Applicability of Machine Learning and Internet of Things-Based for Crop Selection

Agriculture plays a big role in ensuring global food security and sustainability. To optimize crop yields, it is essential to understand the soil and weather conditions of a farm. In this study, we developed a Crop Selection System that leverages the IoT and ML, specifically the Random Forest algorithm, to assist farmers of Rwanda in choosing the most suitable crops for their piece of land. The system makes recommendations based on measurements of nitrogen, phosphorus, potassium, pH, humidity, rainfall, and temperature before cultivation begins. Real-time field data was gathered using IoT sensors deployed in farm areas to collect soil and environmental information. Our device utilized soil sensors to measure nitrogen, soil pH, humidity, phosphorus, temperature, and potassium. The NodeMCU microcontroller preprocessed the data and uploaded it to a cloud database. The data collection took place in Gicumbi District, and the collected samples were analyzed using our crop selection model. The model was developed using the Random Forest algorithm to evaluate soil compatibility and rank the crops based on their probability of successful growth. By training the model with a dataset of crops’ ecological requirements, we achieved an accuracy of 96%. We then tested the model with newly collected data from the field. Over a period of seven days, the model's predictions indicated that potatoes had the highest growing probability at 56%, followed by beans at 43%, carrots at 33%, tomatoes at 33%, and rice at 17%. This data-driven approach significantly enhances farmers' decision-making by enabling them to make informed choices about crop selection. This technology can boost agricultural productivity while reducing unnecessary costs, such as excessive fertilizer use, by ensuring crops are cultivated based on the available ecological conditions. The system helps farmers assess nutrient levels and implement corrective measures to restore soil fertility. This method advances precision agriculture and contributes to the overall modernization of farming practices.