CornCareX is an innovative computer vision application that leverages Convolutional Neural Network (CNN) technology to detect diseases in corn leaves. Developed by two students from the Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, this application is designed to help farmers monitor the health of corn crops quickly and accurately. The technology enables early disease identification, allowing farmers to take preventive or corrective actions before the disease spreads and reduces crop yields.
This innovation addresses challenges faced by corn farmers in Indonesia, where plant diseases often cause significant losses. With the increasing demand for corn as food and livestock feed, disrupted productivity can threaten national food security. Data shows a decrease in harvested corn acreage in 2023 compared to the previous year, emphasizing the need for this early detection solution.
The use of CornCareX aligns with SDG 2, “Zero Hunger,” by supporting productivity and farmer welfare. Through faster and more accurate disease detection, CornCareX has significant potential to stabilize crop yields and strengthen food security in Indonesia.
CornCareX was developed through a lengthy process, Dimaz Andhika Putra and Immanuella Rere Kusumawardhani as the creators, explored various CNN architectures to optimize detection results. After identifying the best approach, they designed it as a user-friendly application accessible to the public, particularly corn farmers. The application is expected to be widely adopted and become a reliable support tool for farmers.
The development of CornCareX contributes to SDG 9, “Industry, Innovation, and Infrastructure,” by providing a technological solution that enhances agricultural efficiency. Support from the government and the private sector is expected to expand the reach of this application to various regions, benefiting an even greater number of farmers.
CornCareX recently won first place in the “Smart Statistic (SMATIC) 5.0” essay competition organized by Universitas Negeri Jakarta, showcasing its potential as an effective solution to support Indonesia’s agricultural sector.
Keywords: Math Innovation, AI Application, Student Achievement
Author: Dimaz Andhika Putra
Editor: Endang Sulastri
Photo: Dimaz Andhika Putra