農(nóng)業(yè)物聯(lián)網(wǎng)決策系統(tǒng)數(shù)據(jù)處理方法研究
發(fā)布時間：2019-06-05 17:33
【摘要】：農(nóng)業(yè)物聯(lián)網(wǎng)是物聯(lián)網(wǎng)技術(shù)在農(nóng)業(yè)生產(chǎn)管理中的具體應(yīng)用。決策系統(tǒng)是農(nóng)業(yè)物聯(lián)網(wǎng)中非常重要的一個部分,負責數(shù)據(jù)信息的處理、分析、推理和決策,為生產(chǎn)管理提供決策支持以利于農(nóng)業(yè)生產(chǎn)。而糧食產(chǎn)量預(yù)測是決策系統(tǒng)中的重要功能模塊,對農(nóng)作物品種的改良、糧食生產(chǎn)結(jié)構(gòu)的調(diào)整、國家政策方針的制定、加快我國現(xiàn)代化農(nóng)業(yè)生產(chǎn)的建設(shè)和發(fā)展、保證糧食安全、國民經(jīng)濟發(fā)展和國家安全具有重大意義,因此糧食產(chǎn)量的科學(xué)預(yù)測具有重要的戰(zhàn)略地位。本文以云南省蒙自縣試驗地玉米產(chǎn)量為例,研究農(nóng)作物產(chǎn)量預(yù)測方法,以提高農(nóng)作物產(chǎn)量預(yù)測精度。本文的主要內(nèi)容包括以下幾個方面:(1)分析國內(nèi)外農(nóng)作物產(chǎn)量預(yù)測方法的研究現(xiàn)狀,在此基礎(chǔ)上選擇多元線性回歸、人工神經(jīng)網(wǎng)絡(luò)(Artificial Neural Network,ANN)、支持向量機(Support Vector Machine,SVM)方法作為產(chǎn)量預(yù)測方法,并對其理論進行了分析。(2)利用多元線性回歸、ANN、SVM方法對云南省蒙自縣玉米產(chǎn)量進行預(yù)測,對各模型的預(yù)測結(jié)果、預(yù)測誤差進行分析,判斷各模型是否符合精度要求,并采用相關(guān)系數(shù)R、平均絕對誤差MAE和均方根誤差RMSE來衡量模型的總體預(yù)測能力。(3)從各模型檢驗結(jié)果和模型理論兩個方面對建立的多元線性回歸模型、ANN模型和SVM模型進行比較分析,得出最終結(jié)論。在前人的研究基礎(chǔ)上,本論文采用多元線性回歸、ANN和SVM方法,以云南省蒙自縣玉米產(chǎn)量為例,對農(nóng)作物產(chǎn)量預(yù)測進行了研究,得出以下主要結(jié)論:(1)所建立的多元線性回歸模型、ANN模型和SVM模型均能應(yīng)用于玉米產(chǎn)量的預(yù)測,這三個模型的預(yù)測誤差都比較小,預(yù)測精度高。(2)在多元線性回歸模型中,玉米產(chǎn)量和5月的日均參考作物騰發(fā)量、8月的最高氣溫、6月的最低氣溫、8月的最低氣溫關(guān)系緊密,而在ANN模型和SVM模型中,玉米產(chǎn)量和3月到5月的日均參考作物騰發(fā)量、8月的最高氣溫、6月的最低氣溫、8月的最低氣溫關(guān)系緊密。(3)針對云南省蒙自縣玉米產(chǎn)量的預(yù)測,多元線性回歸模型的R值為0.813,MAE值為0.1044,RMSE值為0.1441;ANN模型的R值為0.7479,MAE值為0.1326,RMSE值為0.1691;SVM模型的R值為0.8046,MAE值為0.1179,RMSE值為0.1546。多元線性回歸模型的R值最高,MAE和RMSE值最低,因此其預(yù)測效果最好,其次是SVM模型,ANN模型最差。
[Abstract]:The Internet of Things is the specific application of Internet of Things technology in the management of agricultural production. The decision-making system is a very important part of the agricultural Internet of things, which is responsible for the processing, analysis, reasoning and decision-making of the data information, and provides decision support for the production management to facilitate the agricultural production. and the grain yield prediction is an important function module in the decision-making system, the improvement of the crop variety, the adjustment of the grain production structure, the development of the national policy guideline, the construction and the development of the modern agricultural production in China, and the food security, The development of national economy and national security are of great significance, so the scientific prediction of grain output has an important strategic position. Taking the yield of corn in Mengzi county of Yunnan province as an example, this paper studies the method of crop yield prediction to improve the accuracy of crop yield prediction. The main contents of this paper are as follows: (1) The research status of crop yield prediction method at home and abroad is analyzed. Based on this, a multi-element linear regression, an artificial neural network (ANN) and a Support Vector Machine (SVM) method are selected as the yield prediction method. The theory is analyzed. (2) using the multi-element linear regression, the ANN and the SVM method to forecast the yield of the corn in the Mengzi county of Yunnan province, and analyzing the prediction result and the prediction error of each model, judging whether each model meets the precision requirement, and adopting the correlation coefficient R; The average absolute error MAE and the root mean square error RMSE measure the overall prediction capability of the model. (3) The multi-element linear regression model, the ANN model and the SVM model are compared and analyzed from the two aspects of each model test result and the model theory to obtain the final conclusion. Based on the previous research, this paper uses the method of multiple linear regression, ANN and SVM to study the crop yield prediction by taking the maize yield of Mengzi County in Yunnan province as an example. The main conclusions are as follows: (1) the established multi-component linear regression model, Both the ANN model and the SVM model can be applied to the prediction of corn yield, and the prediction error of the three models is small and the prediction accuracy is high. (2) In the multiple linear regression model, the corn yield and the average daily reference crop evapotranspiration in May, the highest air temperature in August, the minimum temperature in June and the lowest temperature in August, and in the ANN model and the SVM model, The corn yield and the average daily reference crop evapotranspiration from March to May, the highest temperature in August, the minimum temperature in June, and the lowest temperature in August. (3) The R value of the multiple linear regression model is 0.813, the MAE value is 0.1044, the RMSE value is 0.1441, the R value of the ANN model is 0.7479, the MAE value is 0.1326, the RMSE value is 0.1691, the R value of the SVM model is 0.8046, the MAE value is 0.1179, and the RMSE value is 0.1546. The R value of the multiple linear regression model is the highest, and the value of MAE and RMSE is the lowest, so the prediction effect is the best, second is the SVM model, and the ANN model is the worst.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TP391.44;TN929.5
本文編號：2493702
[Abstract]:The Internet of Things is the specific application of Internet of Things technology in the management of agricultural production. The decision-making system is a very important part of the agricultural Internet of things, which is responsible for the processing, analysis, reasoning and decision-making of the data information, and provides decision support for the production management to facilitate the agricultural production. and the grain yield prediction is an important function module in the decision-making system, the improvement of the crop variety, the adjustment of the grain production structure, the development of the national policy guideline, the construction and the development of the modern agricultural production in China, and the food security, The development of national economy and national security are of great significance, so the scientific prediction of grain output has an important strategic position. Taking the yield of corn in Mengzi county of Yunnan province as an example, this paper studies the method of crop yield prediction to improve the accuracy of crop yield prediction. The main contents of this paper are as follows: (1) The research status of crop yield prediction method at home and abroad is analyzed. Based on this, a multi-element linear regression, an artificial neural network (ANN) and a Support Vector Machine (SVM) method are selected as the yield prediction method. The theory is analyzed. (2) using the multi-element linear regression, the ANN and the SVM method to forecast the yield of the corn in the Mengzi county of Yunnan province, and analyzing the prediction result and the prediction error of each model, judging whether each model meets the precision requirement, and adopting the correlation coefficient R; The average absolute error MAE and the root mean square error RMSE measure the overall prediction capability of the model. (3) The multi-element linear regression model, the ANN model and the SVM model are compared and analyzed from the two aspects of each model test result and the model theory to obtain the final conclusion. Based on the previous research, this paper uses the method of multiple linear regression, ANN and SVM to study the crop yield prediction by taking the maize yield of Mengzi County in Yunnan province as an example. The main conclusions are as follows: (1) the established multi-component linear regression model, Both the ANN model and the SVM model can be applied to the prediction of corn yield, and the prediction error of the three models is small and the prediction accuracy is high. (2) In the multiple linear regression model, the corn yield and the average daily reference crop evapotranspiration in May, the highest air temperature in August, the minimum temperature in June and the lowest temperature in August, and in the ANN model and the SVM model, The corn yield and the average daily reference crop evapotranspiration from March to May, the highest temperature in August, the minimum temperature in June, and the lowest temperature in August. (3) The R value of the multiple linear regression model is 0.813, the MAE value is 0.1044, the RMSE value is 0.1441, the R value of the ANN model is 0.7479, the MAE value is 0.1326, the RMSE value is 0.1691, the R value of the SVM model is 0.8046, the MAE value is 0.1179, and the RMSE value is 0.1546. The R value of the multiple linear regression model is the highest, and the value of MAE and RMSE is the lowest, so the prediction effect is the best, second is the SVM model, and the ANN model is the worst.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TP391.44;TN929.5
【參考文獻】
相關(guān)期刊論文 前1條
1 吳玉鳴,李建霞;通徑分析在我國糧食生產(chǎn)相關(guān)研究中的應(yīng)用[J];廣西師范大學(xué)學(xué)報(自然科學(xué)版);2003年03期
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