Smart System for Crop and Diseases Prediction using Random Forest and Resnet Architecture

Abstract

The agriculture plays an important role in the growth of every country's economy. In India, Agriculture is one of the most important occupations and a large amount of food is produced by the farmers. The climate and other environmental changes, uneven rainfall has become a major problem in the agriculture field. Machine learning and Deep learning approaches now-a-days play a major role in giving better solution for this problem. Crop type prediction involves predicting the type of crop before cultivation based on the historically available data such as weather, climatic conditions, soil and previous crop yield. Our work focuses on giving a solution to the farmers to decide on the suitable crop to cultivate. The publicly available crop dataset is used for training and testing our model. Crop Prediction is done using Random Forest (RF) machine learning algorithm. The proposed work also recommends the fertilizer to use for the increasing the crop production by using the soil type and the type of crop. The system predicts the plant diseases using ResNet architecture to avoid the spread of crop diseases.