# BHI-YOLO: A Lightweight Instance Segmentation Model for Strawberry Diseases
## 1 Introduction
[25], utilizing enhanced learning strategies derived from the widely used Mask R-CNN method, conducted instance segmentation of seven types of diseases in strawberries, ultimately achieving an 82.4% mAP. This algorithm also suffers from a high number of parameters. [PERSON] et al. [26] used YOLACT++ for image segmentation of northern corn leaf blight, achieving a comprehensive evaluation score of 98.36%. Although this method has high segmentation performance, its single-scale segmentation approach makes it challenging to guarantee the same effectiveness for multi-scale disease segmentation in complex scenes. [PERSON] et al. [27] proposed an improved YOLOv5 algorithm for segmenting different regions of rice leaves using DenseNet-201. They utilized a Bi-FAPN for feature extraction, achieving a mAP of 82.8% on the rice leaf disease dataset. [PERSON] et al. [28] proposed a method that combines high-resolution RGB images (2048 \(\times\) 1536 pixels) and NIR images, achieving an 87.18% mAP in strawberry defect detection. [PERSON] et al. [29] proposed a lightweight, improved YOLOv5s-CGhostnet model. By optimizing the network structure, this method significantly enhances both detection accuracy and efficiency, providing new insights into the classification of strawberry ripeness. [PERSON] et al. [30] combined YOLOv9 with Swin Transformer to develop a complex model for strawberry ripeness detection, achieving an average accuracy of 87.3%. [PERSON] et al. [31] proposed an automatic disease detection method for strawberry leaves based on an improved YOLOv8 model, achieving an accuracy of 89.7% in identifying diseases on strawberry leaves. [PERSON] et al. [32] proposed an improved algorithm based on the YOLOv8 model, optimizing the Convolutional Block Attention Module (CBAM), an ultra-lightweight and effective dynamic upsampler (DySample), and Omni-Dimensional Dynamic Convolution (ODConv), significantly enhancing the real-time detection capability of strawberry diseases. Although the aforementioned methods have shown good results for plant disease detection, various types of strawberry diseases, along with differences in pathogens and con texts, cause these diseases to exhibit varying sizes and characteristics, leading to multi-scale issues. When dealing with multi-scale issues of plant diseases in complex environments, these detection models typically have a high parameter count and large computational requirements. This results in significantly increased deployment and operational costs, thereby reducing the efficiency and feasibility of practical applications. This paper focuses on strawberry disease detection in complex environments, aiming to develop a deep learning detection model, design a lightweight architecture, and bolster the accuracy and robustness of disease segmentation. Based on YOLOv8n-seg, this paper introduces BHI-YOLO, a lightweight approach to instance segmentation. While ensuring high segmentation effectiveness, the model significantly reduces both the parameter count and the measured GFLOPs, thereby achieving efficient instance segmentation performance. This paper designs the UIB [33] and HS-FPN [34] to make the YOLOv8 model more lightweight. Extensive comparative experiments with other modules are conducted to validate that these two modules enable the improved YOLOv8 model to achieve higher performance and a smaller model size. Specifically, UIB incorporates depthwise separable convolutions to reduce the number of parameters while retaining key features. HS-FPN enhances model robustness by integrating high-level and low-level features, further reducing the model's size. Finally, the design of the iRMA module effectively aggregates global information to complement local information, combining the advantages of iRMB [35] and EMA [36], and significantly improves system performance while maintaining the lightweight characteristics of the model.