Raita Mitra for Crop and Pesticide Recommendation along with Disease Prediction using ML

Abstract

Abstract: Agriculture seems to be a key part of both a country's food security and its economic growth. Choosing which crops to grow is one of the most important parts of planning agriculture. The suggested system helps farmers choose crops that will do well in their area. For agriculture to grow, it's important to be able to make accurate predictions about which crops to grow. We've given you a machine-learning method called "Random Forests" that can predict how crop choices will change based on the current climate and biophysical changes. We have gathered a lot of information about crop selection from many different places. These numbers are used both to train and test the model. From different results, it's clear that RF is a good machinelearning algorithm for predicting crops in their current state and has a very high level of accuracy when analysing data. RF algorithm also helps to find the right fertiliser by taking into account NPK values, soil moisture, and the name of the crop. Since a long time ago, plant leaf disease has been one of the biggest threats to food security because it lowers crop yield and lowers the quality of the crop. Accurately diagnosing diseases has been a big problem, but recent advances in computer vision made possible by deep learning have made it possible to use a camera to help diagnose diseases in plat leaf. It talks about the new way to find diseases and use deep learning and convolutional neural networks. neutral networks (CNNs) has done a great job of putting plant leaf diseases into groups. Using an image dataset of plant diseases that was available to the public, a CNN was used to apply and train a number of neuron-wise and layer-wise visualisation methods. So, it was found that neutral networks can pick up on the colours and textures of lesions that are unique to each disease. This can be compared to how humans make decisions.