We always read, hear several incidents related to farmers taking their life due to crop loss and many other factors. These situations arise due to not having knowledge about crop selection process. For easy prediction and selection of the type of crop, we developed the “Crop Recommendation” model. The parameters considered in this model are environmental temperature, humidity, soil pH, geographical location’s average rainfall, and soil type and soil moisture. Their respective sensors take these parameters via Raspberry Pi. That is, temperature, humidity, and soil moisture are obtained through their respective sensors. Where soil type, geographical location’s average rainfall, and soil pH level are given as manual input. We use machine learning concepts in order to develop the model/tool required. We take a standardized dataset, by which we train the model using machine learning algorithms. The Raspberry Pi obtains respective sensor readings and sends the readings to the trained model. This model predicts the type of crop suitable for that particular user’s environmental and soil conditions. The recommended result is sent to the user via SMS. “Twilio” services are used to send results via SMS. Thus, the user would know which crop would suit his soil and avoid crop loss.