Design and development of plant leaves disease detection model using Deep Convolutional neural network

Kanchana devi k (~kanchana)


Description:

A plant leaf disease detection model using a deep convolutional neural network was proposed in this research. The deep convolutional neural network model consists of eleven layers includes convolutional, pooling and dense. The model was trained using a dataset with 38 different classes and 49,598 images. The data augmentation and hyperparameters optimization techniques have enhanced the performance of the model. After the training process, the proposed model achieves 98.26% of classification accuracy in testing data. This accuracy of the proposed work is greater than the accuracy of traditional machine learning approaches. The proposed model is also deployed in edge devices using tensor flow lite.

Prerequisites:

  • Basics of python language
  • Basic Understanding of Deep Learning and TensorFlow

Content URLs:

Poster and Data: Google Drive

Speaker Info:

KANCHANADEVI K

I am currently working as an assistant professor in the department of computer science and engineering at Imayam College of Engineering. I worked as a junior software engineer at SEC Information India Pvt Ltd from May 2016 to Mar 2019. Totaly I have 3 Years and 2 Month work experience in Industry.

I also published a book named as Programming in Java, Charulatha Publications, Dec 2017. (ISBN: 978-9386532541). Also, I Completed and Received a Passing grade in DEV276x: Learn to program in Java a course of study offered by MICROSOFT CORPORATION through edX.

Speaker Links:

Github

Id: 1623
Section: Data Science, Machine Learning and AI
Type: Poster
Target Audience: Advanced
Last Updated: