Classification of Plants Leaf Diseases using Convolutional Neural Network

  • Reem Mohammed Jasim Al-Akkam Department of Computer Science, College of Science, Al-Nahrain University, Baghdad, Iraq
  • Mohammed Sahib Mahdi Altaei Department of Computer Science, College of Science, Al-Nahrain University, Baghdad, Iraq
Keywords: Image preprocessing, Deep Learning, Convolutional Neural Network, Detecting Leaf Diseases

Abstract

Agriculture is one of the most important professions in many countries, including Iraq, as the Iraqi financial system depends on agricultural production and great attention should be paid to concerns about agricultural production. Because plants are exposed to many diseases and monitoring plant diseases with the help of specialists in the agricultural region can be very expensive. There is a need for a system capable of automatically detecting diseases. The aim of the research proposed is to create a model that classifies and predicts leaf diseases in plants. This model is based on a convolution network, which is a kind of deep learning. The dataset used in this study called (Plant Village) was downloaded from the kaggle website. The dataset contains 34,934 RGB images, and the deep CNN model can efficiently classify 15 different classes of healthy and diseased plants using the leaf images. The model used techniques to augment data and dropout. The Softmax output layer was used with the categorical cross-entropy loss function to apply the CNN model proposed with the Adam optimization technique. The results obtained by the proposed model were 97.42% in the training phase and 96.18% in the testing phase.

 

Published
2021-06-27
How to Cite
Mohammed Jasim Al-Akkam, R., & Sahib Mahdi Altaei, M. (2021). Classification of Plants Leaf Diseases using Convolutional Neural Network. Al-Nahrain Journal of Science, 24(2), 64-71. Retrieved from https://anjs.edu.iq/index.php/anjs/article/view/2360
Section
Articles