ArticleSensors (Basel, Switzerland)2019
Evaluation of Aboveground Nitrogen Content of Winter Wheat Using Digital Imagery of Unmanned Aerial Vehicles.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- DMP-UNet: a multimodal fusion network for high-precision nitrogen status monitoring in wheat from UAV multispectral images.Frontiers in plant science · 2026Article
- Improving sugar beet canopy mapping through UAV image analysis.Scientific reports · 2025Article
- Improved Multi-Stage Rice Above-Ground Biomass Estimation Using Wavelet-Texture-Fused Vegetation Indices from UAV Remote Sensing.Plants (Basel, Switzerland) · 2025Article
- Combining features selection strategy and features fusion strategy for SPAD estimation of winter wheat based on UAV multispectral imagery.Frontiers in plant science · 2024Article
- Multi-dimensional variables and feature parameter selection for aboveground biomass estimation of potato based on UAV multispectral imagery.Frontiers in plant science · 2022Article
- Machine Learning in Agriculture: A Comprehensive Updated Review.Sensors (Basel, Switzerland) · 2021Review
- Estimation of Leaf Nitrogen Content in Wheat Based on Fusion of Spectral Features and Deep Features from Near Infrared Hyperspectral Imagery.Sensors (Basel, Switzerland) · 2021Article
- Applications of UAS in Crop Biomass Monitoring: A Review.Frontiers in plant science · 2021Review
- High Throughput Field Phenotyping for Plant Height Using UAV-Based RGB Imagery in Wheat Breeding Lines: Feasibility and Validation.Frontiers in plant science · 2021Article
- Apple Leaf Diseases Recognition Based on An Improved Convolutional Neural Network.Sensors (Basel, Switzerland) · 2020Article
- Cross-Category Tea Polyphenols Evaluation Model Based on Feature Fusion of Electronic Nose and Hyperspectral Imagery.Sensors (Basel, Switzerland) · 2019Article
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Authors and funding
9 authors.
Funding
Abstract
Nitrogen (N) content is an important basis for the precise management of wheat fields. The application of unmanned aerial vehicles (UAVs) in agriculture provides an easier and faster way to monitor nitrogen content. Previous studies have shown that the features acquired from UAVs yield favorable results in monitoring wheat growth. However, since most of them are based on different vegetation indices, it is difficult to meet the requirements of accurate image interpretation. Moreover, resampling also easily ignores the structural features of the image information itself. Therefore, a spectral-spatial feature is proposed combining vegetation indices (VIs) and wavelet features (WFs), especially the acquisition of wavelet features from the UAV image, which was transformed from the spatial domain to the frequency domain with a wavelet transformation. In this way, the complete spatial information of different scales can be obtained to realize good frequency localization, scale transformation, and directional change. The different models based on different features were compared, including partial least squares regression (PLSR), support vector regression (SVR), and particle swarm optimization-SVR (PSO-SVR). The results showed that the accuracy of the model based on the spectral-spatial feature by combining VIs and WFs was higher than that of VIs or WF indices alone. The performance of PSO-SVR was the best (R
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