DCCAM-MRNet: Mixed Residual Connection Network with Dilated Convolution and Coordinate Attention Mechanism for Tomato Disease Identification

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\u27s 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\u27s accuracy while also reducing its size. The DCCAM-classification MRNet\u27s 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

DS-MENet for the Classification of Citrus Disease

Affected by various environmental factors, citrus will frequently suffer from diseases during the growth process, which has brought huge obstacles to the development of agriculture. This paper proposes a new method for identifying and classifying citrus diseases. Firstly, this paper designs an image enhancement method based on the MSRCR algorithm and homomorphic filtering algorithm optimized by Laplacian (HFLF-MS) to highlight the disease characteristics of citrus. Secondly, we designed a new neural network DS-MENet based on the DenseNet-121 backbone structure. In DS-MENet, the regular convolution in Dense Block is replaced with depthwise separable convolution, which reduces the network parameters. The ReMish activation function is used to alleviate the neuron death problem caused by the ReLU function and improve the robustness of the model. To further enhance the attention to citrus disease information and the ability to extract feature information, a multi-channel fusion backbone enhancement method (MCF) was designed in this work to process Dense Block. We use the 10-fold cross-validation method to conduct experiments. The average classification accuracy of DS-MENet on the dataset after adding noise can reach 95.02%. This shows that the method has good performance and has certain feasibility for the classification of citrus diseases in real life

DS-MENet for the Classification of Citrus Disease

Affected by various environmental factors, citrus will frequently suffer from diseases during the growth process, which has brought huge obstacles to the development of agriculture. This paper proposes a new method for identifying and classifying citrus diseases. Firstly, this paper designs an image enhancement method based on the MSRCR algorithm and homomorphic filtering algorithm optimized by Laplacian (HFLF-MS) to highlight the disease characteristics of citrus. Secondly, we designed a new neural network DS-MENet based on the DenseNet-121 backbone structure. In DS-MENet, the regular convolution in Dense Block is replaced with depthwise separable convolution, which reduces the network parameters. The ReMish activation function is used to alleviate the neuron death problem caused by the ReLU function and improve the robustness of the model. To further enhance the attention to citrus disease information and the ability to extract feature information, a multi-channel fusion backbone enhancement method (MCF) was designed in this work to process Dense Block. We use the 10-fold cross-validation method to conduct experiments. The average classification accuracy of DS-MENet on the dataset after adding noise can reach 95.02%. This shows that the method has good performance and has certain feasibility for the classification of citrus diseases in real life

CASM-AMFMNet: A Network based on Coordinate Attention Shuffle Mechanism and Asymmetric Multi-Scale Fusion Module for Classification of Grape Leaf Diseases

Grape disease is a significant contributory factor to the decline in grape yield, typically affecting the leaves first. Efficient identification of grape leaf diseases remains a critical unmet need. To mitigate background interference in grape leaf feature extraction and improve the ability to extract small disease spots, by combining the characteristic features of grape leaf diseases, we developed a novel method for disease recognition and classification in this study. First, Gaussian filters Sobel smooth de-noising Laplace operator (GSSL) was employed to reduce image noise and enhance the texture of grape leaves. A novel network designated coordinated attention shuffle mechanism-asymmetric multi-scale fusion module net (CASM-AMFMNet) was subsequently applied for grape leaf disease identification. CoAtNet was employed as the network backbone to improve model learning and generalization capabilities, which alleviated the problem of gradient explosion to a certain extent. The CASM-AMFMNet was further utilized to capture and target grape leaf disease areas, therefore reducing background interference. Finally, Asymmetric multi-scale fusion module (AMFM) was employed to extract multi-scale features from small disease spots on grape leaves for accurate identification of small target diseases. The experimental results based on our self-made grape leaf image dataset showed that, compared to existing methods, CASM-AMFMNet achieved an accuracy of 95.95%, F1 score of 95.78%, and mAP of 90.27%. Overall, the model and methods proposed in this report could successfully identify different diseases of grape leaves and provide a feasible scheme for deep learning to correctly recognize grape diseases during agricultural production that may be used as a reference for other crops diseases

CASM-AMFMNet: A Network based on Coordinate Attention Shuffle Mechanism and Asymmetric Multi-Scale Fusion Module for Classification of Grape Leaf Diseases

Grape disease is a significant contributory factor to the decline in grape yield, typically affecting the leaves first. Efficient identification of grape leaf diseases remains a critical unmet need. To mitigate background interference in grape leaf feature extraction and improve the ability to extract small disease spots, by combining the characteristic features of grape leaf diseases, we developed a novel method for disease recognition and classification in this study. First, Gaussian filters Sobel smooth de-noising Laplace operator (GSSL) was employed to reduce image noise and enhance the texture of grape leaves. A novel network designated coordinated attention shuffle mechanism-asymmetric multi-scale fusion module net (CASM-AMFMNet) was subsequently applied for grape leaf disease identification. CoAtNet was employed as the network backbone to improve model learning and generalization capabilities, which alleviated the problem of gradient explosion to a certain extent. The CASM-AMFMNet was further utilized to capture and target grape leaf disease areas, therefore reducing background interference. Finally, Asymmetric multi-scale fusion module (AMFM) was employed to extract multi-scale features from small disease spots on grape leaves for accurate identification of small target diseases. The experimental results based on our self-made grape leaf image dataset showed that, compared to existing methods, CASM-AMFMNet achieved an accuracy of 95.95%, F1 score of 95.78%, and mAP of 90.27%. Overall, the model and methods proposed in this report could successfully identify different diseases of grape leaves and provide a feasible scheme for deep learning to correctly recognize grape diseases during agricultural production that may be used as a reference for other crops diseases

PDAM–STPNNet: A Small Target Detection Approach for Wildland Fire Smoke through Remote Sensing Images

The target detection of smoke through remote sensing images obtained by means of unmanned aerial vehicles (UAVs) can be effective for monitoring early forest fires. However, smoke targets in UAV images are often small and difficult to detect accurately. In this paper, we use YOLOX-L as a baseline and propose a forest smoke detection network based on the parallel spatial domain attention mechanism and a small-scale transformer feature pyramid network (PDAM–STPNNet). First, to enhance the proportion of small forest fire smoke targets in the dataset, we use component stitching data enhancement to generate small forest fire smoke target images in a scaled collage. Then, to fully extract the texture features of smoke, we propose a parallel spatial domain attention mechanism (PDAM) to consider the local and global textures of smoke with symmetry. Finally, we propose a small-scale transformer feature pyramid network (STPN), which uses the transformer encoder to replace all CSP_2 blocks in turn on top of YOLOX-L’s FPN, effectively improving the model’s ability to extract small-target smoke. We validated the effectiveness of our model with recourse to a home-made dataset, the Wildfire Observers and Smoke Recognition Homepage, and the Bowfire dataset. The experiments show that our method has a better detection capability than previous methods

PDAM–STPNNet: A Small Target Detection Approach for Wildland Fire Smoke through Remote Sensing Images

The target detection of smoke through remote sensing images obtained by means of unmanned aerial vehicles (UAVs) can be effective for monitoring early forest fires. However, smoke targets in UAV images are often small and difficult to detect accurately. In this paper, we use YOLOX-L as a baseline and propose a forest smoke detection network based on the parallel spatial domain attention mechanism and a small-scale transformer feature pyramid network (PDAM–STPNNet). First, to enhance the proportion of small forest fire smoke targets in the dataset, we use component stitching data enhancement to generate small forest fire smoke target images in a scaled collage. Then, to fully extract the texture features of smoke, we propose a parallel spatial domain attention mechanism (PDAM) to consider the local and global textures of smoke with symmetry. Finally, we propose a small-scale transformer feature pyramid network (STPN), which uses the transformer encoder to replace all CSP_2 blocks in turn on top of YOLOX-L’s FPN, effectively improving the model’s ability to extract small-target smoke. We validated the effectiveness of our model with recourse to a home-made dataset, the Wildfire Observers and Smoke Recognition Homepage, and the Bowfire dataset. The experiments show that our method has a better detection capability than previous methods

A High-Precision Forest Fire Smoke Detection Approach based on ARGNet

The occurrence of forest fires can lead to ecological damage, property loss, and human casualties. Current forest fire smoke detection methods do not sufficiently consider the characteristics of smoke with high transparency and no clear edges and have low detection accuracy, which cannot meet the needs of complex aerial forest fire smoke detection tasks. In this paper, we propose Adjacent layer composite network based on a recursive feature pyramid with deconvolution and dilated convolution and global optimal nonmaximum suppression (ARGNet) for high-accuracy detection of forest fire smoke. First, the Adjacent layer composite network is proposed to enhance the extraction of smoke features with high transparency and no clear edges, and SoftPool in it is used to retain more feature information of smoke. Then, a recursive feature pyramid with deconvolution and dilated convolution (RDDFPN) is proposed to fuse shallow visual features and deep semantic information in the channel dimension to improve the accuracy of long-range aerial smoke detection. Finally, global optimal nonmaximum suppression (GO-NMS) sets the objective function to globally optimize the selection of anchor frames to adapt to the aerial photography of multiple smoke locations in forest fire scenes. The experimental results show that the ARGNet parametric number on the UAV-IoT platform is as low as 53.48 M, mAP reaches 79.03%, mAP50 reaches 90.26%, mAP75 reaches 82.35%, FPS reaches 122.5, and GFLOPs reaches 55.78. Compared with other mainstream methods, it has the advantages of real-time detection and high accuracy

A High-Precision Forest Fire Smoke Detection Approach based on ARGNet

The occurrence of forest fires can lead to ecological damage, property loss, and human casualties. Current forest fire smoke detection methods do not sufficiently consider the characteristics of smoke with high transparency and no clear edges and have low detection accuracy, which cannot meet the needs of complex aerial forest fire smoke detection tasks. In this paper, we propose Adjacent layer composite network based on a recursive feature pyramid with deconvolution and dilated convolution and global optimal nonmaximum suppression (ARGNet) for high-accuracy detection of forest fire smoke. First, the Adjacent layer composite network is proposed to enhance the extraction of smoke features with high transparency and no clear edges, and SoftPool in it is used to retain more feature information of smoke. Then, a recursive feature pyramid with deconvolution and dilated convolution (RDDFPN) is proposed to fuse shallow visual features and deep semantic information in the channel dimension to improve the accuracy of long-range aerial smoke detection. Finally, global optimal nonmaximum suppression (GO-NMS) sets the objective function to globally optimize the selection of anchor frames to adapt to the aerial photography of multiple smoke locations in forest fire scenes. The experimental results show that the ARGNet parametric number on the UAV-IoT platform is as low as 53.48 M, mAP reaches 79.03%, mAP50 reaches 90.26%, mAP75 reaches 82.35%, FPS reaches 122.5, and GFLOPs reaches 55.78. Compared with other mainstream methods, it has the advantages of real-time detection and high accuracy