An Enhanced Lightweight UNet3+ for Tea Leaf Disease Diagnosis

doi:10.12133/j.smartag.SA202507010

Abstract

Abstract:

[Objective] Tea 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 was proposed of the severity of tea spots, designated as MDC-UNet3+. [Methods] This model composed the Multi-scale Feature Fusion Module (MSFFM), dual multi-scale attention (DMSA), and conditional random fields (CRF) to enhance segmentation accuracy on the base framework of UNet3+. MSFFM was incorporated into the backbone network of UNet3+ to obtain feature information across multiple receptive fields of diseased spots, thereby reducing the loss of features within the encoder. 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 CRF to enhance the optimization of the segmentation results. [Results and Discussions] It has been established that the enhanced model achieves 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 UNet3+, this model showed improvements of 1.85 and 2.12 percentage points, respectively. These results illustrate a more effective segmentation performance than that achieved by other classical semantic segmentation models. [Conclusions] The methodology presented herein provides valuable data support for automated disease detection and precise medication, consequently reducing the losses associated with tea diseases.

Key words: disease identification, 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 Lyuwen. An Enhanced Lightweight UNet3+ for Tea Leaf Disease Diagnosis[J]. Smart Agriculture, doi: 10.12133/j.smartag.SA202507010.

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Disease types	Pre-enhancement	Post-enhancement
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 Masse）	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 / %	FPS / （img/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
UNet3+	93.07	88.78	94.59	92.96	20.633 6	48.576 1	143.031 8
MDC-UNet3+	94.92	90.90	95.24	94.58	14.173 4	68.313 2	308.483 8