本文選題：作物生長模型 + 遺傳算法�。� 參考：《南京農(nóng)業(yè)大學(xué)》2015年碩士論文

【摘要】：作物模型是以作物生長發(fā)育機(jī)理為基礎(chǔ),對作物生理過程與環(huán)境和技術(shù)的關(guān)系加以理論概括和量化分析的數(shù)學(xué)模型,已經(jīng)在農(nóng)作物估產(chǎn)、農(nóng)田管理決策等領(lǐng)域廣泛應(yīng)用。作物模型在應(yīng)用過程中需要針對不同環(huán)境條件重新優(yōu)化其品種參數(shù)。遺傳算法作為一種高效的啟發(fā)式搜索技術(shù),已在作物生長模型參數(shù)優(yōu)化問題中得到了良好的應(yīng)用。但存在以下問題:由于作物模型本身結(jié)構(gòu)復(fù)雜、實(shí)測數(shù)據(jù)誤差、參數(shù)智能優(yōu)化過程環(huán)節(jié)眾多,給作物模型參數(shù)智能優(yōu)化帶來了很大的不確定性;由于遺傳算法的隨機(jī)搜索機(jī)制,會導(dǎo)致優(yōu)化過程中出現(xiàn)目標(biāo)擬合較好但不符合生理學(xué)特性的優(yōu)化結(jié)果;隨著模型的廣泛應(yīng)用,模型不確定性評價(jià)、參數(shù)智能優(yōu)化等工作需要借助計(jì)算機(jī)軟件工具進(jìn)行快速實(shí)現(xiàn),但目前的軟件工具功能單一,難以面向通用的作物模型領(lǐng)域廣泛應(yīng)用。針對上述問題,論文主要貢獻(xiàn)包括:(1)分析基于協(xié)同進(jìn)化遺傳算法的水稻生長模型參數(shù)優(yōu)化框架(BGA-CMPOF)的不確定性。BGA-CMPOF中的不確定性來源包括:實(shí)測數(shù)據(jù)誤差、適應(yīng)度函數(shù)設(shè)計(jì)、參數(shù)優(yōu)化策略、優(yōu)化算法性能等環(huán)節(jié),本文設(shè)計(jì)了評價(jià)BGA-CMPOF框架不確定性的策略,從目標(biāo)變量選擇、適應(yīng)度權(quán)重設(shè)置、分階段參數(shù)優(yōu)化策略以及不同優(yōu)化算法等角度,分析了 BGA-CMPOF在RiceGrow水稻生長模型參數(shù)優(yōu)化中的不確定性。以汕優(yōu)63在徐州等地的實(shí)測數(shù)據(jù)進(jìn)行試驗(yàn),結(jié)果表明:1)BGA-CMPOF框架可有效優(yōu)化RiceGrow模型品種參數(shù),各參數(shù)優(yōu)化的相對誤差在7%以內(nèi);2)選取LAI、各器官(莖、葉、穗)生物量作為目標(biāo)變量的效果較好,穗生物量、總生物量、LAI的NRMSE分別減小了 0.32%、1.52%和 1.73%,RMSE 分別減小了 8kg/ha、123.1 kg/ha、0.08;MAD分別減小了 5.44 kg/ha、105.1 kg/ha、0.07;3)分階段參數(shù)優(yōu)化策略對于BGA-CMPOF而言效果不明顯,各目標(biāo)NRMSE的差距1%;4)MECA算法比IAGA算法的優(yōu)化精度略高,但耗時(shí)較長,不利于模型參數(shù)的快速優(yōu)化。(2)提出基于領(lǐng)域知識引導(dǎo)遺傳算法的作物生育期模型參數(shù)優(yōu)化方法。建立作物模型參數(shù)智能優(yōu)化領(lǐng)域知識庫,通過約束模型初始參數(shù)范圍、確定調(diào)參關(guān)鍵物候期、擴(kuò)展物候期實(shí)測值、提煉方向算子等環(huán)節(jié),對基于遺傳算法的作物模型參數(shù)優(yōu)化框架進(jìn)行約束和引導(dǎo)。本文以WheatGrow小麥生育期模型為應(yīng)用對象,針對濟(jì)南13號小麥品種在徐州、濟(jì)寧、濰坊、臨沂等地的實(shí)測數(shù)據(jù)的參數(shù)優(yōu)化試驗(yàn)結(jié)果表明:1)四個(gè)地點(diǎn)的初步試驗(yàn)驗(yàn)證結(jié)果的RMSE分別達(dá)到了:1.51d、2.05d、0.72d和1.08d,R2均0.99,MAD分別為1.1d、1.6d、0.55d和0.8d,模擬效果較好,但部分參數(shù)存在不符合生物學(xué)特性的參數(shù)值;2)通過約束參數(shù)PVT的初始范圍[30,40],加入品種參數(shù)范圍約束后,各地點(diǎn)的調(diào)參結(jié)果的各項(xiàng)指標(biāo)均有小幅增加,RMSE分別為 1.67d、2.12d、1.09d和 1.58d,R2均0.99,MAD 分別為 1.4d、1.6d、0.8d和 1.3d,但PVT的參數(shù)值均符合濟(jì)南13號半冬性的品種特性;3)擴(kuò)展關(guān)鍵物候期的實(shí)測值后,四地的驗(yàn)證結(jié)果RMSE分別為1.24d、1.56d、1.22d和1.48d,R2分別為0.997、0.993、0.997和0.991,MAD分別為1d、1.2d、1.8d和1.2d,并且品種參數(shù)IE符合生物學(xué)特性,結(jié)果表明,擴(kuò)展調(diào)參物候期數(shù)據(jù)進(jìn)行參數(shù)優(yōu)化,在保證優(yōu)化效果的同時(shí),能夠起到約束品種參數(shù)的作用。4)加入方向算子后,IAGA算法的收斂代數(shù)分別減少了 8代和3代,方向算子能夠加快算法的收斂速度。(3)研制作物模型參數(shù)優(yōu)化及不確定性分析工具(CMPOAT)采用構(gòu)件化軟件中基于框架的軟件開發(fā)方法,開發(fā)基于動態(tài)組裝框架的作物模型自動調(diào)參及不確定性分析工具,能夠根據(jù)用戶需求,實(shí)現(xiàn)作物模型、進(jìn)化算法、數(shù)據(jù)處理等業(yè)務(wù)的動態(tài)組裝。系統(tǒng)實(shí)現(xiàn)了:數(shù)據(jù)管理與分析、作物模型分析、調(diào)參計(jì)算、專家知識庫管理、組件庫管理等功能。以WheatGrow模型和IAGA算法為對象的應(yīng)用案例表明,CMPOAT能夠分析作物模型不確定性并進(jìn)行模型參數(shù)優(yōu)化,為作物模型的分析和應(yīng)用提供了有力的軟件工具。
[Abstract]:The crop model is a mathematical model based on the mechanism of crop growth and development and the theoretical generalization and quantitative analysis of the relationship between crop physiological process and environment and technology. It has been widely used in the fields of crop yield estimation and farmland management decision. As an efficient heuristic search technique, genetic algorithm has been well applied in the parameter optimization of crop growth model. However, the following problems are as follows: because the structure of the crop model is complex, the measured data error and the parameter intelligent optimization process are numerous, it has brought a great deal to the intelligent optimization of the crop model parameters. As a result of the stochastic search mechanism of genetic algorithm, the optimization results will lead to the optimization results of better target fitting but not in conformity with the physiological characteristics. With the extensive application of the model, the model uncertainty evaluation and parameter intelligent optimization need to be implemented quickly with the aid of computer software tools, but the current software tools are used. The main contributions of this paper are as follows: (1) the uncertainty sources in the uncertainty.BGA-CMPOF of the parameter optimization framework of the rice growth model (BGA-CMPOF) based on Coevolutionary Genetic Algorithm (coevolutionary genetic algorithm) include the measured data error, the fitness function design, and the parameter design. In this paper, the strategy of evaluating the uncertainty of BGA-CMPOF framework is designed in this paper. The uncertainty of BGA-CMPOF in the parameter optimization of RiceGrow rice growth model is analyzed from the selection of the target variables, the setting of fitness weight, the optimization strategy of the phased parameters and the different optimization algorithms. Experiments were conducted in Xuzhou and other places. The results showed that: 1) the BGA-CMPOF framework could effectively optimize the parameter of RiceGrow model, and the relative error of each parameter was less than 7%; 2) LAI was selected as the target variable, and the ear biomass, total biomass, and NRMSE of LAI were reduced by 0.32%, 1.52% respectively. And 1.73%, RMSE reduced 8kg/ha, 123.1 kg/ha, 0.08; MAD decreased 5.44 kg/ha, 105.1 kg/ha, 0.07; 3). The phase parameter optimization strategy is not obvious to BGA-CMPOF, the gap 1% of each target NRMSE, 4) MECA algorithm is slightly higher than IAGA algorithm, but it takes longer time and is not conducive to the rapid optimization of model parameters. (2) The parameter optimization method of crop growth period model based on domain knowledge guided genetic algorithm is proposed. The knowledge base of intelligent optimization field of crop model parameters is set up. By restricting the range of the initial parameters of the model, the key phenology period of the adjustment parameter, the measured value of the phenology period and the direction operator are refined, and the parameter of the crop model based on the genetic algorithm is obtained. The optimization of the framework for constraints and guidance. This paper takes the WheatGrow wheat growth period model as the application object. The results of the parameters optimization test of the measured data of Ji'nan No. 13 wheat variety in Xuzhou, Jining, Weifang, Linyi and other places show that: 1) the preliminary test of four locations verified that the results of the results were as follows: 1.51d, 2.05d, 0.72d and 1.08d, R2 0 respectively. .99, MAD are 1.1d, 1.6d, 0.55d and 0.8d, and the simulation results are better, but some parameters have the parameter values that do not conform to the biological characteristics; 2) after the restriction of the initial range [30,40] of the parameters of the parameter PVT, the parameters of the parameters of the parameter range of the local points are increased slightly, and RMSE is 1.67d, 2.12d, 1.09d, and, respectively. All 0.99, MAD are 1.4d, 1.6d, 0.8d and 1.3d respectively, but the parameters of PVT are in line with Ji'nan 13 and half winter variety characteristics; 3) after expanding the measured values of the key phenology, the verification results of the four regions are 1.24d, 1.56D, 1.22d and 1.48d, respectively, and 0.991, respectively. According to the biological characteristics, the results show that the parameter optimization of the phenology period data is extended, while the effect of the optimization is guaranteed, while the effect of the constrained variety parameters can be played.4), the convergence algebra of the IAGA algorithm is reduced by 8 and 3 generations respectively. The direction operator can speed up the convergence speed of the algorithm. (3) to develop the parameter of the crop model. Optimization and uncertainty analysis tool (CMPOAT) adopts a framework based software development method in component-based software, and develops a tool for automatic parameter adjustment and uncertainty analysis of crop models based on dynamic assembly framework. It can implement the dynamic assembly of crop model, evolutionary algorithm and data processing based on user requirements. According to management and analysis, crop model analysis, parameter calculation, expert knowledge base management, component library management and other functions. The application cases of WheatGrow model and IAGA algorithm show that CMPOAT can analyze crop model uncertainty and optimize model parameters, and provide a powerful software tool for crop model analysis and application.
【學(xué)位授予單位】：南京農(nóng)業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：S126;TP18

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