基于无人机多光谱影像的槟榔黄化病遥感监测

黄化病是一种严重危害槟榔生长的病害,迫切需要及时、准确地监测其侵染的严重度差异和空间分布。低空无人机遥感可有效解决槟榔种植区由于多云雨天气而造成光学卫星影像获取不足,提高槟榔黄化病监测的实时性。该文利用大疆精灵Phantom 4 Pro V2.0四旋翼无人机搭载MicaSense RedEdge-M多光谱相机获取5波段多光谱影像,基于最小冗余最大相关算法(Minimum Redundancy Maximum Relevance,m RMR)从15个潜在的植被指数中优选比值植被指数(Ratio VegetationIndex,RVI)、改进的简单比值指数(ModifiedSimpleRatioIndex,MSR)和花青素反射指数(Anthocyanin Reflectance Index,ARI)作为敏感特征,分别利用后向传播神经网络(Back Propagation Neural Network, BPNN)、随机森林(Random Forest, RF)和支持向量机(Support Vector Machine, SVM)分类算法,构建了槟榔黄化病严重度监测模型。结果表明,BPNN模型总体精度达到91.7%,分别比RF模型和SVM模型提高6.7%和10.0%,且Kappa系数为0.875,为所有模型中最高,漏分、错分误差也最小,健康,轻度和重度分别为11.1%、15.8%,13.6%、9.5%和0、0。研究结果证明了无人机多光谱遥感影像监测槟榔黄化病的可行性,同时也可为其他热带作物病害监测提供案例研究。

Remote sensing monitoring of areca yellow leaf disease based on UAV multi-spectral images

Yellow leaf disease is a serious disease that endangers the growth of areca, it is urgent to monitor the infection severity and spatial distribution in time and accurately. However, the traditional monitoring methods are mainly depend on visual inspection and manual investigation, which affects the efficiency and spatial scope of monitoring. Low altitude UAV remote sensing technology can effectively solve the problems of insufficient optical satellite images acquisition caused by cloudy and rainy weather in areca planting area, and improve the real-time monitoring of areca yellow leaf disease. In this paper, in order to identify the severities and spatial distribution of areca yellow leaf disease, five band(including blue, green, red, near-infrared and red-edge wavebands) multispectral images were obtained by using the Mica Sense RedEdge-M multispectral camera mounted on the DJI Phantom 4 Pro V2.0. Based on the Minimum Redundancy Maximum Relevance(m RMR), three sensitive features were selected from 15 potential vegetation indexes. Using Back Propagation Neural Network(BPNN), Random Forest(RF) and Support Vector Machine(SVM) classification algorithms respectively, a monitoring model of areca yellow leaf disease severity was constructed. A total sixty in-situ sampling points were selected and the disease index(DI) were obtained, according to the characteristics of the disease and the separability of remote sensing images, the severities of the disease were divided into three grades: health(DI<1%), slight(1%≤DI<10%) and serious(DI≥10%), and the number of samples was 18, 22 and 20 respectively. According to the priority principle of importance of feature variables, Ratio Vegetation Index(RVI), Modified Simple Ratio Index(MSR) and Anthocyanin Reflectance Index(ARI) were finally selected. Two-tier neural networks including hidden layer and output layer were used to build the BPNN model. The results showed that the overall accuracy(OA) of BPNN model was 91.7%, which was 6.7% and 10.0% higher than that of RF model and SVM model, respectively. The Kappa coefficient of the BPNN model was 0.875, which was the highest among the three models. In general, the omission errors and commission errors of BPNN model were the smallest, the errors of health, slight and serous levels were 11.1%, 15.8%, 13.6%, 9.5% and 0, 0 respectively. Consequently, it is feasible to monitor the severities of arecanut yellow leaf disease based on the UAV multispectral image. The study can provide a reference for the diseases monitoring of other tropical crops.