As a crop with significant medicinal value and nutritional components, the market demand for bitter melon continues to grow. The diversity of bitter melon shapes has a direct impact on its market acceptance and consumer preferences, making precise identification of bitter melon germplasm resources crucial for breeding work. To address the limitations of time-consuming and less accurate traditional manual identification methods, there is a need to enhance the automation and intelligence of bitter melon phenotype detection. This study developed a bitter melon phenotype detection model named CSW-YOLO. By incorporating the ConvNeXt V2 module to replace the backbone network of YOLOv8, the model’s focus on critical target features is enhanced. Additionally, the SimAM attention mechanism was introduced to compute attention weights for neurons without increasing the parameter count, further enhancing the model’s recognition accuracy. Finally, WIoUv3 was introduced as the bounding box loss function to improve the model’s convergence speed and positioning capabilities. The model was trained and tested on a bitter melon image dataset, achieving a precision of 94.6%, a recall of 80.6%, a mAP50 of 96.7%, and an F1 score of 87.04%. These results represent improvements of 8.5%, 0.4%, 11.1%, and 4% in precision, recall, mAP50, and F1 score, respectively, over the original YOLOv8 model. Furthermore, the effectiveness of the improvements was validated through heatmap analysis and ablation experiments, demonstrating that the CSW-YOLO model can more accurately focus on target features, reduce false detection rates, and enhance generalization capabilities. Comparative tests with various mainstream deep learning models also proved the superior performance of CSW-YOLO in bitter melon phenotype detection tasks. This research provides an accurate and reliable method for bitter melon phenotype identification and also offers technical support for the visual detection technologies of other agricultural products.

Bitter melon, an important medicinal and edible economic crop, is often threatened by diseases such as downy mildew, powdery mildew, viral diseases, anthracnose, and blight during its growth. Efficient and accurate disease detection is of significant importance for achieving sustainable disease management in bitter melon cultivation. To address the issues of weak generalization ability and high computational demands in existing deep learning models in complex field environments, this study proposes an improved lightweight YOLOv8-LSW model. The model incorporates the inverted bottleneck structure of LeYOLO-small to design the backbone network, utilizing depthwise separable convolutions and cross-stage feature reuse modules to achieve lightweight design, reducing the number of parameters while enhancing multi-scale feature extraction capabilities. It also integrates the ShuffleAttention mechanism, strengthening the feature response in lesion areas through channel shuffling and spatial attention dual pathways. Finally, WIoUv3 replaces the original loss function, optimizing lesion boundary regression based on a dynamic focusing mechanism. The results show that YOLOv8-LSW achieves a precision of 95.3%, recall of 94.3%, mAP50 of 98.1%, mAP50-95h of 95.6%, and F1-score of 94.80%, which represent improvements of 2.2%, 2.7%, 1.2%, 2.2%, and 2.46%, respectively, compared to the original YOLOv8n. The effectiveness of the improvements was verified through heatmap analysis and ablation experiments. The number of parameters and GFLOPS were reduced by 20.58% and 20.29%, respectively, with an FPS of 341.58. Comparison tests with various mainstream deep learning models also demonstrated that YOLO-LSW performs well in the bitter melon disease detection task. This research provides a technical solution with both lightweight design and strong generalization ability for real-time detection of bitter melon diseases in complex environments, which holds significant application value in promoting precision disease control in smart agriculture.