Soil Monitoring and Crop Recommendation System via IoT and Machine Learning

Traditional practices in agriculture lack the proper guidance in regard to crop production and yields, making the processes inefficient with Poor use of resources. To address the above challenges, we proposed an IoT and machine learning-based system targeting on the improvement of precision farming. The technology incorporated into our system is a microcontroller, numerous soil sensors and a GSM for transmitting the data. The main focus of our study is accurate crop recommendations that depend on which crops are suitable for the farmer and his environment. This is done via, evaluating some of the key factors of the environment in terms of Nitrogen, Phosphorous, and potassium Content in the soil, its Ph Level, the temperature, humidity and TDS. The way it works is that an image or a video of the soil is taken while, at the same time, it monitors the real-time data of the soil parameters using the sensors. Then it sends this data to a GSM module with IoT technology which connects to a model website on the cloud. The information is then processed by enhanced machine learning formulas in order to determine the fertility level of the soils and the type of crops to be grown on the land. The processed data and recommendations are sent back to the device with enabled user interface to see the analyzed results on a display. Here our use of IoT with machine learning enables the system give accurate and recommendable crop data, making the most of the resources and maximizing crop production. It bypasses the shortcomings of conventional ways of agricultural methods offering a scalable and efficient solution for modern agriculture.