Detection of Citrus Huanglongbing Based on Multi-Input Neural Network Model of UAV Hyperspectral Remote Sensing
Detection of Citrus Huanglongbing Based on Multi-Input Neural Network Model of UAV Hyperspectral Remote Sensing
复制标题
基于无人机高光谱遥感多输入神经网络模型的柑橘黄龙病检测
DOI：
10.3390/rs12172678
复制
发表时间：
2020-09-01
期刊：
影响因子：
5
通讯作者：
Lan, Yubin
中科院分区：
文献类型：
--
作者：
Deng, Xiaoling;Zhu, Zihao;Lan, Yubin
柑橘是世界上重要的经济作物，而柑橘黄龙病是柑橘生产中的一种毁灭性病害。为了有效地检测大规模果园柑橘树的HLB胁迫程度，利用无人机高光谱遥感工具对HLB进行快速检测。在DJI Matrice 600 Pro的无人机上安装了Cubert S185(机载高光谱相机)来拍摄高光谱遥感图像，并使用ASD Handheld2(光谱仪)来验证遥感数据的有效性。利用相关验证的无人机高光谱遥感数据，提取基于单像素的冠层光谱样本进行处理和分析。利用改进选择算子的遗传算法提取的特征条带分别为468 nm、504 nm、512 nm、516 nm、528 nm、536 nm、632 nm、680 nm、688 nm和852 nm。提出的植被指数多特征融合方法和基于堆叠自动编码神经网络特征带构造的冠层光谱特征参数，训练集分类正确率为99.33%，损失为0.0783，验证集分类正确率为99.72%，损失为0.0585。这一性能比基于全频带自编码神经网络的性能要高。田间试验结果表明，该模型能够有效地检测出高寒植株，并输出病害在冠层内的分布情况，从而有效地判断大面积的植物病害程度。
Citrus is an important cash crop in the world, and huanglongbing (HLB) is a destructive disease in the citrus industry. To efficiently detect the degree of HLB stress on large-scale orchard citrus trees, an UAV (Uncrewed Aerial Vehicle) hyperspectral remote sensing tool is used for HLB rapid detection. A Cubert S185 (Airborne Hyperspectral camera) was mounted on the UAV of DJI Matrice 600 Pro to capture the hyperspectral remote sensing images; and a ASD Handheld2 (spectrometer) was used to verify the effectiveness of the remote sensing data. Correlation-proven UAV hyperspectral remote sensing data were used, and canopy spectral samples based on single pixels were extracted for processing and analysis. The feature bands extracted by the genetic algorithm (GA) of the improved selection operator were 468 nm, 504 nm, 512 nm, 516 nm, 528 nm, 536 nm, 632 nm, 680 nm, 688 nm, and 852 nm for the HLB detection. The proposed HLB detection methods (based on the multi-feature fusion of vegetation index) and canopy spectral feature parameters constructed (based on the feature band in stacked autoencoder (SAE) neural network) have a classification accuracy of 99.33% and a loss of 0.0783 for the training set, and a classification accuracy of 99.72% and a loss of 0.0585 for the validation set. This performance is higher than that based on the full-band AutoEncoder neural network. The field-testing results show that the model could effectively detect the HLB plants and output the distribution of the disease in the canopy, thus judging the plant disease level in a large area efficiently.