DRYLAND AGRICULTURE AND YIELD GAP ANALYSIS BY MACHINE LEARNING ALGORITHMS USING IOT SENSORS

DRYLAND AGRICULTURE AND YIELD GAP ANALYSIS BY MACHINE LEARNING ALGORITHMS USING IOT SENSORS Abstract In arid zones, agriculture is a key component of every country's economy. Another major problem that agriculture in the arid zone confronts is a lack of water, which keeps it from producing the highest potential output. Increasing yield is possible with the Internet of Things (IoT). Agricultural productivity is affected by many external influences, including biotic and abiotic challenges, which are all exacerbated by climate change, creating long-term sustainability problems. To have success with these obstacles, we will have to gather many heterogeneous data sets as well as advanced analytics to bring them together and discover the fundamental productivity problems on different sizes. We ran an ML (Machine Learning) algorithm on the data to see whether the problem persisted, and then we showed off a sophisticated IoT-powered micromanagement device that was capable of monitoring certain environmental factors constantly in various locations. We utilised an ELM (Extremely Learning Machine) to determine the moisture content of the soil surface. Following the data's pre-processing and feature extraction, it is then put into an ELM-based regression model that predicts the soil surface's humidity. This device regulates the amount of water that is in the soil as well as the irrigation system, making it possible for agriculture to be sustainable. The moisture sensors in the packaging design transmit an alert when the amount of moisture drops below the set thresholds. To avoid delays and unnecessary machine operations, users must take manual action to restore the moisture level.