ArticlePlant phenomics (Washington, D.C.)2023
An Effective Image-Based Tomato Leaf Disease Segmentation Method Using MC-UNet.
Article in Plant phenomics (Washington, D.C.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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33 citing papers in PubMed, 81 citations in OpenAlex.
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- Precise leaf damage detection across diverse species and environments via a large-scale vision model.Frontiers in plant science · 2026Article
- DU-Net-L: an effective and lightweight segmentation model for alfalfa images that integrates the strengths of DeepLabV3+ and U-Net.aBIOTECH · 2025Article
- High-performance parallel multi-scale attention network with explainable AI for intelligent diagnosis of leaf diseases in agricultural systems.Scientific reports · 2025Article
- Depth Imaging-Based Framework for Efficient Phenotypic Recognition in Tomato Fruit.Plants (Basel, Switzerland) · 2025Article
- Detection of Pine Wilt Disease in UAV Remote Sensing Images Based on SLMW-Net.Plants (Basel, Switzerland) · 2025Article
- KBNet: A Language and Vision Fusion Multi-Modal Framework for Rice Disease Segmentation.Plants (Basel, Switzerland) · 2025Article
- Interpretable multitask deep learning model for detecting and analyzing severity of rice bacterial leaf blight.Scientific reports · 2025Article
- Enhanced Leaf Disease Segmentation Using U-Net Architecture for Precision Agriculture: A Deep Learning Approach.Food science & nutrition · 2025Article
- CMSAF-Net: integrative network design with enhanced decoder for precision segmentation of pear leaf diseases.Plant methods · 2025Article
- Evaporative cooling signals for wound healing in plants.bioRxiv : the preprint server for biology · 2025Article
- FFAE-UNet: An Efficient Pear Leaf Disease Segmentation Network Based on U-Shaped Architecture.Sensors (Basel, Switzerland) · 2025Article
- CBSNet: An Effective Method for Potato Leaf Disease Classification.Plants (Basel, Switzerland) · 2025Article
- MD-Unet for tobacco leaf disease spot segmentation based on multi-scale residual dilated convolutions.Scientific reports · 2025Article
- Detection techniques for tomato diseases under non-stationary climatic conditions.Frontiers in plant science · 2025Article
- PMJDM: a multi-task joint detection model for plant disease identification.Frontiers in plant science · 2025Article
- A Multi-Source Circular Geodesic Voting Model for Image Segmentation.Entropy (Basel, Switzerland) · 2024Article
- A Precise Segmentation Algorithm of Pumpkin Seedling Point Cloud Stem Based on CPHNet.Plants (Basel, Switzerland) · 2024Article
- ICPNet: Advanced Maize Leaf Disease Detection with Multidimensional Attention and Coordinate Depthwise Convolution.Plants (Basel, Switzerland) · 2024Article
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tomato disease control is an urgent requirement in the field of intellectual agriculture, and one of the keys to it is quantitative identification and precise segmentation of tomato leaf diseases. Some diseased areas on tomato leaves are tiny and may go unnoticed during segmentation. Blurred edge also makes the segmentation accuracy poor. Based on UNet, we propose an effective image-based tomato leaf disease segmentation method called Cross-layer Attention Fusion Mechanism combined with Multi-scale Convolution Module (MC-UNet). First, a Multi-scale Convolution Module is proposed. This module obtains multiscale information about tomato disease by employing 3 convolution kernels of different sizes, and it highlights the edge feature information of tomato disease using the Squeeze-and-Excitation Module. Second, a Cross-layer Attention Fusion Mechanism is proposed. This mechanism highlights tomato leaf disease locations via gating structure and fusion operation. Then, we employ SoftPool rather than MaxPool to retain valid information on tomato leaves. Finally, we use the SeLU function appropriately to avoid network neuron dropout. We compared MC-UNet to the existing segmentation network on our self-built tomato leaf disease segmentation dataset and MC-UNet achieved 91.32% accuracy and 6.67M parameters. Our method achieves good results for tomato leaf disease segmentation, which demonstrates the effectiveness of the proposed methods.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.