本文選題：冠層圖像 + 顏色特征　； 參考：《西北農(nóng)林科技大學學報(自然科學版)》2016年12期

【摘要】：【目的】針對寒地大豆發(fā)生缺素癥狀時冠層顏色變化復雜性,建立基于冠層圖像顏色特征的大豆缺素癥狀識別新方法�！痉椒ā坎捎脽o土盆栽試驗,以墾農(nóng)18為供試大豆品種,設(shè)計缺氮、缺磷、缺鉀3種營養(yǎng)狀況,采集大豆缺素癥狀的冠層圖像樣本,利用圖像灰度直方圖結(jié)合主成分分析方法,提取大豆冠層圖像的紅光值R、綠光值G、藍光值B,計算最佳顏色特征藍光標準化值B/(R+G+B)和綠光標準化值G/(R+G+B),將其作為正則化模糊神經(jīng)網(wǎng)絡(luò)輸入向量,并利用實數(shù)編碼的遺傳算法改進傳統(tǒng)梯度下降學習算法,將其作為模糊神經(jīng)網(wǎng)絡(luò)的學習方法,同時應(yīng)用傳統(tǒng)梯度下降算法和改進梯度下降算法訓練神經(jīng)網(wǎng)絡(luò)參數(shù)并比較�！窘Y(jié)果】應(yīng)用遺傳計算改進的梯度下降學習算法計算時,迭代次數(shù)為277次,其各項計算指標均明顯優(yōu)于傳統(tǒng)梯度下降算法,大豆缺素癥狀識別準確率達100%;而采用傳統(tǒng)的多元線性回歸方程和BP神經(jīng)網(wǎng)絡(luò)算法計算時,識別準確率分別為52.50%,68.33%。【結(jié)論】以大豆冠層圖像顏色特征為基礎(chǔ),利用改進學習算法的神經(jīng)網(wǎng)絡(luò)模型,能夠快速有效地挖掘出大豆缺素癥狀與顏色特征向量之間的模糊邏輯映射關(guān)系,為大豆缺素癥狀識別提供了一種快速且準確的方法。
[Abstract]:[objective] in view of the complexity of color change of canopy in cold region soybean, a new method based on color characteristics of canopy image was established to identify soybean deficiency symptoms. [methods] Soil-free pot experiment was used to test soybean cultivar Kannong 18. Three nutrition conditions were designed: nitrogen deficiency, phosphorus deficiency and potassium deficiency. The canopy image samples of soybean deficiency symptoms were collected, and the image gray histogram combined with principal component analysis (PCA) was used. Red light value R, green light value G, blue light value B of soybean canopy image were extracted, and the best color characteristics, blue light standardization value B / (R G B) and green light standard value G / (R G B) were calculated as input vectors of regularized fuzzy neural network. The traditional gradient descent learning algorithm is improved by real-coded genetic algorithm, which is regarded as the learning method of fuzzy neural network. At the same time, the traditional gradient descent algorithm and the improved gradient descent algorithm are used to train and compare the parameters of the neural network. [results] in the computation of the improved gradient descent learning algorithm based on genetic computation, the number of iterations is 277 times. All the calculation indexes are obviously superior to the traditional gradient descent algorithm, and the accuracy of soybean deficiency symptom recognition is 100, while the traditional multivariate linear regression equation and BP neural network algorithm are used to calculate, [conclusion] based on the color characteristics of soybean canopy image, the neural network model of improved learning algorithm is used. The fuzzy logic mapping relationship between soybean deficiency symptoms and color feature vectors can be quickly and effectively mined, which provides a fast and accurate method for soybean deficiency symptom recognition.
【作者單位】： 黑龍江八一農(nóng)墾大學信息技術(shù)學院;中國農(nóng)業(yè)大學信息與電氣工程學院;黑龍江八一農(nóng)墾大學農(nóng)學院;
【基金】：黑龍江省自然科學基金項目(QC2016031) 黑龍江省大學生創(chuàng)新創(chuàng)業(yè)訓練計劃項目(1022320169433) 黑龍江省教育廳科學技術(shù)研究項目(12521375)
【分類號】：S435.651;TP391.41

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