發(fā)布時間：2018-06-24 22:02

  本文選題：蟲害預測系統(tǒng) + PSO��； 參考：《浙江理工大學》2017年碩士論文

【摘要】：我國作為農業(yè)大國,農作物的產量占據(jù)重要地位。蟲害的發(fā)生對農作物的生產造成了嚴重的危害。因此,及時、準確的對蟲害的發(fā)生進行預測預報,才能為蟲害的防治工作提供基礎,才能有利于農業(yè)的良好發(fā)展,減少損失。隨著信息技術的發(fā)展,計算機網絡,數(shù)據(jù)管理等技術早已被廣泛應用到蟲害的監(jiān)測和預測等領域。為了適應農作物蟲害資料的規(guī)范化、信息化、網絡化的要求,以及相關人員日常工作的需求,本文構建了一個基于Web技術的蟲害預測系統(tǒng),該系統(tǒng)集蟲害數(shù)據(jù)管理、查詢、預測于一體。蟲害的發(fā)生不僅受天氣、農作物的生長情況等的影響,還具有地域上的差別,蟲害數(shù)據(jù)自身又包含顯著的動態(tài)時序特征,很難對其做到精準的預測。為了能及時和準確的對蟲害的發(fā)生進行預測,本文主要研究內容為:(1)針對農業(yè)中蟲害發(fā)生數(shù)據(jù)的特點,以及對相關基礎理論的研究,以偏最小二乘支持向量機(LSSVM)為核心,建立了針對農業(yè)蟲害發(fā)生的預測模型(PLS-PSO-LSSVM)。首先,針對LSSVM參數(shù)尋優(yōu)的問題,提出用遺傳算法(GA)和粒子群算法(PSO)對其參數(shù)尋優(yōu)進行改進。其次,針對蟲害發(fā)生影響因素之間的共線性問題,提出用偏最小二乘法(PLS)對蟲害發(fā)生影響因素進行主成分提取。(2)基于區(qū)域的蟲害發(fā)生量的預測。以稻飛虱為例,通過對其近30年發(fā)生情況的分析,稻飛虱的發(fā)生不僅受到溫度、濕度、降雨、日照的影響,發(fā)生的情況還有地域上的差別。因此,本文以具體地區(qū)的蟲害影響因素作為預測因子,結合本文建立的預測模型進行預測,并與BP神經網絡、偏最小二乘(PLS)模型進行對比分析,得出本文建立的預測模型的預測精度更高。(3)根據(jù)基于Web技術的蟲害預測系統(tǒng)的設計需求,對系統(tǒng)進行了總體設計和詳細設計分析,并完成了系統(tǒng)的開發(fā)。實現(xiàn)了實時蟲害數(shù)據(jù)監(jiān)測、歷史蟲害數(shù)據(jù)查詢、報表設計分析、蟲害預測等功能模塊。實現(xiàn)了數(shù)據(jù)的集中管理,及時將蟲情發(fā)送到相應的用戶手中。
[Abstract]:As a large agricultural country, the output of crops occupies an important position in China. The occurrence of insect pests has caused serious harm to the production of crops. Therefore, timely and accurate prediction of the occurrence of insect pests can provide a basis for pest control, can be conducive to the good development of agriculture and reduce losses. With the development of information technology, computer network, data management and other technologies have been widely used in pest monitoring and forecasting. In order to meet the requirements of standardization, information and networking of crop pest data and the daily work of related personnel, a pest prediction system based on Web technology is constructed in this paper. Prediction is one thing. The occurrence of insect pests is not only affected by the weather and crop growth, but also has regional differences. The pest data itself contains significant dynamic temporal characteristics, it is difficult to accurately predict them. In order to predict the occurrence of insect pests in time and accurately, the main contents of this paper are as follows: (1) according to the characteristics of pest occurrence data in agriculture and the research of related basic theories, partial least squares support vector machine (LSSVM) is the core. A prediction model for agricultural pest occurrence (PLS-PSO-LSSVM) was established. Firstly, to solve the problem of LSSVM parameter optimization, genetic algorithm (GA) and particle swarm optimization (PSO) are proposed to improve the optimization of LSSVM parameters. Secondly, aiming at the problem of collinearity between the influencing factors of pest occurrence, the partial least square (PLS) method is proposed to extract the principal components of the influencing factors of pest occurrence. (2) the prediction of pest occurrence quantity based on region. Taking rice planthopper as an example, the occurrence of rice planthopper is not only affected by temperature, humidity, rainfall and sunshine, but also by regional difference through the analysis of the occurrence of rice planthopper in recent 30 years. Therefore, this paper takes the pest influence factors of specific area as the prediction factor, combining the prediction model established in this paper, and carries on the comparison analysis with the BP neural network, partial least squares (PLS) model. It is concluded that the prediction model established in this paper has higher prediction accuracy. (3) according to the design requirements of the pest prediction system based on Web technology, the overall design and detailed design analysis of the system are carried out, and the development of the system is completed. Real-time pest data monitoring, historical pest data query, report design and analysis, pest prediction and other functional modules are realized. The centralized management of the data is realized, and the bug situation is sent to the corresponding users in time.
【學位授予單位】：浙江理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：S431;TP393.09

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本文編號：2063154