基于计算机视觉的棉花植株含水量的预测研究
首发时间:2014-03-06
摘要:本文应用计算机视觉技术分析棉花冠层图像,提取图像颜色特征值建立不同生育期棉花植株含水量的预测模型,实现膜下滴灌条件下棉花植株快速、无损、低成本的植株含水量预测,为灌溉决策提供简单快速的方法。在棉花的初花、盛花期和花铃期使用数码相机拍摄不同程度干旱胁迫的棉花冠层图像,采用图像识别软件提取冠层图像的HSBr与RGB颜色特征值,同时在实验室采用常规方法测定棉花植株含水量。结果表明在不同生育期颜色特征值和植株含水量间的相关性达到显著水平。将颜色特征值分组后利用逐步回归法建立不同生育期棉花植株含水量的最佳预测模型。初花期颜色特征值G/R、盛花期特征值S/(B/R)、S/(2G-R-B)和花铃期特征值2G-R-B与植株含水量建立了最佳预测模型,方程相关系数分别为0.886、0.890 和0.831。模型检验结果表明不同时期建立的最佳模型预测的植株含水量与实测含水量间的决定系数分别为0.874、0.602和0.790;RMSE分别为1.476、1.096和0.956;相对误差RE均小于2%。合并不同生育期数据,建立适用于不同生育期预测植株含水量的通用模型。经检验,最佳通用模型的预测精度高于初花期的最佳方程,低于盛花期和花铃期的最佳模型,RMSE为1.386,相对误差RE为1.40%。结果表明,自然光照条件下应用数码相机采集不同生育期的棉花冠层图像获取的颜色特征值能够较好的预测棉花植株含水量。
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Research on cotton water content prediction based on computer-vision
Abstract:This paper aimed at establishing a rapidly, non-destructively and low-costly model to predict cotton water content in different growth periods based on computer-vision technology,for providing theoretical basis for irrigation decision-making. Cotton canopy digital image under different drought stress were captured by Nikon D90 digital camera during the period of cotton initial bloom stage, full bloom stage and flowering and boll-setting stage, respectively. The color characteristic parameters of the images of cotton canopy were extracted by a image processing software. Green parts of cotton image were extracted at hue between 60-180 degree. Then RGB and HSBr value of cotton canopy image were extracted. Meanwhile, the cotton water content for each sample was detected using the oven dry method in the laboratory. The result showed that the correlation coefficient between color characteristic parameters and cotton water content reached extremely significant level in different growth periods. The cotton water content predictive models at different growth stages were established separately by using the stepwise regression method. The color characteristic parameters of G/R in cotton initial bloom stage; S/(B/R)and S/(2G-R-B) in full bloom stage and 2G-R-B in flowering and boll-setting stage with cotton water content established the best prediction model respectively. The correlation coefficient of the predict model were 0.886, 0.890 and 0.831 in three different periods. The collected samples were used to construct a large-scale data set,two-thirds of which were used as the train set and the remaining one third were used as the test set. In order to test the prediction accuracy of models, the determination coefficient (R2) and root mean square error (RMSE) between plant water content of the measured values and predicted value were calculated by mathematical methods. The water content predicts model in initial bloom stage had highest accuracies, the R2 reached to 0.874 and root mean square error was 1.476. The model in full bloom stage had higher accuracies, the R2 reached to 0.602 and root mean square error was 1.096. The model based in flowering and boll-setting stage had lower accuracies, the validation R2 reached to 0.790 and root mean square error was 0.956. The relative error between predicted values and measured values were less than 2% in different periods, and the predicted results were reliable. The data of different growth periods was merged, established the universal model suitable for predicting plant water content at different stages .After accuracy test, universal model prediction accuracy was higher than the best model of initial bloom stage , and less than the best model of full bloom stage. The root mean square error between predicted values and measured values was 1.096 and relative error was 1.40%. It was concluded that color characteristic parameters of the canopy images can predict cotton crop water content based on computer-vision under the conditions of field natural light.
Keywords: computer vision cotton plant water content color characteristic parameters model
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