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Smart Agriculture ›› 2024, Vol. 6 ›› Issue (2): 62-71.doi: 10.12133/j.smartag.SA201311014

• 专刊--农业信息感知与模型 • 上一篇    下一篇

基于改进YOLOv8和多元特征的对虾发病检测方法

许瑞峰1,2,3,4, 王瑶华1,3,4, 丁文勇1,3,4, 於俊琦1,3,4, 闫茂仓1,3,4(), 陈琛1,3,4()   

  1. 1. 中国浙江省海洋水产养殖研究所,浙江 温州 325000,中国
    2. 上海海洋大学 水产与生命学院,上海 201306,中国
    3. 浙江省近岸水域生物资源开发与保护重点实验室,浙江 温州 325000,中国
    4. 温州市海洋生物遗传育种重点实验室,浙江 温州 325000,中国
  • 收稿日期:2023-11-09 出版日期:2024-03-30
  • 基金资助:
    浙江省重点研发计划(2021C02025); 温州市重大科技攻关项目(ZN2021001); 浙江省“三农九方”科技协作计划(2023SNJF077); 国家重点研发计划课题(2020YFD0900801)
  • 作者简介:

    许瑞峰,研究方向为对虾智能化养殖。E-mail:

  • 通信作者:
    闫茂仓,博士,研究员,研究方向为数字化育种和养殖。E-mail:
    陈 琛,学士,高级工程师,研究方向为对虾智能化养殖。E-mail:

Shrimp Diseases Detection Method Based on Improved YOLOv8 and Multiple Features

XU Ruifeng1,2,3,4, WANG Yaohua1,3,4, DING Wenyong1,3,4, YU Junqi1,3,4, YAN Maocang1,3,4(), CHEN Chen1,3,4()   

  1. 1. Zhejiang Mariculture Research Institute, Wenzhou 325000, China
    2. Shanghai Ocean University, Shanghai 201306, China
    Zhejiang Key Lab of Exploitation and Preservation of Coastal Bio-Resource, Wenzhou 325000, China
    4. Wenzhou Key Laboratory of Marine Genetics and Breeding, Wenzhou 325000, China
  • Received:2023-11-09 Online:2024-03-30
  • corresponding author:
    YAN Maocang, E-mail: ;
    CHEN Chen, E-mail:
  • About author:

    XU Ruifeng, E-mail:

  • Supported by:
    Zhejiang Key Science and Technology Project(2021C02025); Key Scientific and Technological Innovation Projects of Wenzhou(ZN2021001); Zhejiang Province San-Nong-Jiu-Fang Science and Technology Cooperation Project(2023SNJF077); National Key Research and Development Program of China(2020YFD0900801)

摘要:

[目的/意义] 对虾病害严重危害对虾养殖业。针对对虾病害发病快、死亡率高等特点,高密度的工厂化养殖等模式需要一种高效率对虾发病检测方法替代传统人工检查方法,实现对虾发病的及时预警。 [方法] 提出一种基于改进YOLOv8(You Only Look Once)和多元特征的对虾发病检测方法。首先利用改进YOLOv8网络从对虾夜间水面红外图像中进行前景提取,再利用Farneback光流法和灰度共生矩阵(Gray Level Co-occurrence Matrix,GLCM)提取对虾视频片段的运动特征与图像纹理特征,利用提取到的特征参数构建训练数据集,训练支持向量机(Support Vector Machine,SVM)作为分类器用于检测对虾视频片段,实现对正常与发病的对虾视频片段的检测分类。 [结果和讨论] 训练好的SVM分类器在300个测试样本上的表现为检测准确率平均值为83%,检测效果达到设计要求。检测误差主要是将发病片段错误地检测为正常片段。该误差主要受水面对虾数量和视频影响。 [结论] 本研究实现了对对虾发病的检测,提供了一种基于计算机视觉的检测方法。但受条件限制,仅在工厂化养殖环境下进行了实验,尚不能适用于多种养殖环境,仍有改进空间。

关键词: 对虾病害, 计算机视觉, YOLOv8, Farneback光流法, 灰度共生矩阵, 支持向量机

Abstract:

[Objective] In recent years, there has been a steady increase in the occurrence and fatality rates of shrimp diseases, causing substantial impacts in shrimp aquaculture. These diseases are marked by their swift onset, high infectivity, complex control requirements, and elevated mortality rates. With the continuous growth of shrimp factory farming, traditional manual detection approaches are no longer able to keep pace with the current requirements. Hence, there is an urgent necessity for an automated solution to identify shrimp diseases. The main goal of this research is to create a cost-effective inspection method using computer vision that achieves a harmonious balance between cost efficiency and detection accuracy. The improved YOLOv8 (You Only Look Once) network and multiple features were employed to detect shrimp diseases. [Methods] To address the issue of surface foam interference, the improved YOLOv8 network was applied to detect and extract surface shrimps as the primary focus of the image. This target detection approach accurately recognizes objects of interest in the image, determining their category and location, with extraction results surpassing those of threshold segmentation. Taking into account the cost limitations of platform computing power in practical production settings, the network was optimized by reducing parameters and computations, thereby improving detection speed and deployment efficiency. Additionally, the Farnberck optical flow method and gray level co-occurrence matrix (GLCM) were employed to capture the movement and image texture features of shrimp video clips. A dataset was created using these extracted multiple feature parameters, and a Support Vector Machine (SVM) classifier was trained to categorize the multiple feature parameters in video clips, facilitating the detection of shrimp health. [Results and Discussions] The improved YOLOv8 in this study effectively enhanced detection accuracy without increasing the number of parameters and flops. According to the results of the ablation experiment, replacing the backbone network with FasterNet lightweight backbone network significantly reduces the number of parameters and computation, albeit at the cost of decreased accuracy. However, after integrating the efficient multi-scale attention (EMA) on the neck, the mAP0.5 increased by 0.3% compared to YOLOv8s, while mAP0.95 only decreased by 2.1%. Furthermore, the parameter count decreased by 45%, and FLOPs decreased by 42%. The improved YOLOv8 exhibits remarkable performance, ranking second only to YOLOv7 in terms of mAP0.5 and mAP0.95, with respective reductions of 0.4% and 0.6%. Additionally, it possesses a significantly reduced parameter count and FLOPS compared to YOLOv7, matching those of YOLOv5. Despite the YOLOv7-Tiny and YOLOv8-VanillaNet models boasting lower parameters and Flops, their accuracy lags behind that of the improved YOLOv8. The mAP0.5 and mAP0.95 of YOLOv7-Tiny and YOLOv8-VanillaNet are 22.4%, 36.2%, 2.3%, and 4.7% lower than that of the improved YOLOv8, respectively. Using a support vector machine (SVM) trained on a comprehensive dataset incorporating multiple feature, the classifier achieved an impressive accuracy rate of 97.625%. The 150 normal fragments and the 150 diseased fragments were randomly selected as test samples. The classifier exhibited a detection accuracy of 89% on this dataset of the 300 samples. This result indicates that the combination of features extracted using the Farnberck optical flow method and GLCM can effectively capture the distinguishing dynamics of movement speed and direction between infected and healthy shrimp. In this research, the majority of errors stem from the incorrect recognition of diseased segments as normal segments, accounting for 88.2% of the total error. These errors can be categorized into three main types: 1) The first type occurs when floating foam obstructs the water surface, resulting in a small number of shrimp being extracted from the image. 2) The second type is attributed to changes in water movement. In this study, nanotubes were used for oxygenation, leading to the generation of sprays on the water surface, which affected the movement of shrimp. 3) The third type of error is linked to video quality. When the video's pixel count is low, the difference in optical flow between diseased shrimp and normal shrimp becomes relatively small. Therefore, it is advisable to adjust the collection area based on the actual production environment and enhance video quality. [Conclusions] The multiple features introduced in this study effectively capture the movement of shrimp, and can be employed for disease detection. The improved YOLOv8 is particularly well-suited for platforms with limited computational resources and is feasible for deployment in actual production settings. However, the experiment was conducted in a factory farming environment, limiting the applicability of the method to other farming environments. Overall, this method only requires consumer-grade cameras as image acquisition equipment and has lower requirements on the detection platform, and can provide a theoretical basis and methodological support for the future application of aquatic disease detection methods.

Key words: shrimp diseases, computer vision, YOLOv8, Farnberck optical flow, gray level co-occurrence matrix, support vector machine