The problem CropSense solves

Farmers need a solution that will help them to identify potential diseases at an early stage of their crop growth , so that they can take preventive measures and increase their productivity and earning. In this project, we propose the practice of smart agriculture using automation. Timely prediction for most probable disease in the crop can be made using data analytics and AI. By implementing a model where the inputs would be provided by sensors and intelligent decision making based applications can be deployed using machine learning algorithms, a well designed “Smart Plant Disease Surveillance System” can be made on accurate real time field data. We intend to develop a smart system in which various parameters such as temperature, humidity, pressure, water level, microbes can be observed and timely action can be taken to prevent any hazards such as diseases in the crops etc.

Challenges we ran into

Training ML model as it requires lot of CPU and GPU power.
Creating an Flask API for ML model.