Estimating Grass Sward Quality and Quantity Parameters Using Drone Remote Sensing with Deep Neural Networks

编号 030034602

推送时间 20220606

研究领域 森林经理 

年份 2022 

类型 期刊 

语种 英语

标题 Estimating Grass Sward Quality and Quantity Parameters Using Drone Remote Sensing with Deep Neural Networks

来源期刊 REMOTE SENSING

期 第346期

发表时间 20220603

关键词 drone;  remote sensing;  hyperspectral;  RGB;  CNN;  image transformer;  silage production;  grass sward; 

摘要 The objective of this study is to investigate the potential of novel neural network architectures for measuring the quality and quantity parameters of silage grass swards, using drone RGB and hyperspectral images (HSI), and compare the results with the random forest (RF) method and handcrafted features. The parameters included fresh and dry biomass (FY, DMY), the digestibility of organic matter in dry matter (D-value), neutral detergent fiber (NDF), indigestible neutral detergent fiber (iNDF), water-soluble carbohydrates (WSC), nitrogen concentration (Ncont) and nitrogen uptake (NU); datasets from spring and summer growth were used. Deep pre-trained neural network architectures, the VGG16 and the Vision Transformer (ViT), and simple 2D and 3D convolutional neural networks (CNN) were studied. In most cases, the neural networks outperformed RF. The normalized root-mean-square errors (NRMSE) of the best models were for FY 19% (2104 kg/ha), DMY 21% (512 kg DM/ha), D-value 1.2% (8.6 g/kg DM), iNDF 12% (5.1 g/kg DM), NDF 1.1.% (6.2 g/kg DM), WSC 10% (10.5 g/kg DM), Ncont 9% (2 g N/kg DM), and NU 22% (11.9 N kg/ha) using independent test dataset. The RGB data provided good results, particularly for the FY, DMY, WSC and NU. The HSI datasets provided advantages for some parameters. The ViT and VGG provided the best results with the RGB data, whereas the simple 3D-CNN was the most consistent with the HSI data.

服务人员 付贺龙

服务院士 唐守正

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