In the field of tomato disease spot detection, despite significant advancements in convolutional neural network technology, challenges still persist in detecting small-sized tomato disease spots. This study aims to enhance the tomato disease spot detection model to improve the accuracy of detecting small disease spots on tomatoes. Firstly, the concept of DenseNet is introduced, and a Dense_C3 module is constructed, which incorporates two 3×3 convolutional layers within the Bottleneck of C3 to achieve dense feature propagation and reuse. Secondly, the Convolutional Block Attention Module (CBAM) is employed to weight the feature maps, enhancing the focus on local and spatial information to enhance model accuracy. To validate the effectiveness of the proposed improvements, experiments were conducted on tomato disease spots of different scales, including Septoria Leaf Spot, Early Blight, Late Blight, and Tomato Yellow Leaf Curl Virus. The experimental results demonstrate that the introduction of the Dense_C3 structure and CBAM improves the detection accuracy for small-scale targets. In a comprehensive evaluation, the improved YOLOv5s model shows increases of 2.4% in F1 score and 2.1% in mAP.
In response to the current issues of missed detections, false alarms, low detection accuracy, and incomplete detection categories in potato quality assessment, this paper proposes an enhanced YOLOv5s-based object detection model. The model categorizes external potato features into four standard classes. Firstly, this paper introduces the SE (Squeeze and Excitation) attention mechanism, enabling the model to adaptively adjust feature weights across different channels, emphasizing features crucial for quality assessment. Secondly, incorporate the SPPCSPC (Spatial Pyramid Pooling Cross Stage Partial Channel) convolutional neural network structure, transforming various-sized feature maps into fixed-length feature vectors. Additionally, employ the up-sampling operator CARAFE (Content-Aware Reassembly of Features) to enhance the performance of the feature pyramid network. Experimental results demonstrate that the improved YOLOv5s model exhibits a 4.5% increase in detection accuracy and a 2.1% improvement in average precision.
Mushrooms, as a delicacy in people's lives, are deeply loved by people, and the nutrients in mushrooms play an essential role in people's health. However, the characteristics of poisonous mushrooms and non-toxic mushrooms are extremely similar, and they are easily confused in the field of miscellaneous circumstances, and therefore often cause the eaters to ingest poisoning. The identification of poisonous mushrooms is a basic measure to avoid poisoning. At present, the methods for identifying poisonous mushrooms mainly include shape recognition method based on folk experience, chemical analysis methods, and animal testing methods. However, these methods have some disadvantages such as low accuracy in the practical application identification, complex experimental equipment required, unsatisfactory detection of unknown toxins, and long experimental period. Aim at the deficiency of the traditional poisonous mushroom identification method; this paper proposes a poison mushroom identification method based on BP neural network. Through the learning of the characteristics of the known poisonous mushroom, identify unknown poisonous mushrooms.
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