Penggunaan Aplikasi Berbasis Visi Komputer dalam Diagnosis dan Monitoring Tingkat Keparahan Penyakit Busuk Buah Kakao (Phytophthora palmivora) Pada Dua Jenis Klon yang Berbeda di Kabupaten Gowa = Use of Computer Vision-Based Applications in Diagnosis and Monitoring the Severity of Cocoa Pod Rot Disease (Phytophthora palmivora) in Two Different Types of Clones in Gowa Regency

Cocoa plants are plantation crops that contribute to economic development in Indonesia. Cocoa production in Indonesia has increased significantly but the quality produced has shortcomings, one of the causes is pest attacks. Currently, technological developments are increasingly rapid and are able to give birth to new innovations that are useful in human life, one of which is the use of application-based computer vision technology in the field of phytopathology. This research aims to calculate the level of accuracy of the Agrio and Google Lens applications in diagnosing symptoms of cocoa pod rot disease and calculating the severity of the disease in the Sulawesi 1 and MCC 02 clonses. The research stages are location survey, determining observation samples, diagnosis application testing (Agrio and Google Lens), monitoring disease severity use Scan It To Office aplications, and data analysis. The research results show that the accuracy level of the Agrio application in diagnosing pod rot symptoms is 90% and the Google Lens application shows 80% accuracy. Both applications can be recommended and can be trusted in diagnosing symptoms of cocoa pod rot. The highest level of disease severity in the Sulawesi 1 clone was 59.10%, while the disease severity level in the MCC 02 clone was 43%. The high level of disease severity requires fast and appropriate control measures. The highest level of disease severity in the Sulawesi 1 clone was 59.10%, while the disease severity level in the MCC 02 clone was 43%. The high level of disease severity requires fast and appropriate control measures. The highest level of disease severity in the Sulawesi 1 clone was 59.10%, while the disease severity level in the MCC 02 clone was 43%. The high level of disease severity requires fast and appropriate control measures.

Keywords : Agrio, Google Lens, MCC 02, Scan It To Office, Sulawesi 1