Abstract
Indonesia is one of the countries with the population majority of farming. The agricultural sector in Indonesia is supported by fertile land and a tropical climate. Rice is one of the agricultural sectors in Indonesia. Rice production in Indonesia has decreased every year. Thus, rice production factors are very significant. Rice disease is one of the factors causing the decline in rice production in Indonesia. Technological developments have made it easier to recognize the types of rice plant diseases. Machine learning is one of the technologies used to identify types of rice diseases. The classification system of rice plant disease used the Convolutional Neural Network method. Convolutional Neural Network (CNN) is a machine learning method used in object recognition. This method applies to the VGG19 architecture, which has features to improve results. The image used as training and test data consists of 105 images, divided into training and test images. Parameter testing using epoch variations and data augmentation. The research results obtained a test accuracy of 95.24%.
Highlights
Indonesia is known as the third-largest rice producer and consumer in the world [1]
Rice disease is one of the factors causing the decline in rice production in Indonesia
Convolutional Neural Network (CNN) is a machine learning method used in object recognition
Summary
Data from the Central Bureau of Statistics show that around 35.7 million Indonesians in 2018 are farmers, and some of them live below poverty. Activities that can increase rice productivity will affect millions of rice farmers in Indonesia. It estimates that farmers lose 37% of their rice production annually due to rice pests and diseases [2]. Knowledge of pests and diseases of rice plants is very significant in increasing farmers’ income. It is necessary to develop a system to recognize and classify rice plant diseases, and it can help Indonesian rice farmers. Recognition and classification of rice plant diseases require an accurate system to produce classification data. Types of rice diseases can be identified in several ways, one of which is leaf characteristics
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