achieve non-contact， low-cost accurate detection of cotton top buds， this paper proposed a recognition method based on the YOLOv4 network to detect the cotton top buds in a complex environment. The K-means algorithm was used to cluster the cotton top buds data set， and the anchors was optimized to improve the precision and speed of network detection， and the optimal model was selected through analysis. Compared with the performance of model before and after optimization， and the optimal weight model was compared with other object detection models in the detection performance of cotton top buds. The results showed that the average precision （AP） of optimal model in the test set， precision （P）， recall （R）， F₁ value of the optimize model increased 0.36%， 1.73%， 0.52%， 1.16% compared with the original YOLOv4 model， and the average detection time of a single image shortened 0.28 s； compared with SSD， YOLOv3， Tiny-YOLOV4 models， the optimize model had the highest F₁ value and recognition precision， and its performance was more balanced. The top buds at a backlight state in natural scenes had no effect on model detection， and the detection accuracy of each model decreased at different degrees under occlusion conditions.

Key words: cotton topping, YOLOv4, deep learning, K-means, image recognition

摘要：

为实现非接触、低成本、精准识别棉花顶芽，提出一种基于YOLOv4网络在复杂环境下对棉花顶芽进行精准识别的方法。利用K-means算法对棉花顶芽数据集进行聚类，优化先验框改善网络检测精度和速度，得到最优权值模型。对聚类前后模型以及与其他目标检测模型在棉花顶芽检测性能上进行了对比试验，并探究了顶芽在逆光和遮挡环境下，不同模型的检测性能。结果表明：该模型在测试集的平均检测精度（AP）、精确率（P）、召回率（R）、调和平均值（F₁）比原模型分别提高0.36%、1.73%、0.52%、1.16%，单张图像平均检测时间缩短0.28 s；对比SSD、YOLOv3、Tiny-YOLOV4模型，该模型检测精确率和F₁值最高，性能均衡；在自然场景处于逆光状态下，YOLOv4模型检测顶芽效果好于其他模型，且逆光环境对检测影响小；在遮挡条件下各个模型检测精度均有不同程度下降。

关键词: 棉花顶芽, YOLOv4, 深度学习, K-means, 图像识别

CLC Number:

S126

Haitao LIU, Xin HAN, Yubin LAN, Lili YI, Baoju WANG, Lihua CUI. Precise Recognition Method of Cotton Top Buds Based on YOLOv4 Network[J]. Journal of Agricultural Science and Technology, 2022, 24(8): 99-108.

刘海涛, 韩鑫, 兰玉彬, 伊丽丽, 王宝聚, 崔立华. 基于YOLOv4网络的棉花顶芽精准识别方法[J]. 中国农业科技导报, 2022, 24(8): 99-108.

Figures/Tables 12

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光照 Light	植株状态 Plant status	图像数量 Number of images
晴天 Sunny	单株 Single plant	1 538
	多株 Multiple plants	429
	遮挡 Shelter	170
阴天 Cloudy	单株 Single plant	318
	多株 Multiple plants	24
	遮挡 Shelter	67
雨天 Rainy	单株 Single plant	254
	多株 Multiple plants	25
	遮挡 Shelter	75
总计Total		2 900

光照 Light	植株状态 Plant status	图像数量 Number of images
晴天 Sunny	单株 Single plant	1 538
	多株 Multiple plants	429
	遮挡 Shelter	170
阴天 Cloudy	单株 Single plant	318
	多株 Multiple plants	24
	遮挡 Shelter	67
雨天 Rainy	单株 Single plant	254
	多株 Multiple plants	25
	遮挡 Shelter	75
总计Total		2 900

模型Model	精确率 Precision/%	召回率 Recall/%	调和平均值 F₁/%	平均精度 Average precision/%
1	87.82	99.30	93.21	98.86
2	92.52	99.43	95.85	99.23
3	92.14	99.36	95.61	99.16
4	94.21	99.62	96.84	99.40
5	92.15	99.55	95.71	99.38
6	90.17	99.36	94.54	99.15
7	91.75	99.24	95.35	99.05
8	90.92	99.62	95.07	99.44
9	92.73	99.17	95.84	98.90

模型Model	精确率 Precision/%	召回率 Recall/%	调和平均值 F₁/%	平均精度 Average precision/%
1	87.82	99.30	93.21	98.86
2	92.52	99.43	95.85	99.23
3	92.14	99.36	95.61	99.16
4	94.21	99.62	96.84	99.40
5	92.15	99.55	95.71	99.38
6	90.17	99.36	94.54	99.15
7	91.75	99.24	95.35	99.05
8	90.92	99.62	95.07	99.44
9	92.73	99.17	95.84	98.90

模型 Model	精确率 Precision/%	召回率 Recall/%	调和平均值 F₁/%	平均精度 Average precision/%	检测时间 Time/s
YOLOv4	94.21	99.62	96.84	99.40	0.65
YOLOv3	93.73	99.23	96.40	99.17	0.84
Tiny-YOLOv4	72.56	99.70	83.99	99.48	0.53
SSD	72.03	99.80	83.67	99.51	0.78