Intelligent IoT-based Combined Crop-type and Disease Prediction System with Different Machine Learning & Deep Learning Techniques

Agriculture contributes significantly to economic and social prosperity around the world. The world's population is increasing rapidly, so the food demand is also growing. Farmers must use harmful pesticides more frequently and intensively on the soil to meet the increased demand since more than previous methods are required. Also, this strategy can occasionally result in agricultural disaster, resulting in a considerable waste of crops, money, and effort. New automated edge intelligence technology sparks a farming revolution to meet that need. An intelligent IoT-based application is a comprehensive solution that predicts crop yields and detects illness in crops by utilizing machine learning and deep learning techniques with the most popular fashionable algorithms. To create a system, we use edge intelligence. This research focuses on integrating Raspberry Pi, sensor, and Azure IoT Hub. We successfully prevented crop waste by training 2000+ data points with a 94.18% accuracy rate. In contrast, this system is automated and software-based since it is lightweight, the costs are low, and the study reacts significantly better than other existing studies.