No abstract

IntroductionTobacco brown spot disease caused by Alternaria fungal species is a major threat to tobacco growth and yield. Thus, accurate and rapid detection of tobacco brown spot disease is vital for disease prevention and chemical pesticide inputs.MethodsHere, we propose an improved YOLOX-Tiny network, named YOLO-Tobacco, for the detection of tobacco brown spot disease under open-field scenarios. Aiming to excavate valuable disease features and enhance the integration of different levels of features, thereby improving the ability to detect dense disease spots at different scales, we introduced hierarchical mixed-scale units (HMUs) in the neck network for information interaction and feature refinement between channels. Furthermore, in order to enhance the detection of small disease spots and the robustness of the network, we also introduced convolutional block attention modules (CBAMs) into the neck network.ResultsAs a result, the YOLO-Tobacco network achieved an average precision (AP) of 80.56% on the test set. The AP was 3.22%, 8.99%, and 12.03% higher than that obtained by the classic lightweight detection networks YOLOX-Tiny network, YOLOv5-S network, and YOLOv4-Tiny network, respectively. In addition, the YOLO-Tobacco network also had a fast detection speed of 69 frames per second (FPS).DiscussionTherefore, the YOLO-Tobacco network satisfies both the advantages of high detection accuracy and fast detection speed. It will likely have a positive impact on early monitoring, disease control, and quality assessment in diseased tobacco plants.

The identification of corn leaf diseases in a real field environment faces several difficulties, such as complex background disturbances, variations and irregularities in the lesion areas, and large intra-class and small inter-class disparities. Traditional Convolutional Neural Network (CNN) models have a low recognition accuracy and a large number of parameters. In this study, a lightweight corn disease identification model called DFCANet (Double Fusion block with Coordinate Attention Network) is proposed. The DFCANet consists mainly of two components: The dual feature fusion with coordinate attention and the Down-Sampling (DS) modules. The DFCA block contains dual feature fusion and Coordinate Attention (CA) modules. In order to completely fuse the shallow and deep features, these features were fused twice. The CA module suppresses the background noise and focuses on the diseased area. In addition, the DS module is used for down-sampling. It reduces the loss of information by expanding the feature channel dimension and the Depthwise convolution. The results show that DFCANet has an average recognition accuracy of 98.47%. It is more efficient at identifying corn leaf diseases in real scene images, compared with VGG16 (96.63%), ResNet50 (93.27%), EffcientNet-B0 (97.24%), ConvNeXt-B (94.18%), DenseNet121 (95.71%), MobileNet-V2 (95.41%), MobileNetv3-Large (96.33%), and ShuffleNetV2-1.0× (94.80%) methods. Moreover, the model’s Params and Flops are 1.91M and 309.1M, respectively, which are lower than heavyweight network models and most lightweight network models. In general, this study provides a novel, lightweight, and efficient convolutional neural network model for corn disease identification.

No abstract