Classification of Cotton Leaf Diseases Using AlexNet and Machine Learning Models

  • Borugadda P
  • Lakshmi R
  • Govindu S
N/ACitations
Citations of this article
25Readers
Mendeley users who have this article in their library.

Abstract

Computer vision has been demonstrated as state-of-the-art technology in precision agriculture in recent years. In this paper, an Alex net model was implemented to identify and classify cotton leaf diseases. Cotton Dataset consists of 2275 images, in which 1952 images were used for training and 324 images were used for validation. Five convolutional layers of the AlexNet deep learning technique is applied for features extraction from raw data. They were remaining three fully connected layers of AlexNet and machine learning classification algorithms such as Ada Boost Classifier (ABC), Decision Tree Classifier (DTC), Gradient Boosting Classifier (GBC). K Nearest Neighbor (KNN), Logistic Regression (LR), Random Forest Classifier (RFC), and Support Vector Classifier (SVC) are used for classification. Three fully connected layers of Alex Net provided the best performance model with a 94.92% F1_score at the training time of about 51min.

Cite

CITATION STYLE

APA

Borugadda, P., Lakshmi, R., & Govindu, S. (2021). Classification of Cotton Leaf Diseases Using AlexNet and Machine Learning Models. Current Journal of Applied Science and Technology, 29–37. https://doi.org/10.9734/cjast/2021/v40i3831588

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free