Edge Computing and Internet of Things Technology for Enhancing Crop Yield

This study aims to predict the yield of crops based on the type of crop, area, and climatic conditions such as humidity, temperature, and pH values. Sensory data is collected from the land using the device integrated with sensors and uploaded to the remote server. The sensory data will be fetched from the server to the integrated development environment (IDE) for predicting the yield of the crop using advanced machine learning algorithms. The effort towards making the model employs Raspberry Pi, which is connected to an analog-to-digital converter MPC3008 that facilitates the connection of soil moisture and pH sensor, and temperature sensor can be connected directly to the Raspberry Pi. The data fetched from these sensors are uploaded to a public application program interface (API). In this study, flask server is used to meet API needs. The same data is then forwarded to an IDE where it is used along with existing data to make predictions regarding the yield of the crop in a particular region. Python is used as a programming language and Spyder as IDE.