Manuscript received December 2, 2022; revised April 13, 2023; accepted May 22, 2023.

Abstract—Roselle is a fiber-producing plant that has broad benefits for health food, so many farmers are interested in starting to cultivate it. This study aims to design a rosella plant pest detection system to reduce the risk of crop failure or reduced yields of rosella calyx. The design of a system for detecting and classifying rosella pests uses the threshold method as a digital image processing method connected via the internet with information media applications and template matching to detect and classify pests on rosella plants. Detection of pests on rosella plants has been successfully built using a detection system using thresholding and template matching methods. Datasets of rosella plant pests that are not yet widely available encourage the detection of rosella plant pests with datasets from rosella plant objects and limited data testing. Testing with 75% accuracy, the detection process is affected by light and camera quality.

Keywords—classification, threshold method, image processing, template matching, embedded system

Cite: Ade Bastian, Adie Iman Nurzaman, Tri Ferga Prasetyo, and Sri Fatimah, "Roselle Pest Detection and Classification Using Threshold and Template Matching," Journal of Image and Graphics, Vol. 11, No. 4, pp. 330-342, December 2023.

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