FCNN-LDA: A faster convolution neural network model for leaf disease identification on apple's leaf dataset

Abstract

Fruits are common items bought by every household. They are delicious to eat and rich in nourishment. However they may also adversely affect health if the fruits are from a diseased tree/plant. Moreover, Farmers may also loose lot of amount of profit if their plants get affected by some disease. In this article, the main objective/goal is to develop a convolution neural network based approach to identify the disease in apple fruit. The data for experiment has been taken from PlantVillage. In the proposed work, a convolution neural network model has been developed to identify the disease in apple and it consists of three convolution layer, three max pooling layer followed by two densely connected layers. This model was formed after testing with varying number of convolution layers from 2 to 6 and found that 3 layer was giving best accuracy. For the result comparison purpose, the traditional machine learning algorithms are also executed on the same dataset. Along with traditional machine learning approaches, the famous pre-trained CNN models i.e. VGG16 and InceptionV3 are also executed. The experiments results shows the efficacy of proposed algorithm over pre-trained models and traditional machine learning approach in terms of accuracy, computational time, specificity, F1 score and AUC-ROC curve. The proposed model achieves the state of the art accuracy of 99%. Moreover, the proposed model requires only 20% of the space as compared to pre-trained model with inference time less than 1 second as pre-trained models require minimum 30 second. © 2019 IEEE.