Tea Leaf Disease Diagnosis Based on Improved Lightweight U-Net3+

doi:10.12133/j.smartag.SA202507010

Abstract

Abstract:

[Objective] Leaf diseases significantly affect both the yield and quality of tea throughout the year. To address the issue of inadequate segmentation finesse in the current tea spot segmentation models, a novel diagnosis of the severity of tea spots was proposed in this research, designated as MDC-U-Net3+, to enhance segmentation accuracy on the base framework of U-Net3+. [Methods] Multi-scale feature fusion module (MSFFM) was incorporated into the backbone network of U-Net3+ to obtain feature information across multiple receptive fields of diseased spots, thereby reducing the loss of features within the encoder. Dual multi-scale attention (DMSA) was incorporated into the skip connection process to mitigate the segmentation boundary ambiguity issue. This integration facilitates the comprehensive fusion of fine-grained and coarse-grained semantic information at full scale. Furthermore, the segmented mask image was subjected to conditional random fields (CRF) to enhance the optimization of the segmentation results [Results and Discussions] The improved model MDC-U-Net3+ achieved a mean pixel accuracy (mPA) of 94.92%, accompanied by a mean Intersection over Union (mIoU) ratio of 90.9%. When compared to the mPA and mIoU of U-Net3+, MDC-U-Net3+ model showed improvements of 1.85 and 2.12 percentage points, respectively. These results illustrated a more effective segmentation performance than that achieved by other classical semantic segmentation models. [Conclusions] The methodology presented herein could provide data support for automated disease detection and precise medication, consequently reducing the losses associated with tea diseases.

Key words: disease diagnosis, semantic segmentation, U-Net3+, multi-scale feature fusion, attention mechanism, conditional random fields

CLC Number:

TP183

HU Yumeng, GUAN Feifan, XIE Dongchen, MA Ping, YU Youben, ZHOU Jie, NIE Yanming, HUANG Lüwen. Tea Leaf Disease Diagnosis Based on Improved Lightweight U-Net3+[J]. Smart Agriculture, 2026, 8(1): 15-27.

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Disease types	Pre-augmentation	Post-augmentation
Tea anthracnose （Gloeosporium theae-sinensis Miyake）	428	2 568
Tea leaf blight （Colletotrichum camelliae Massee）	375	2 250
Tea red scab （Cercospora theae BreadaDe Haan）	486	2 916
Tea blister blight （Exobasidium vexans Massee）	469	2 814
Tea red leaf spot （Phyllosticta theicola Petch）	435	2 610

Disease severity	Classification standard of leaf
Disease severity	Area of leaf spots	Number of leaf spots
0	none	0
1	less than 25% of the leaf area	1
2	25% to 50% of the leaf area	2
3	50% to 75% of the leaf area	3
4	more than 75% of the leaf area	4

Dataset division ratios	mPA/%	mIoU/%	Precision/%	Dice/%
8∶1∶1	93.73	89.20	94.63	93.78
7∶2∶1	94.20	90.26	95.28	94.29
7∶1∶2	94.90	90.88	95.24	94.56
6∶2∶2	94.92	90.90	95.24	94.58

Backbone network	mPA/%	mIoU/%
VGG16	93.35	88.84
VGG19	92.71	89.04
ResNet50	93.07	88.78
ResNet101	93.69	88.92
5-layer MSFFM	94.29	89.28

Model	mPA/%	mIoU/%	Precision/%	Dice/%	Frame rate/（f/s）	Average inference time/ms	Model size/MB
DeepLab V3+	88.59	84.38	94.67	89.85	22.476 2	44.898 6	10.987 4
PSPNet	91.91	84.81	86.71	88.78	9.601 5	44.499 6	9.601 5
U-Net	92.50	86.71	92.55	91.50	24.568 7	38.632 2	95.031 1
U-Net3+	93.07	88.78	94.59	92.96	20.633 6	48.576 1	143.031 8
MDC-U-Net3+	94.92	90.90	95.24	94.58	14.173 4	68.313 2	308.483 8