本文選題：大豆病蟲害診斷 + 層次分析法��； 參考：《東北農(nóng)業(yè)大學》2017年碩士論文

【摘要】：在我國專業(yè)農(nóng)業(yè)診斷知識的匱乏和專業(yè)農(nóng)業(yè)專家的稀缺之間的矛盾已經(jīng)日益嚴重,也嚴重阻礙了我國農(nóng)業(yè)精準化、現(xiàn)代化的發(fā)展,而解決這一矛盾的重要渠道,就是實現(xiàn)農(nóng)業(yè)的智能化。在我國眾多大豆種植區(qū)域中病蟲害的存在往往會造成10%以上的直接經(jīng)濟損失,個別地區(qū)會達到30%以上,多種形式的病蟲害已經(jīng)極大的制約我國出產(chǎn)的大豆的產(chǎn)量及品質(zhì)。目前人工智能技術(shù)已廣泛應(yīng)用于疾病診斷領(lǐng)域,人工神經(jīng)網(wǎng)絡(luò)在作物病蟲害診斷中的應(yīng)用已成為一種流行趨勢。因此本文擬針對大豆病蟲害進行精準判定,選取模糊神經(jīng)網(wǎng)絡(luò)進行模型建立,并引入AHP層次分析法自動生成和調(diào)整隸屬度函數(shù),探討結(jié)合模糊神經(jīng)網(wǎng)絡(luò)與AHP層次分析法進行病蟲害診斷的可行途徑。通過仿真實驗顯示,利用模糊神經(jīng)網(wǎng)絡(luò)與層次分析法相結(jié)合的模型用于大豆病蟲害診斷具有泛化能力強、診斷速度快、正確率高等優(yōu)點,不失為一個好的選擇。具體內(nèi)容如下:首先,輸出采用7種我國具有代表性的食心蟲等蟲害。對182個大豆蟲害樣品,依據(jù)危害方式、危害癥狀等8種性狀進行診斷,選擇136個大豆蟲害樣本作為訓練集,并用46個樣本作為測試集。通過對大豆病蟲害癥狀的收集整理和分析,分別使用對輸入/輸出向量進行數(shù)字化編碼和對輸入使用AHP層次分析法,將用兩種方法處理后的數(shù)據(jù)用作神經(jīng)網(wǎng)絡(luò)的輸入向量。其次,分別建立3種用于訓練和仿真的神經(jīng)網(wǎng)絡(luò)模型。分析BP神經(jīng)網(wǎng)絡(luò)中的最佳隱含層節(jié)點數(shù)、訓練目標、學習速率和訓練次數(shù)等參數(shù)對網(wǎng)絡(luò)性能的影響;論證RBF徑向基神經(jīng)網(wǎng)絡(luò)中徑向基密度參數(shù)對訓練結(jié)果的影響;同時論證模糊神經(jīng)網(wǎng)絡(luò)中隱層節(jié)點數(shù)和訓練次數(shù)等參數(shù)對模型的響應(yīng)結(jié)果。最后,對比三種類型神經(jīng)網(wǎng)絡(luò)對不同大豆病蟲害進行診斷后的準確率,從實驗結(jié)果論證了模糊神經(jīng)網(wǎng)絡(luò)應(yīng)用于大豆病蟲害具有最佳診斷效果。實驗結(jié)果表明,選擇層次分析法對輸入進行處理以及模糊神經(jīng)網(wǎng)絡(luò)進行建模,在46個測試樣本中,共有44個樣本進行了預(yù)測,識別率高達95%,證明了該方法對大豆害蟲的判別是可行的。
[Abstract]:The contradiction between the lack of specialized agricultural diagnostic knowledge and the scarcity of specialized agricultural experts in China has become increasingly serious, which has also seriously hindered the development of agricultural precision and modernization in our country. It is to realize the intelligence of agriculture. In many soybean planting areas in China, the existence of pests and diseases will often cause more than 10% of direct economic losses, and a few areas will reach more than 30%. Many forms of diseases and pests have greatly restricted the yield and quality of soybean produced in China. At present, artificial intelligence technology has been widely used in the field of disease diagnosis, and the application of artificial neural network in the diagnosis of crop diseases and insect pests has become a popular trend. Therefore, this paper intends to accurately judge soybean pests and diseases, select fuzzy neural network to establish the model, and introduce AHP to automatically generate and adjust membership function. This paper discusses the feasible ways to diagnose diseases and insect pests by combining fuzzy neural network with AHP. The simulation results show that the model combined with fuzzy neural network and analytic hierarchy process has the advantages of strong generalization ability, fast diagnosis speed and high accuracy, and it is a good choice. The specific contents are as follows: first, the output uses 7 representative insect pests and other pests in China. Based on the diagnosis of 182 soybean pest samples, 136 soybean pest samples were selected as training set and 46 samples were used as test sets. By collecting and analyzing the symptoms of soybean diseases and insect pests, the input / output vector was digitalized and the input was analyzed by AHP. The data processed by two methods was used as the input vector of neural network. Secondly, three neural network models are established for training and simulation. The effects of the optimal number of hidden layer nodes, training target, learning rate and training times on the performance of the neural network are analyzed, and the effects of radial basis function density parameters on the training results in RBF radial basis function neural network are demonstrated. At the same time, the response results of the parameters such as the number of hidden layer nodes and the number of training times to the model in the fuzzy neural network are discussed. Finally, the accuracy of three types of neural networks for the diagnosis of different soybean pests and diseases was compared, and the best diagnostic effect of the fuzzy neural network applied to soybean diseases and insect pests was demonstrated from the experimental results. The experimental results show that the analytic hierarchy process (AHP) is selected to deal with the input and the fuzzy neural network is used to model the model. Out of 46 test samples, 44 samples are predicted. The recognition rate is as high as 95%, which proves that this method is feasible for the identification of soybean pests.
【學位授予單位】：東北農(nóng)業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：S435.651;TP183

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