Agrowise

Abstract

Agriculture faces several challenges, such as unpredictable weather factors and diseases that affect crops. However, the implementation of technological innovations, such as Machine Learning, has significantly contributed to increasing crop yields and quality. Thus, machine learning, a branch of Artificial Intelligence, has been applied in several areas, including precision agriculture, which uses large volumes of data to identify complex patterns in the soil, revolutionizing crop cycles. Furthermore, integrating it into agriculture can bring better results and lower risks to production and the farmer. The work proposes the development of a mobile application for crop recommendation, using a machine learning model trained with data on temperature, humidity, pH and soil information. The application was developed using the Flutter framework and an Application Programming Interface (API) in Django and Django Rest Framework to power the application