Designing an Application for Detecting Diseases of Rice Plants Using OOAD Method

Abstract

Rice, as a key element of Indonesia's food security, plays a crucial role in agricultural ecosystems. Despite its high economic value, rice plants are susceptible to various diseases that can reduce productivity and harvest quality. Farmer's limited knowledge about disease types, identification, and proper handling poses a serious challenge to sustainable agriculture. Previous studies highlight farmers' inadequate understanding of pests and diseases in rice plants, leading to a high dependency on pesticides. Furthermore, lack of training data and a shallow understanding of rice diseases present significant challenges in disease management efforts. This research aims to develop an Android-based Smart Farm application. This application utilizes image processing and artificial intelligence technologies to assist farmers in identifying leaf diseases in rice plants. Requirements analysis involves literature review and field observations around Bandung Regency. It can be concluded; Smart Farm application has been successfully developed with three functional and two non-functional requirements. Validation testing indicates a 100% functionality rate and an 80% accuracy in disease detection. Nevertheless, further attention is required to enhance accuracy by providing more training data and improving image quality. The implications of this research extend to enhancing farmers' knowledge, reducing pesticide dependency, and supporting sustainable agriculture in the future.

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