Lightweight detection model of citrus leaf disease based on improved SSD

doi:10.3969/j.issn.1004-1524.20230159

Acta Agriculturae Zhejiangensis ›› 2024, Vol. 36 ›› Issue (3): 662-670.DOI: 10.3969/j.issn.1004-1524.20230159

• Biosystems Engineering • Previous Articles Next Articles

Lightweight detection model of citrus leaf disease based on improved SSD

LI Dahua¹^,²(), KONG Shu¹^,²^,^*(), LI Dong¹^,², YU Xiao¹^,²

1. College of Electrical and Electronic Engineering, Tianjin University of Technology, Tianjin 300380, China
2. Tianjin Key Laboratory of New Energy Power Conversion, Transmission and Intelligent Control, Tianjin 300380, China

Received:2023-02-14 Online:2024-03-25 Published:2024-04-09

Abstract

Abstract:

Aiming at the problems of large model proportion, slow detection speed, and low accuracy in the current target detection algorithm for citrus leaf disease, a lightweight detection method based on improved single shot multibox detector (SSD) for citrus leaf disease was proposed. MobileNetV2, a lightweight convolutional neural network, was introduced as the backbone of the SSD network to reduce the model size and improve the detection speed. The RFB (receptive field block) was introduced into the shallow prediction feature map to expand its receptive field, so as to improve the detection effect of the model on small targets. Additionally, the coordinate attention (CA) was introduced to strengthen feature information at different depths, further enhancing the recognition accuracy of citrus leaf disease. The results showed that compared with the VGG16-SSD network, the improved model (MR-CA-SSD) achieved an mean average precision (mAP) increase of 4.4 percentage points in citrus leaf disease detection, reduced the model proportion by 52.3 MB, and improved the frames per seconds by 3.15. The comprehensive performance of MR-CA-SSD also outperformed algorithms such as YOLOv4, CenterNet, and Efficientnet-YoloV3. This improved model could achieve rapid and accurate diagnosis of citrus leaf disease, contributing to timely and precise pesticide application for diseased areas.

Key words: citrus, leaf disease, lightweight network, receptive field block, attention mechanism

CLC Number:

S431

LI Dahua, KONG Shu, LI Dong, YU Xiao. Lightweight detection model of citrus leaf disease based on improved SSD[J]. Acta Agriculturae Zhejiangensis, 2024, 36(3): 662-670.

Figures/Tables 10

Fig.1 SSD network model structure Conv, Convolution; s, Stride.

Fig.2 MR-CA-SSD network model structure Conv, Convolution; CA, Coordinate attention; RFB, Receptive field block.

Fig.3 Inverted residual block structure

Fig.4 Structure of CA attention mechanism Avg Pool, Average pool; BatchNorm, Batch normalization; Non-linear, Non-linear classification; Sigmoid, Activation function; C, channel number; H, Height; W, Width.

Fig.5 RFB model structure

Fig.6 RFB-s model structure

Table 1 Results comparison before and after improvement of algorithm

模型 Model	mAP@0.5/%	mAP@0.5:0.95/%	F1值F1 score/%			模型大小 Size/MB	每秒检测帧数 Frames per seconds
模型 Model	mAP@0.5/%	mAP@0.5:0.95/%	炭疽病 Anthracnose	黑素病 Melanosis	褐斑病 Brown spot disease	模型大小 Size/MB	每秒检测帧数 Frames per seconds
SSD	92.7	59.4	57.0	94.0	89.0	91.6	54.65
MR-CA-SSD	97.1	66.5	84.0	97.0	86.0	39.3	57.80

Fig.7 Comparison of loss curve and mean average precision between MR-CA-SSD and original SSD model

Fig.8 Detection effect of MR-CA-SSD and original SSD model Disease-A, Disease-B and Disease-C represent anthracnose, melanosis and brown spot disease of citrus leaf, respectively.

Table 2 Comparison of results from different models

模型 Model	mAP@0.5/%	mAP@0.5:0.95/%	模型大小 Size/MB	每秒检测帧数 Frames per seconds
MR-CA-SSD	97.1	66.5	39.3	57.80
Faster R-CNN	88.1	36.3	108.0	11.24
YOLOv4	73.5	27.3	127.0	66.49
CenterNet	94.9	44.1	124.0	55.74
Efficientnet-YoloV3	92.9	51.2	60.7	35.03
Ghostnet-YoloV4	74.4	29.6	42.5	59.70
YoloV4-tiny	87.3	42.8	22.4	141.12

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Lightweight detection model of citrus leaf disease based on improved SSD

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