ArticlePlants (Basel, Switzerland)2024
Large-Scale Mapping of Maize Plant Density Using Multi-Temporal Optical and Radar Data: Models, Potential and Application Strategy.
Article in Plants (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Accurate crop density estimation is critical for effective agricultural resource management, yet existing methods face challenges due to data acquisition difficulties and low model usability caused by inconsistencies between optical and radar imagery. This study presents a novel approach to maize density estimation by integrating optical and radar data, addressing these challenges with a unique mapping strategy. The strategy combines available data selection, key feature extraction, and optimization to improve accuracy across diverse growth stages. By identifying critical features for maize density and incorporating machine learning to explore optimal feature combinations, we developed a multi-temporal model that enhances estimation accuracy, particularly during leaf development, stem elongation, and tasseling stages (R
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