Design of an IoT-based Machine-Learning model to improve harvest performance on a farm

Agriculture plays a crucial role in the economy of any nation, as the need for food increases daily alongside the global population. However, traditional farming techniques are unable to meet the demand generated by the current population. This is why innovative approaches have been develop to automate the agricultural process. The objective of this article was to design an integrated IoT and Machine Learning model for monitoring soil pH, weather conditions, irrigation control, pests, and diseases on a farm to enhance production by early identification of issues that could impact these parameters; thereby improving crop yield, processing, and marketing through Internet of Things (IoT)-based crop cultivation. This enhances market demand analysis, price prediction, and determining the optimal timing for planting and harvesting a crop