Скачать или смотреть Agribot: An IoT and ML Based System for Enhanced Precision Farming

Agriculture faces growing challenges due to climate variability, soil degradation, and inefficient resource use. Traditional farming relies on estimations and periodic testing, leading to suboptimal crop selection, inefficient irrigation, and poor pest management. To address these issues, we propose an IoT and machine learning-based precision farming system that integrates real-time environmental monitoring, intelligent crop recommendations, automated irrigation control, and pest detection.
Our system uses IoT-enabled sensors to continuously monitor key soil parameters, including nitrogen (N), phosphorus (P), potassium (K), pH, moisture, temperature, humidity, and total dissolved solids (TDS). This data, along with real-time weather information from the OpenWeather API, is transmitted via Wi-Fi or GSM modules to a cloud platform for analysis. Machine learning algorithms then process the data to provide crop recommendations based on soil fertility and environmental conditions.