文章摘要
基于Deep Forest算法的对虾急性肝胰腺坏死病(AHPND)预警数学模型构建
Construction of Early Warning Mathematical Model for Litopenaeus vannamei AHPND Based on Deep Forest Algorithm
投稿时间:2022-11-24  修订日期:2023-03-21
DOI:
中文关键词: 对虾,急性肝胰腺坏死病,预警数学模型,Deep Forest,Python语言
英文关键词: shrimp, AHPND, early  warning mathematical  model, Deep  Forest algorithm, Python  programming language
基金项目:国家重点研发计划课题(2019YFD0900102);山东省泰山产业领军人才项目(LJNY201802);苏北科技专项(SZ-LYG202028)资助 [Supported by the National Key Research and Development Program of China (2019YFD0900102); the Project of Taishan Industry Leading Talent Project of Shandong Province (LJNY201802); Policy Guidance Program of Jiangsu Province (SZ-LYG202028)]
作者单位邮编
王印庚* 中国水产科学研究院黄海水产研究所
青岛海洋科学与技术国家实验室 
266071
于永翔 中国水产科学研究院黄海水产研究所
青岛海洋科学与技术国家实验室 
蔡欣欣 中国水产科学研究院黄海水产研究所
青岛海洋科学与技术国家实验室 
张正 中国水产科学研究院黄海水产研究所 
王春元 中国水产科学研究院黄海水产研究所
青岛海洋科学与技术国家实验室 
朱洪洋 中国水产科学研究院黄海水产研究所 
李昊 中国水产科学研究院黄海水产研究所 
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中文摘要:
      为预报池塘养殖凡纳滨对虾急性肝胰腺坏死病(AHPND)的发生,自2020年开始,笔者对凡纳滨对虾养殖区开展了连续监测工作,包括与疾病发生相关的环境理化因子、微生物因子、虾体自身健康状况等18个候选预警因子指标,通过数据标准化处理后分析病原、宿主与环境之间的相关性关系,对候选预警因子进行筛选,基于Python语言编程结合Deep Forest、LightGBM、XGBoost算法进行数据建模和预测性能评判,仿真环境为Python2.7,以预警因子指标作为输入样本(即警兆),以对虾是否发病指标作为输出结果(即警情),根据输入样本和输出结果各自建立输入数据矩阵和目标数据矩阵,利用原始数据矩阵对输入样本进行初始化,结合函数方程进行拟合,拟合的源代码能利用已知环境、病原及对虾免疫指标数据对目标警情的预测。最终建立了基于Deep Forest算法的虾体细菌总数、虾体弧菌占比、水体细菌总数、盐度的4维向量预警预报模型,准确率达89.00%。本文利用人工智能算法应用到对虾AHPND发生的预测预报,相关研究结果为对虾AHPND疾病预警预报建立了预警数学模型,并这为对虾健康养殖和疾病防控提供了技术支撑和有力保障。
英文摘要:
      Acute hepatopancreatic necrosis disease (AHPND) has a wide prevalence, rapid onset, and high mortality for shrimp culture, which is an important limiting factor to affect shrimp aquaculture development in recent years and bring massive economic losses to the industry in worldwide. Systematic study of which factors are significantly correlated with the occurrence of AHPND, and further establishment of a prediction model for the occurrence of shrimp AHPND are important for the prevention and control of the diseases. In this study, the Litopenaeus vannamei in pond culture were preliminarily analyzed the coupling relationship between the occurrence and prevalence of AHPND in shrimps and pathogens, environmental and host autoimmune factors by collecting environmental factors, pathogen abundance, and the host health indicators under AHPND incidence. Then the mathematical early warning model of AHPND occurrence in pond cultured L. vannamei was constructed by Deep Forest Algorithm. In order to explore the relationship between the occurrence of AHPND and environment, pathogen and shrimp immunity, the occurrence of AHPND and its environment, pathogen and shrimp immunity factors in pond cultured L. vannamei were continuously monitored. A total of 18 parameters were collected, including the air temperature, water temperature, salinity, pH, dissolved oxygen (DO), ammonia nitrogen (NH4-N) and nitrite (NO2-N) concentration, culturable bacteria and Vibrio in water, culturable bacteria and Vibrio in shrimp hepatopancreas, the proportion of Vibrio in water and shrimp hepatopancreas, and the activities of ACP, AKP, SOD, LZM, PO in shrimp muscles. The parameter simulation prediction data based on the L. vannamei AHPND occurrence related factor sequence (environmental factor, microbial factor and shrimp health indicator) are constructed for the first time, the one-dimensional sequence is mapped into the three-dimensional space, different kernel functions are selected in combination with the actual classification problem to compare the model fitting accuracy, and the parameters in the model are optimized by the test algorithm. A total of 140 groups of relevant data were collected under the same mode, and the groups of additional pouring of exogenous inputs in the process of breeding were eliminated. By deleting invalid data, there were 100 groups of classified monitoring data, including 25 groups of morbidity data and 75 groups of health data. And due to the dimensional and quantitative differences among different factors, the model will be affected. In order to improve the speed of subsequent experimental training and the accuracy of prediction, the 100 groups of training test data were processed by mapminmax function are normalized for data processing. The relationship between 18 parameters and the occurrence of AHPND in L. vannamei was analyzed by Pearson correlation, and the main influencing factors were further screened by pairwise analysis between the factors. Pearson correlation analysis showed that the incidence of AHPND was positively correlated (P<0.05) with the salinity, the number of culturable bacteria and Vibrio in shrimp hepatopancreas, the proportion of Vibrio in shrimp hepatopancreas, the number of culturable bacteria and Vibrio in water, the activities of LZM, ACP and PO in shrimp muscles, the correlation coefficients were 0.3501, 0.5741, 0.5211, 0.3911, 0.3747, 0.2383, 0.4382, 0.2571 and 0.2289, respectively. which indicating that the AHPND was more likely to occur with the increase of these parameters under a certain range. On the other hand, the incidence of AHPND was negatively correlated with water temperature (P<0.05), and the correlation coefficient was -0.2279. And the water temperature, pH, DO, NH4-N concentration, NO2-N concentration, Vibrio proportion in water, AKP and SOD had little correlation with the incidence of AHPND (P>0.05). Furthermore, in the process of model construction, parameters are removed according to the correlation between parameters and factors. The occurrence of AHPND in L. vannamei was directly and significantly correlated with 7 parameters including the total number of shrimp bacteria, the total number of shrimp Vibrios, LZM, the proportion of shrimp Vibrio, the total number of water bacteria, salinity, and water Vibrios. The prediction performance of three popular integrated learning method algorithms based on decision tree, Deep Forest, LightGBM, and XGBoost, was evaluated by Python language programming, and finally a 4-dimensional vector early warning prediction model based on Deep Forest algorithm for the total number of shrimp bacteria, the proportion of Vibrio shrimp, water bacteria, and salinity was established (accuracy 89.00%). Although the prediction performance of the deep forest model decreased somewhat compared with the support vector machine model (accuracy 100.00%) established in this study, the algorithm was gradually screened out based on the correlation relationship between factors, including the effects of all factors. It is proved that the deep forest model established in this study is the most ideal prediction model for predicting the occurrence of AHPND in L. vannamei among the ten algorithm models tried, and the superiority of the deep forest algorithm is also further verified. The relevant research results provide basic data and technical support for shrimp AHPND disease prediction and health prevention and control, and lay a theoretical foundation for further establishment of aquaculture animal disease early warning theory.
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