Precision Agriculture

Precision agriculture studies how to sense variation within fields and use it to tailor decisions such as irrigation, fertilization, pest control, and harvest timing. Common approaches combine satellite and UAV imagery, field and IoT sensors, and crop or weather data with computer vision, time-series learning, crop models, and spatial analytics; autonomous tractors and spraying robots turn estimates into site-specific actions. Work also explores uncertainty-aware decision support, edge AI, and language-model or reinforcement-learning agents, evaluated on image benchmarks, cross-season and cross-site tests, and field trials. A central challenge is moving beyond high in-domain prediction scores: domain shifts, sparse labels, connectivity limits, and uncertain economics can undermine recommendations, making robust validation and measurement of yield, input use, and profit essential.