Crop yield prediction is crucial for ensuring food security by enabling farmers to optimize resource use, manage risks, and plan for market demands, ultimately leading to increased agricultural productivity and sustainability..The IoT-based crop yield prediction system integrates advanced sensing technologies, communication protocols, machine learning algorithms, and real-time monitoring to optimize crop production. Nutrient, soil moisture, and environmental sensors deployed in fields measure essential soil nutrients (NPK), soil moisture, and climatic conditions. Data is transmitted to a centralized server via LoRaWAN technology, ensuring efficient, low-power communication over large distances. The data reaches the server, after which it undergoes preprocessing, such as cleaning and ensuring integrity. A Random Forest model takes the purified data and predicts crop yield based on inputs in historical and real time. The patterns revealed are complex and can make accurate estimated yield predictions. Actionable insights, like optimal irrigation and fertilization schedules, are generated by the system and communicated by alerts and a dashboard in real time based on mobile devices to the farmers. It also gives key measures and predictions to inform decision-making. It goes on running continuously and collecting, processing, and analyzing data at specific intervals for an ongoing optimization. With high accuracy in yield predictions matching the actual yields and confidence intervals close to those yields, the system had achieved this result. Through correlation analysis, critical factors in terms of nutrient levels and soil moisture were found to influence yields greatly, with the application of these factors helping in efficient resource management and sustainable agricultural practices.
Alsalami et al. (Fri,) studied this question.