A novel approach for monitoring agricultural production process using wireless sensor networks and Machine Learning

The majority of nations rely heavily on agriculture. In India, agriculture directly supports more than half of the country's population. The yield of a given crop is influenced by a number of variables, including the climate, wind speed, soil quality, humidity, etc. The growth of a crop is impacted by these components' ongoing variability. The agricultural industry has benefited from technological advancement. In the agriculture industry, wireless sensor networks and crop yield prediction have had a significant impact. This study introduces a revolutionary Precision Farming method and demonstrates how sensor data can be effectively utilised. The Crop Yield Prediction model receives real-time input from the sensor-generated data. This strategy aids us in getting more accurate results. The research suggests a mobile application that, every split second, sends data from wireless sensors used in precision farming as an input to a crop production forecast model. Additionally, these sensed parameters from different users can be used as a training dataset. This method not only makes the most of the sensor data but also predicts crop yield with accuracy and promptness.