Ai Leaf and Crops Sentry

Abstract

Artificial Intelligence has been witnessing a monumental growth in bridging the gap between the capa- bilities of humans and machines. A Convolutional Neural Network (ConvNet/CNN) is a Deep Learning algorithm that can take in an input image, assign importance (learnable weights and biases) to various aspects/objects in the image, and be able to differentiate one from the other. The pre- processing required in a ConvNet is much lower as com- pared to other classification algorithms. In our base paper image segregation and identification is done using matrix factorization and machine learning. But we create a trained model using a Convolutional Neural Network where the user-given image is detected by converting and comparing the images which are in matrix form. In our application, the user image is compared with our trained model to identify the specifics of images and sent to the client where machine learning helps to find the right fertilizers for the crop based on disease recognized in the image given by the user in the first place. Our proposed method is way efficient and effective compared to traditional image recognization. And our application use weather report for any given location and soil report - suggestion for the cultivation of specific crop for best productivity.