The implementation of image recognition in agriculture to detect symptoms of plant disease using deep learning Convolutional Neural Network (CNN) models are proven to be highly effective. The computational efficiency by using CNN, made possible to run the application on mobile device. To optimize the utilization of mobile device and choosing the most effective CNN model to run as detection system in mobile device with the highest accuracy and low resource consumption is proposed in this paper. In this study, PlantVillage dataset which extended to coffee leaf, were tested and compared using three CNN models, two models which specifically designed for mobile, MobileNet and Mobile Nasnet (MNasNet), and one model that recognized for its accuracy on personal computer (PC), InceptionV3. The experiment executed on both mobile and PC found a slightly degradation on accuracy when the application is running on mobile. InceptionV3 experienced the most persistence model compares to MNasNet and MobileNet. Yet, InceptionV3 had biggest latency time. The final result on mobile device recorded InceptionV3 achieved highest accuracy of 95.79%, MNasNet 94.87%, and MobileNet 92.83%, while for time latency MobileNet achieved the lowest with 394.70 ms, MNasnet 430.20 ms, and InceptionV3 2236.10 ms respectively. It is expected that the outcome of this study will be of great benefit to farmers as mobile image recognition would help them analyze the condition of their plants on site simply by taking a picture of the leaf and running the experiment on their mobile device
Batik is one of the Indonesian cultural heritages that has been recognized by the global community. Indonesian batik has a vast diversity in motifs that illustrate the philosophy of life, the ancestral heritage and also reflects the origin of batik itself. Because of the manybatik motifs, problems arise in determining the type of batik itself. Therefore, we need a classification method that can classify various batik motifs automatically based on the batik images. The technique of image classification that is used widely now is deep learning method. This technique has been proven of its capacity in identifying images in high accuracy. Architecture that is widely used for the image data analysis is Convolutional Neural Network (CNN) because this architecture is able to detect and recognize objects in an image. This workproposes to use the method of CNN and VGG architecture that have been modified to overcome the problems of classification of the batik motifs. Experiments of using 2.448 batik images from 5 classes of batik motifs showed that the proposed model has successfully achieved an accuracy of 96.30%.
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