Plant Diseases Recognition Using Machine Learning

The way a doctor can predict what kind of diseases a patient is suffering from, similarly, the fastest stratagem of predicting plant diseases is to analyze leaf's physiognomy changes and compare them with their actual color, shape, structure, etc. Plant disease recognition on the basis of leaf's physiognomy changes is the fundamental purpose of our project. We have used Convolutional Neural Network as a training method. CNN works via 3 dimensions of layers where neurons of every layer aren't fully connected to the next layer rather only a small portion is connected and the output will be decreased to a single dimension. For this, even with big datasets CNN works faster than any other networks. That's why we have used it for achieving a satisfying accuracy outcome. The program will exert plant images as input and detaching them to predict plant diseases. So it will help to identify and differentiate various types of plant diseases like aster yellows, bacterial wilt, scab, etc. quite easily & correctly.

I. Introduction

Nowadays, Plant diseases are a leading threat to food production and food diversity caused by different environmental conditions and pathogens. Worldwide, plant diseases have become an incubus because of the reduction in yield grain and fruits. Plant disease is not only reducing the quantity of production of fruits and grains but also causes of decreasing the quality level. Plant diseases influences our ecosystem negatively by damaging crops, soil, etc.