AI-DRIVEN MAIZE YIELD FORECASTING USING UNMANNED AERIAL VEHICLE-BASED HYPERSPECTRAL AND LIDAR DATA FUSION

10 citations as recorded by crossref.

1. UAV Multisensory Data Fusion and Multi-Task Deep Learning for High-Throughput Maize Phenotyping C. Nguyen et al. 10.3390/s23041827
2. LiDAR applications in precision agriculture for cultivating crops: A review of recent advances G. Rivera et al. 10.1016/j.compag.2023.107737
3. Hyperspectral Analysis and Regression Modeling of SPAD Measurements in Leaves of Three Mangrove Species H. Li et al. 10.3390/f14081566
4. Potato Leaf Area Index Estimation Using Multi-Sensor Unmanned Aerial Vehicle (UAV) Imagery and Machine Learning T. Yu et al. 10.3390/rs15164108
5. Corn Grain Yield Prediction Using UAV-Based High Spatiotemporal Resolution Imagery, Machine Learning, and Spatial Cross-Validation P. Killeen et al. 10.3390/rs16040683
6. Bridging the gap between crop breeding and GeoAI: Soybean yield prediction from multispectral UAV images with transfer learning J. Skobalski et al. 10.1016/j.isprsjprs.2024.03.015
7. Methods and Applications of 3D Ground Crop Analysis Using LiDAR Technology: A Survey M. Micheletto et al. 10.3390/s23167212
8. Estimating Crop Seed Composition Using Machine Learning from Multisensory UAV Data K. Dilmurat et al. 10.3390/rs14194786
9. An Overview of Machine Learning Applications on Plant Phenotyping, with a Focus on Sunflower L. Centorame et al. 10.3390/agronomy14040719
10. Spatial Decision Support Systems with Automated Machine Learning: A Review R. Wen & S. Li 10.3390/ijgi12010012