Comparison of machine learning methods for citrus greening detection on UAV multispectral images
Comparison of machine learning methods for citrus greening detection on UAV multispectral images
复制标题
无人机多光谱图像柑橘黄龙病检测机器学习方法比较
DOI：
10.1016/j.compag.2020.105234
复制
发表时间：
2020-04-01
影响因子：
8.3
通讯作者：
Tong, Zejing
中科院分区：
文献类型：
--
作者：
Lan, Yubin;Huang, Zixiao;Tong, Zejing
Citrus Huanglongbing (HLB), also known as citrus greening, is the most destructive disease in the citrus industry. Detecting this disease as early as possible and eradicating the roots of HLB-infected trees can control its spread. Ground diagnosis is time-consuming and laborious. Large area monitoring method of citrus orchard with high accuracy is rare. This study evaluates the feasibility of large area detection of citrus HLB by low altitude remote sensing and commits to improve the accuracy of large-area detection. A commercial multispectral camera (ADC-lite) mounted on DJI M100 UAV(unmanned Aerial Vehicle) was used to collect green, red and near-infrared multispectral image of large area citrus orchard, a linear-stretch was performed to remove noise pixel, vegetation indices (VIs) were calculated followed by correlation analysis and feature compression using PCA (principal components analysis) and AutoEncoder to discover potential features. Several machine learning algorithms, such as support vector machine (SVM), k-nearest neighbour (kNN), logistic regression (LR), naive Bayes and ensemble learning, were compared to model the healthy and HLB-infected samples after parameter optimization. The results showed that the feature of PCA features of VIs combining with original DN (digital numbers) value generally have highest accuracy and agreement in all models, and the ensemble learning and neural network approaches had strong robustness and the best classification results (100% in AdaBoost and 97.28% in neural network) using threshold strategy.
