Field and Weather Data Platform for Crop Yield Prediction

Added Jun 2025 3 design docs

Farmers make planting, irrigation, and selling decisions months before harvest, largely on instinct, because yield forecasting has traditionally required agronomists and expensive tooling. Making credible predictions accessible from data a farmer already has, namely field characteristics and local weather records, changes how those decisions get made. The intern builds a Flask platform in Python where users enter field data and upload weather records, with pandas pipelines cleaning and structuring the inputs. A generative AI layer then produces the core value: yield predictions accompanied by plain-language explanations and concrete improvement suggestions, such as adjusting irrigation timing or revising nutrient plans. The app includes visualization of yield trends across seasons, exportable reports suitable for lenders or cooperatives, and user profiles that keep each farm's history separate and private. The intern practices the full applied AI workflow: ingesting and cleaning agricultural data, designing prompts and context so the AI reasons over real field inputs rather than generalities, and presenting probabilistic output responsibly. The project also builds genuine domain fluency in agricultural analytics, a growing field for data-minded engineers.

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