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| 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 |
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王印庚1,2, 于永翔1,3, 蔡欣欣1, 张正1,2, 王春元1, 廖梅杰1,2, 朱洪洋1, 李昊4
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1.中国水产科学研究院黄海水产研究所 山东 青岛 266071;2.青岛海洋科技中心海洋渔业科学与食品产出过程功能实验室 山东 青岛 266071;3.青岛海洋科技中心海洋渔业科学与食品产出过程功能实验室 山东 青岛 266072;4.中国水产科学研究院黄海水产研究所 山东 青岛 266072
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| 摘要: |
| 为预报池塘养殖凡纳对虾(Penaeus vannamei)急性肝胰腺坏死病(AHPND)的发生，自2020年开始，笔者对凡纳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，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进行预测。最终建立了基于Deep Forest算法的虾体(肝胰腺内)细菌总数、虾体弧菌(Vibrio)占比、水体细菌总数和盐度的4维向量预警预报模型，准确率达89.00%。本研究将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，并为对虾健康养殖和疾病防控提供了技术支撑和有力保障。 |
| 关键词: 对虾 急性肝胰腺坏死病 预警数学模型 Deep Forest算法 Python语言 |
| DOI：10.19663/j.issn2095-9869.20221124002 |
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| Construction of an early warning mathematical model for Penaeus vannamei AHPND based on the Deep Forest algorith |
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WANG Yingeng,YU Yongxiang,CAI Xinxin,ZHANG Zheng,WANG Chunyuan,LIAO Meijie,ZHU Hongyang,LI Hao
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1.Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071, China;2.Laboratory for Marine Fisheries Science and Food Production Processes, Qingdao Marine Science and
Technology Center, Qingdao 266071, China;3.Laboratory for Marine Fisheries Science and Food Production Processes, Qingdao Marine Science and
Technology Center, Qingdao 266072, China
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| Abstract: |
| Acute hepatopancreatic necrosis disease (AHPND) is widely prevalent, has a rapid onset, and has high mortality in shrimp culture, making it a key limiting factor affecting shrimp aquaculture development in recent years, resulting in massive economic losses to the industry worldwide. Systematic studies that investigate which factors significantly correlate with the occurrence of AHPND, and further establishment of a prediction model for the occurrence of shrimp AHPND, are important for preventing and controlling the disease. In this study, Penaeus vannamei in pond culture were preliminarily analyzed for the coupling relationship between the occurrence and prevalence of AHPND in shrimps and pathogens, and for environmental and host autoimmune factors by assessing the environmental factors, pathogen abundance, and host health indicators under AHPND incidence. Then, a mathematical early warning model of AHPND occurrence in pond-cultured P. vannamei was constructed using Deep Forest algorithm. The occurrence of AHPND and its environment, pathogen, and shrimp immunity factors in pond-cultured P. vannamei were continuously monitored to explore the relationship between the occurrences of AHPND in relation to these factors. A total of 18 parameters were assessed, including the air and water temperature, salinity, pH, dissolved oxygen (DO), ammonia nitrogen (NH4+-N) and nitrite (NO2-N) concentrations, culturable bacteria and Vibrio in water, culturable bacteria and Vibrio in the shrimp hepatopancreas, the proportion of Vibrio in water and the shrimp hepatopancreas, and the activities of acid phosphatase (ACP), alkaline phosphatase (AKP), superoxide dismutase (SOD), lysozyme (LZM), and phenol oxidase (PO) in shrimp muscles. The parameter simulation prediction data based on the P. vannamei AHPND occurrence-related factor sequence (environmental factor, microbial factor, and shrimp health indicator) were constructed for the first time. The one-dimensional sequence was mapped into the three-dimensional space, different kernel functions were selected in combination with the actual classification problem to compare the model fitting accuracy, and the test algorithm optimized the parameters in the model. A total of 140 relevant data groups were collected under the same mode, and the groups of additional exogenous inputs during the breeding process were eliminated. After deleting invalid data, there were 100 groups of classified monitoring data, including 25 groups of morbidity data and 75 groups of health data. Moreover, the model was affected due to the dimensional and quantitative differences among different factors. In order to improve the speed of subsequent experimental training and prediction accuracy, the 100 groups of training test data processed by the mapminmax function were normalized for data processing. The relationship between 18 parameters and the occurrence of AHPND in P. vannamei was analyzed using Pearson´s correlation, and the main influencing factors were further screened using pairwise analysis between the factors. Pearson´s correlation analysis indicated that the incidence of AHPND positively correlated (P<0.05) with salinity, the number of culturable bacteria and Vibrio in the shrimp, the proportion of Vibrio in the shrimp, the number of culturable bacteria and Vibrio in water, and the activities of LZM, ACP, and PO in shrimp muscles. The correlation coefficients were 0.350 1, 0.574 1, 0.521 1, 0.391 1, 0.374 7, 0.238 3, 0.438 2, 0.257 1, and 0.228 9, respectively, indicating that AHPND was more likely to occur with an increase of these parameter values within a certain range. The incidence of AHPND negatively correlated with water temperature (P<0.05), and the correlation coefficient was –0.227 9. Moreover, the water temperature, pH, DO, NH4+-N and NO2-N concentrations, Vibrio proportion in water, AKP, and SOD had a weak correlation with the incidence of AHPND (P>0.05). Furthermore, parameters were removed in the model construction process according to the correlation between parameters and factors. The occurrence of AHPND in P. vannamei directly and significantly correlated with seven parameters, including the total number of shrimp bacteria, the total number of shrimp Vibrio, LZM, the proportion of shrimp Vibrio, the total number of water bacteria, salinity, and the total number of water Vibrio. The prediction performance of three popular integrated learning method algorithms based on decision tree, Deep Forest, LightGBM, and XGBoost was evaluated using Python language programming, and, finally, a four-dimensional vector early warning prediction model based on the Deep Forest algorithm for the total number of shrimp bacteria, the proportion of Vibrio shrimp, the total number of water bacteria, and salinity was established (accuracy: 89.00%). Although the prediction performance of the Deep Forest model decreased somewhat compared with that of the support vector machine model established in this study, the algorithm was gradually screened out based on the correlation between factors, including the effects of all factors. It was proven that the Deep Forest model established in this study was the ideal prediction model for predicting the occurrence of AHPND in P. vannamei among the 10 dimension parameters tried, and the superiority of the Deep Forest algorithm was also further verified. The results provide basic data and technical support for shrimp AHPND disease prediction, prevention and control, and lay a theoretical foundation for further establishment of aquaculture animal disease early warning theory. |
| Key words: Shrimp AHPND Early warning mathematical model Deep Forest algorithm Python programming language |
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