Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
Digital Object Identifier (DOI) : 10.14569/IJACSA.2018.090109
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 9 Issue 1, 2018.
Abstract: This study examines the use of deep convolutional neural network in the classification of rice plants according to health status based on images of its leaves. A three-class classifier was implemented representing normal, unhealthy, and snail-infested plants via transfer learning from an AlexNet deep network. The network achieved an accuracy of 91.23%, using stochastic gradient descent with mini batch size of thirty (30) and initial learning rate of 0.0001. Six hundred (600) images of rice plants representing the classes were used in the training. The training and testing dataset-images were captured from rice fields around the district and validated by technicians in the field of agriculture.
Ronnel R. Atole and Daechul Park, “A Multiclass Deep Convolutional Neural Network Classifier for Detection of Common Rice Plant Anomalies” International Journal of Advanced Computer Science and Applications(IJACSA), 9(1), 2018. http://dx.doi.org/10.14569/IJACSA.2018.090109