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The development rate of alfalfa seed crop depends on both environmental conditions and management decisions. Crop management decisions, such as determining when to release pollinators to optimize pollination, can be informed by the identification of plant development stages from remote sensing data. I first identify what electromagnetic wavelengths are sensitive to alfalfa plant development stages using hyperspectral data. A Random Forest regression is used to determine the best Vegetation Index (VI) to monitor how much of the plant is covered in flower. The results indicate that Blue, Green, and Near-Infrared are the important electromagnetic wavelengths for the VI. Imagery collected throughout this study are converted into a VI time-series for analysis. The analysis involves using a state-space model to estimate the percentage of flower cover from observations. We found that a simple state-space model can be used to estimate, as well as predict, the flower cover percentage.

Informing Field Management Decisions to Enhance Alfalfa Seed Production Using Remote Sensing