Plant disease and pest detection based on visiual attention enhancement

doi:10.12302/j.issn.1000-2006.202210022

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2023, Vol. 47 ›› Issue (3): 11-18.doi: 10.12302/j.issn.1000-2006.202210022

Special Issue: 第三届中国林草计算机应用大会论文精选

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Plant disease and pest detection based on visiual attention enhancement

YANG Kun(), FAN Xijian(), BO Weihao, LIU Jie, WANG Junling

School of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China

Received:2022-10-16 Revised:2023-02-20 Online:2023-05-30 Published:2023-05-25
Contact: FAN Xijian E-mail:1309126732@qq.com;xijian.fan@njfu.edu.cn

Abstract

Abstract:

【Objective】 Accurate detection is the key to precise control of plant diseases and pests. Building an accurate and efficient monitoring model of plant diseases and pests provides an important basis for the early diagnosis and warning of plant diseases and pests.【Method】In view of the weak generalization ability of the existing plant diseases and pests detection models and the high rate of missed detection of small targets, a plant disease and pest detection model based on visual attention enhancement improvement YOLOv 5-VE (vision enhancement) was proposed. The Mosaic 9 data enhancement method was used to facilitate the detection of small targets in experimental samples; a feature enhancement module based on a visual attention convolutional block attention module (CBAM) was designed; to determine the location loss of overlapping and occluded targets, the DIoU bounding box location loss function was introduced.【Result】The recognition and average detection accuracies of the YOLOv 5-VE model on the experimental dataset reached 65.87% and 73.49%, respectively, which were 1.07% and 8.25% higher, respectively, than those of the original model. The detection speed on the GPU with the model 1 080 Ti reached 35 fps. 【Conclusion】This method can quickly and effectively detect and identify a variety of diseases and pests in field scenes with complex backgrounds, improve robustness of detection, improve the feature extraction ability of the model for pests and diseases, reduce the interference of complex field scenes in detection, and shows good application potential. It can be widely used for the large-scale detection of plant diseases and pests.

Key words: detection of plant diseases and pests, object detection, attention mechanism, data augmentation

CLC Number:

TP3
S763

YANG Kun, FAN Xijian, BO Weihao, LIU Jie, WANG Junling. Plant disease and pest detection based on visiual attention enhancement[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2023, 47(3): 11-18.

Figures/Tables 10

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References 23

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类型 type	模型 model	参数大小/ MB parameter size	输入大小/pixel input size	查准率/% precision	查全率/% recall	阈值大于0.5 的平均准确度/% m_{AP_0.5}	F₁/%	单张检测耗时/ms detection time
原模型 original model	YOLOv 3	237.73	640	52.58	59.12	62.17	55.66	22.75
	YOLOv 4	136.43	640	21.29	26.42	39.45	23.58	14.91
	YOLOv 5	174.77	640	64.80	59.39	65.24	61.98	36.90
	YOLO X	378.82	640	33.61	42.15	42.78	37.40	7.52
	YOLOv 5-VE	194.77	640	65.87	68.39	73.49	67.11	35.46
改进模型 improved model	YOLOv 5	174.77	640	64.80	59.39	65.24	61.98	36.90
	+CBAM	194.77	640	62.72	68.90	69.61	65.66	34.36
	+CBAM+Mosaic 9	194.77	640	66.27	67.30	70.69	66.78	35.34
	YOLOv 5-VE	194.77	640	65.87	68.39	73.49	67.11	35.46

Plant disease and pest detection based on visiual attention enhancement

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