Krishinova: Precision Crop, Fertilizers and Pesticides Recommendation for Smart Farming

KrishiNova is an intelligent farming assistant that helps farmers determine ideal crops to grow, suitable fertilizers to use and the type of disease afflicting their plants. It uses data sets, machine learning models, weather data and image processing. The crop recommendation takes the previous crop, previous crop yield and location and uses accessible weather data to estimate soil nutrients. The features are then passed to a Random Forest model. The fertilizer recommendation takes crop and field data. The disease detection uses a convolutional neural network to classify leaf images from the user into 53 different plant health categories. The application has been created using React, Python (Flask) and the MySQL database with SQLAlchemy. The disease detection model had been tested on 13007 validation images. The accuracy, macro F1 score and weighted F1 score were 91.21%, 85.85% and 91.28% respectively. The application also has user authentication, prediction history, feedback, notifications and English-Kannada functionality. The results show that it is possible to create an application which can help provide multiple pieces of advice for farming. However more validation would need to be done in the field for the NPK estimation and recommendations to be used as alternatives to a soil test or professional agronomist.