Agriculture Field Automation and Digitization Using Internet of Things and Machine Learning

The real-time smart monitoring with intelligence highly gained significant attention for enhancing the productivity of the crop. Currently, IoT generates a lot of real-time data from the sensors, actuators, and identification technologies. However, extracting the meaningful insights from the data is necessary for realizing the intelligent ecosystem in agriculture. Based upon the previous studies, it is also identified that the limited studies have merely implemented machine learning (ML) on real-time data obtained through customized hardware with dedicated server. In this study, we have proposed a customized hand-held device that enables to deliver recommendations to the farmer on the basis of real-time data obtained through IoT hardware and ML. A three-layer structure is proposed in the study for realizing custom hardware with 2.4 GHz ZigBee and IoT sensors for the data acquisition, communication, and recommendation. As a part of real-time implementation, the calibration of the sensors is processed to form a real-time dataset with precision. The study evaluated four ML models and concluded that XGBoost has shown a better accuracy on the proposed dataset. The XGBoost recommended the crop based on selected parameters. The developed hand-held device can be customized with advance features with crop recommendations.