Cotton Plant Disease Prediction using Deep Learning

Abstract

Cotton is one of the economical important crops in the Ethiopia, but there are so many various types of restrictions in the leaves areas. Most often these are restricted to identified most of the diseases in the leaves or the pests that are difficult to see the diseases with the naked eyes. This study is focusing on the developing on the model to improve the detection of cotton leaf diseases and then pests are using Convolutional Neural Network (CNN) deep learning technology. To do this further, the researchers are used the common cotton leaf diseases and the pests, this type of bacteria is rotten and bollworm. Similarly we can say approximate of the samples are the 2400 (600 images in each class) they were used in this further study for training purposes. In this paper, it is tried with developing a model and tried implementing it by using python version. The accuracy for the classification of cotton leaf illnesses and the pests will be studied by using the model. An accuracy of 96.4% for the classification of cotton leaf illnesses and the pests using this model is obtained.