本文選題：無人機 + 回歸分析��； 參考：《沈陽農(nóng)業(yè)大學學報》2017年06期

【摘要】：葉綠素相對含量(soil and plant analyzer development,SPAD)是評價水稻健康狀況的重要農(nóng)學參數(shù),為了解決傳統(tǒng)監(jiān)測方法工作量大,效率低的問題,以東北粳稻為研究對象,采用不同施肥處理開展小區(qū)試驗,利用無人機低空遙感技術(shù)分別獲取水稻分蘗期、拔節(jié)孕穗期、抽穗灌漿期水稻冠層高清數(shù)碼影像,同時利用葉綠素儀測量水稻冠層SPAD值,并對無人機高清數(shù)碼影像反演SPAD的可行性及方法進行研究。結(jié)合k-means聚類和閾值分割的方法去除背景提取出水稻葉片的RGB值,構(gòu)建出R、G、B及G/R、G/B、B/R、R-B、G-R、NRI、NGI、NBI共11種顏色參數(shù),并分別用11種參數(shù)和水稻葉片SPAD做相關(guān)性分析,分析結(jié)果表明NRI、B/R、R-B 3種參數(shù)和SPAD值高度相關(guān)。分別采用一元線性回歸分析法和BP神經(jīng)網(wǎng)絡法對3種參數(shù)和SPAD的關(guān)系進行建模并對建模精度進行分析。結(jié)果表明:無人機高清影像反演SPAD是可行的,其中一元線性回歸分析中,NRI和SPAD的建模精度高于B/R和R-B,均方根誤差(RMSE)為1.51;基于NRI、B/R和R-B的多特征輸入的BP神經(jīng)網(wǎng)絡預測粳稻SPAD的RMSE為1.354,相比基于NRI的一元線性回歸分析模型精度提升11%,BP模型能較好地對東北粳稻的SPAD進行反演,能為無人機低空遙感反演粳稻SPAD提供理論依據(jù)和實現(xiàn)方法。
[Abstract]:Relative chlorophyll content (and plant analyzer) is an important agronomic parameter to evaluate rice health status. In order to solve the problem of heavy workload and low efficiency of traditional monitoring methods, Northeast japonica rice was used as the research object and different fertilization treatments were used to carry out plot experiment. The high-definition digital images of rice canopy at tillering stage, jointing and booting stage and heading and filling stage were obtained by using UAV low-altitude remote sensing technology, and SPAD values of rice canopy were measured by chlorophyll meter. The feasibility and method of SPAD inversion of UAV high-definition digital image are studied. In combination with k-means clustering and threshold segmentation, RGB values of rice leaves were extracted by removing background, and 11 color parameters were constructed, and 11 color parameters were analyzed by using 11 parameters and SPAD of rice leaves. The results show that there is a high correlation between the three parameters and the SPAD value. The relationship between the three parameters and SPAD is modeled by linear regression analysis and BP neural network, respectively, and the modeling accuracy is analyzed. The results show that the SPAD inversion of UAV high-definition image is feasible. The modeling accuracy of NRI and SPAD in univariate linear regression analysis is higher than that of B / R and R-Band the root mean square error (RMSE) is 1.51.The BP neural network based on NRI-B / R and R-B for predicting SPAD of japonica rice is 1.354, which is higher than that based on NRI. The SPAD of japonica rice in Northeast China can be retrieved by using BP model with improved accuracy of the model. It can provide theoretical basis and implementation method for retrieving japonica rice SPAD from UAV low altitude remote sensing.
【作者單位】： 沈陽農(nóng)業(yè)大學信息與電氣工程學院/遼寧省農(nóng)業(yè)信息化工程技術(shù)中心;
【基金】：國家重點研發(fā)計劃項目(2016YFD020060307)
【分類號】：S511.22;TP751

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