Plantect: Real-time IoT and Machine Learning for Smart Crop Planning

Real-time IoT and Machine Learning for Smart Crop Planning presents an innovative approach to optimizing crop selection by leveraging real-time sensor data and machine learning classification. The system integrates IoT sensors to monitor crucial soil parameters such as temperature and humidity and utilizes Random Forest classification, an ensemble learning method which is a type of machine learning classification algorithm that accurately predicts the crops that are most suited to the soil parameters that are taken in real-time enabling farmers to make informed, data-driven decisions. This real-time approach ensures that recommendations adapt to changing environmental conditions, such as soil quality and weather fluctuations, ultimately improving agricultural productivity. Experimental results show that using a Random Forest Classifier, the system achieves an impressive 96.445% accuracy in predicting suitable crops. This highlights its effectiveness in enhancing crop planning accuracy, optimizing resource use, and maximizing yields.