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

14 citations as recorded by crossref.

1. A Data-Driven Review of Remote Sensing-Based Data Fusion in Precision Agriculture From Foundational to Transformer-Based Techniques M. Saki et al.
2. UAV Multisensory Data Fusion and Multi-Task Deep Learning for High-Throughput Maize Phenotyping C. Nguyen et al.
3. UAS-based remote sensing for agricultural Monitoring: Current status and perspectives J. Wang et al.
4. LiDAR applications in precision agriculture for cultivating crops: A review of recent advances G. Rivera et al.
5. Advancing table beet root yield estimation via unmanned aerial systems (UAS) multi-modal sensing M. Saif et al.
6. Hyperspectral Analysis and Regression Modeling of SPAD Measurements in Leaves of Three Mangrove Species H. Li et al.
7. Potato Leaf Area Index Estimation Using Multi-Sensor Unmanned Aerial Vehicle (UAV) Imagery and Machine Learning T. Yu et al.
8. Corn Grain Yield Prediction Using UAV-Based High Spatiotemporal Resolution Imagery, Machine Learning, and Spatial Cross-Validation P. Killeen et al.
9. Bridging the gap between crop breeding and GeoAI: Soybean yield prediction from multispectral UAV images with transfer learning J. Skobalski et al.
10. Methods and Applications of 3D Ground Crop Analysis Using LiDAR Technology: A Survey M. Micheletto et al.
11. Estimating Crop Seed Composition Using Machine Learning from Multisensory UAV Data K. Dilmurat et al.
12. An Overview of Machine Learning Applications on Plant Phenotyping, with a Focus on Sunflower L. Centorame et al.
13. Advancements in 3D field-crop phenotyping using point clouds: a comparative review of sensor technology, target traits, and challenges under controlled and field conditions E. Omia et al.
14. Spatial Decision Support Systems with Automated Machine Learning: A Review R. Wen & S. Li