ABSTRACT
Crop yield forecasting is crucial for predicting harvest outcomes, influencing governmental decisions, market prices, and import/export regulations. It expedites warehouse construction and enables proactive measures, such as timely humanitarian aid in response to potential crop losses from natural disasters. Acknowledging the adverse effects of excessive fertilizer use, the review emphasizes optimal fertilizer recommendations and advanced remote sensing techniques. The literature overview explores diverse methodologies, including machine learning and statistical approaches, focusing on regression techniques applied to satellite data. The study develops and compares models, presenting calculation results and evaluating against reference model.ESP8266 enables real-time monitoring of soil temperature, humidity, and moisture content. Based on this data, it provides precise fertilizer recommendations, optimizing crop growth and minimizing environmental impact.
