本文選題：模型 + 施肥決策��； 參考：《吉林農(nóng)業(yè)大學(xué)》2016年博士論文

【摘要】：本文結(jié)合GPS技術(shù)、數(shù)據(jù)庫技術(shù)和GIS二次開發(fā)技術(shù),針對縣級農(nóng)業(yè)技術(shù)管理部門指導(dǎo)農(nóng)戶科學(xué)施肥的實(shí)際,運(yùn)用可視化程序設(shè)計(jì)語言Visual Basci 6.0和Mapobjects2.4,構(gòu)建縣域統(tǒng)計(jì)分區(qū)施肥模型和應(yīng)用平臺,建立了符合實(shí)際的施肥模型,有效解決了科學(xué)施肥的問題。研究區(qū)域?yàn)榧质￠L春市九臺區(qū),建立土壤養(yǎng)分?jǐn)?shù)據(jù)庫、地形圖和土地利用現(xiàn)狀圖,利用九臺區(qū)測土配方施肥的土壤養(yǎng)分歷史數(shù)據(jù)進(jìn)行插值形成土壤養(yǎng)分圖,以及主要作物的養(yǎng)分需求數(shù)據(jù),建立短信平臺施肥決策智能專家系統(tǒng),用戶可利用手機(jī)或者能發(fā)送短信的GSM短信貓,依據(jù)格式要求發(fā)送用戶地塊GPS數(shù)據(jù)、作物目標(biāo)平均產(chǎn)量等信息,短信平臺接收后就可以通過系統(tǒng)施肥模型的運(yùn)算和優(yōu)化,為用戶發(fā)回施肥決策信息,指導(dǎo)用戶科學(xué)施肥。本系統(tǒng)是對測土配方施肥成果的有效推廣,對于農(nóng)戶非常便捷,對解決測土配方施肥推廣應(yīng)用和最后“一公里”的瓶頸問題有很大作用,對解決測土配方施肥實(shí)時決策和通訊的技術(shù)問題,為農(nóng)業(yè)技術(shù)加速度推廣有著深遠(yuǎn)的意義。本文主要進(jìn)行以下研究。1.利用神經(jīng)網(wǎng)絡(luò)對樣本數(shù)據(jù)進(jìn)行觀測和計(jì)算,通過氮肥、磷肥、鉀肥的加權(quán),同時對玉米產(chǎn)量和肥料用量的實(shí)際比較,建立土壤施肥模型,最后利用GSM短信貓和中國移動公司手機(jī)接收信號相匹配的應(yīng)用軟件進(jìn)行施肥決策,將所需要的肥量用量以短信的形式反饋到用戶手機(jī)上。結(jié)果表明,這種將神經(jīng)網(wǎng)絡(luò)應(yīng)用于建立土壤施肥模型,會得到良好的施肥決策效果。2.利用統(tǒng)計(jì)分區(qū)代替?zhèn)鹘y(tǒng)的人為分區(qū)方法,建立統(tǒng)計(jì)分區(qū)施肥模型,并結(jié)合九臺區(qū)施肥決策效果分析,檢驗(yàn)該模型的科學(xué)性和適宜性,旨在為作物施肥決策提供新的可靠決策技術(shù)方法,最后通過短信平臺軟件將施肥結(jié)果反饋到農(nóng)戶的手機(jī)。結(jié)果表明,這種基于地統(tǒng)計(jì)學(xué)建立的施肥決策模型計(jì)算出的高、中、低肥力下的平均施肥量,對農(nóng)戶施肥指導(dǎo)具有良好的科學(xué)性。3.本文的核心模型是統(tǒng)計(jì)分區(qū)施肥模型(Statistical Division Fertilization Model,SDFM),它是依據(jù)在一定區(qū)域內(nèi)的某一作物常年產(chǎn)量和土壤養(yǎng)分進(jìn)行統(tǒng)計(jì)分析,劃分高、中、低產(chǎn)量和土壤養(yǎng)分的區(qū)間而建立的。根據(jù)在該區(qū)域作物產(chǎn)量與施肥用量的關(guān)系,確定高、中、低產(chǎn)量區(qū)間的氮、磷、鉀肥平均量,再以土壤養(yǎng)分測試值進(jìn)行校正調(diào)整,以此決策施肥用量。用該模型決策施肥量不受所采用土壤養(yǎng)分測定形態(tài)的影響,統(tǒng)計(jì)分區(qū)施肥模型避開了養(yǎng)分平衡法中難以確定的參數(shù),應(yīng)用區(qū)域作物產(chǎn)量的統(tǒng)計(jì)量劃分耕地肥力級別,根據(jù)此值確定平均適宜施肥量,并對土壤養(yǎng)分作相同的統(tǒng)計(jì)分析,確定各級土壤適宜養(yǎng)分量,通過產(chǎn)量差異和土壤養(yǎng)分差異雙重調(diào)整施肥用量。4.通過對神經(jīng)網(wǎng)絡(luò)和統(tǒng)計(jì)分區(qū)建立施肥模型的比較,選擇統(tǒng)計(jì)分區(qū)模型作為唯一的決策模型嵌入到作者研發(fā)的測土配方施肥平臺中,通過計(jì)算、識別、查詢等方式,利用通過GSM短信貓,實(shí)現(xiàn)自動向農(nóng)戶回復(fù)決策短信。5.采用Microsoft Access建立了施肥決策系統(tǒng)的屬性數(shù)據(jù)庫,通過ArcGIS Server和ArcGIS Engine建立GIS土壤養(yǎng)分分布圖,以及由矢量圖層建立空間數(shù)據(jù)庫,結(jié)合本研究所需數(shù)據(jù)的要求,并進(jìn)行無縫對接。
[Abstract]:In this paper, based on GPS technology, database technology and the two development technology of GIS, according to the practice of agricultural technology management department at county level to guide farmers' scientific fertilization, the application of visual programming language Visual Basci 6 and Mapobjects2.4 is used to construct the county statistical division fertilization model and the application platform, and a practical fertilizer model is established, which can be effectively solved. The research area is the problem of scientific fertilization. The research area is nine districts of Changchun city of Jilin province. The database of soil nutrients, topographic map and the status map of land use are set up. The soil nutrient map of the soil and the nutrient requirement data of the main crops are interpolated and the nutrient requirement data of the main crops are used to establish the decision wisdom of the short message platform fertilization. In the expert system, the user can use the mobile phone or the GSM short message cat that can send short message, and send the information of the GPS data of the user block and the average yield of the crop target according to the format requirements. After the reception of the SMS platform, the application and optimization of the system fertilization model can be sent back to the user, and the user can be used for scientific fertilization. This system is the system. The effective popularization of the results of soil testing formula fertilization is very convenient for farmers, and it has a great effect on solving the bottleneck problem of the popularization and application of soil testing formula fertilization and the last "one kilometer". It has far-reaching significance for solving the problem of real-time decision-making and communication of soil testing formula fertilization, which is of profound significance for the promotion of agricultural technology acceleration. .1. is used to observe and calculate the sample data by using neural network. Through the weighting of nitrogen fertilizer, phosphate fertilizer and potash fertilizer, the soil fertilization model is established by comparing the actual comparison of Maize Yield and fertilizer amount. Finally, the application software of GSM short message cat and China Mobile mobile phone receiving signal is used to make fertilizer decision, which will be needed. The amount of fertilizer is fed back to the user's cell phone in the form of short message. The result shows that the application of the neural network to the establishment of soil fertilization model will get a good effect of fertilization decision.2.. The statistical zoning method is used to replace the traditional artificial partition method, and the statistical partition fertilization model is established, and the results are analyzed and tested in combination with the effect of fertilization decision in nine regions. The scientific and suitability of the model is designed to provide a new and reliable decision technology method for crop fertilization decision. Finally, the fertilization results are fed back to the mobile phone by the SMS platform software. The results show that the fertilizer decision model based on Geostatistics is used to calculate the average amount of fertilizer under the high, medium and low fertility, and to fertilize the farmers. The core model of.3. with good scientific nature is the statistical partition fertilization model (Statistical Division Fertilization Model, SDFM). It is based on the statistical analysis of the annual yield and soil nutrients of a certain crop in a certain region, and is established for the division of high, middle, low yield and soil nutrients. The relationship between crop yield and the amount of fertilizer applied to determine the average amount of nitrogen, phosphorus, potassium fertilizer in the high, middle and low yield regions, and then adjust and adjust the soil nutrient test value, so as to decide the amount of fertilizer. The determined parameters, using the statistics of regional crop yield to divide the fertility level of cultivated land, determine the average suitable fertilizer amount according to this value, and make the same statistical analysis of soil nutrients, determine the suitable nutrient components at all levels, and adjust the amount of fertilizer.4. through the difference of yield and the difference of soil nutrients through the neural network and statistical division. In comparison with the model of fertilizer application, the statistical partition model is selected as the only decision model to be embedded in the soil formula fertilization platform developed by the author. By calculating, identifying and querying, using the GSM short message cat to realize the automatic reply decision message.5. to the farmer, the attribute database of the fertilization decision system is established by Microsoft Access. ArcGIS Server and ArcGIS Engine are used to establish the distribution map of soil nutrients in GIS, and the spatial database is set up by the vector layer, and the requirements of the data required in this study are combined, and the seamless docking is carried out.

【學(xué)位授予單位】：吉林農(nóng)業(yè)大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：TP183;S147

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