基于BA-SVM的冬小麦旱情表型分析与诊断

提升作物水分表型诊断精度和时效性是当前智慧灌溉领域研究的难点和热点之一。该研究针对以上难点提出了一种改进机器视觉算法的冬小麦旱情智能诊断方法。在测坑试验系统中设置了适宜水分处理（CK）、中度干旱处理（T1）、重度干旱处理（T2），通过数码相机获取冬小麦早期RGB高清图像，利用HSV色彩空间改进的K-means聚类算法对小麦图像分割敏感区域，提取图像颜色和纹理特征数据并开展主成分分析，辨别出累计贡献率达到97.2%的前3维主成分。采用蝙蝠算法优化支持向量机（bat algorithm-support vector machine，BA-SVM）惩罚因子$ (c=5) $和核参数(σ=0.1)，建立了基于蝙蝠算法优化的冬小麦旱情感知支持向量机模型，运用主成分分析降维后的识别精度优于其他特征组合，识别正确率为96.5%。明显高于GA-SVM（6.5%）和SVM（9.3%），运行时间分别缩短7、14 s。构建了冬小麦旱情智能诊断方法，可为实时诊断冬小麦旱情和智慧灌溉决策提供可靠方法。

Phenotypic analysis and diagnosis of winter wheat drought based on BA-SVM

Crop drought diagnosis has been an urgent need to be solved with the development of precision irrigation. It is a high demand to improve the accuracy and timeliness of crop water phenotype diagnosis in the field of intelligent irrigation. In this study, an intelligent diagnosis of winter wheat drought was proposed using improved machine vision. Field experiments were carried out to verify. Three treatments were set in the pit test: suitable, moderate drought and severe drought treatment. The early RGB HD images were capture from the winter wheat by digital camera. The sensitive areas of wheat images were segmented by K-means clustering with the improved by HSV color space. The color and texture features were then extracted as principal components. The support vector machine (SVM) that optimized by bat algorithm (BA) was used to classify the data after dimensionality reduction. Compared with the SVM with/without optimization by Genetic Algorithm, the BA-SVM model was more efficient in diagnosis. The K-means clustering combined with HSV color shared the better segmentation than the traditional one. There were the extract seven-dimensional color features of red channel (R), green channel (G), blue channel (B), brightness (V), 2G-R, 2G-B, (G+R+B)/3 and four-dimensional texture features Energy, Homogeneity, Contrast, Correlation. The principal component analysis was used to reduce the dimensionality of 11 dimensional features. There were the first 3-dimensional principal components with a cumulative contribution rate of 97.2%. The BA was used to optimize the penalty factor and kernel parameters of SVM. The recognition accuracy after dimensionality reduction by principal component analysis was superior to other feature combinations. The recognition accuracy was 96.1% and the running time was 31s, which was 8.3%, 7.2% and 4.4% higher than those of color feature, texture feature and color + texture feature, respectively. The running time was shortened by 14, 21 and 25 s, respectively. Compared with the genetic algorithm SVM (GA-SVM) and un-optimized SVM, the dimensionality reduction features were improved by 6.1% and 8.9%, and shortened by 7 and 14 s, respectively. Therefore, the intelligent diagnosis of winter wheat drought was constructed using improved algorithms, such as image segmentation, feature extraction, data dimensionality reduction, data recognition and classification. The finding can provide the real-time diagnosis of winter wheat drought for the decision-making on intelligent irrigation.