ArticleComputational intelligence and neuroscience2022
DCCAM-MRNet: Mixed Residual Connection Network with Dilated Convolution and Coordinate Attention Mechanism for Tomato Disease Identification.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 16 citations in OpenAlex.
- Precise leaf damage detection across diverse species and environments via a large-scale vision model.Frontiers in plant science · 2026Article
- Deep learning networks-based tomato disease and pest detection: a first review of research studies using real field datasets.Frontiers in plant science · 2024Review
- LightMixer: A novel lightweight convolutional neural network for tomato disease detection.Frontiers in plant science · 2023Article
- An Effective Image-Based Tomato Leaf Disease Segmentation Method Using MC-UNet.Plant phenomics (Washington, D.C.) · 2023Article
- Design of Drug Sales Forecasting Model Using Particle Swarm Optimization Neural Networks Model.Computational intelligence and neuroscience · 2022Article
- DA-ActNN-YOLOV5: Hybrid YOLO v5 Model with Data Augmentation and Activation of Compression Mechanism for Potato Disease Identification.Computational intelligence and neuroscience · 2022Article
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tomato is an important and fragile crop. During the course of its development, it is frequently contaminated with bacteria or viruses. Tomato leaf diseases may be detected quickly and accurately, resulting in increased productivity and quality. Because of the intricate development environment of tomatoes and their inconspicuous disease spot features and small spot area, present machine vision approaches fail to reliably recognize tomato leaves. As a result, this research proposes a novel paradigm for detecting tomato leaf disease. The INLM (integration nonlocal means) filtering algorithm, for example, decreases the interference of surrounding noise on the features. Then, utilizing ResNeXt50 as the backbone, we create DCCAM-MRNet, a novel tomato image recognition network. Dilated Convolution (DC) was employed in STAGE 1 of the DCCAM-MRNet to extend the network's perceptual area and locate the scattered disease spots on tomato leaves. The coordinate attention (CA) mechanism is then introduced to record cross-channel information and direction- and position-sensitive data, allowing the network to more accurately detect localized tomato disease spots. Finally, we offer a mixed residual connection (MRC) technique that combines residual block (RS-Block) and transformed residual block (TR-Block) (TRS-Block). This strategy can increase the network's accuracy while also reducing its size. The DCCAM-classification MRNet's accuracy is 94.3 percent, which is higher than the existing network, and the number of parameters is 0.11 M lesser than the backbone network ResNeXt50, according to the experimental results. As a result, combining INLM and DCCAM-MRNet to identify tomato diseases is a successful strategy.
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