Combination of Active Contour and CNN-based Segmentation Methods to Improve Accuracy in Detecting Rice Diseases

Wahyu Saptha Negoro; Ratih  Adinda Destari; Asbon  Hendra Azhar; Achmad  Syahrian

doi:10.35134/komtekinfo.v12i4.671

Authors

Wahyu Saptha Negoro Universitas Potensi Utama
Ratih Adinda Destari Universitas Potensi Utama
Asbon Hendra Azhar Universitas Potensi Utama
Achmad Syahrian Universitas Potensi Utama

DOI:

https://doi.org/10.35134/komtekinfo.v12i4.671

Keywords:

Segmentation, Active Contour, CNN, Detection, Rice Disease

Abstract

Rice diseases are one of the main factors causing decreased productivity and threatening national food security. The main problem in controlling rice diseases is the delay and inaccuracy of symptom identification in the field. This study aims to develop an artificial intelligence-based rice disease detection system through a combination of Active Contour and Convolutional Neural Network (CNN) methods. The research object is rice leaf images taken from rice fields in Pulau Sejuk Village, Batubara Medan, with a dataset of 600 images consisting of healthy leaves and 3 types of rice diseases. The Active Contour method is used in the segmentation stage to extract leaf areas precisely, while CNN is applied for the disease classification process. The results show that this combination of methods can significantly improve the accuracy of rice disease detection. The developed system is expected to assist farmers and stakeholders in the early detection of rice diseases, thereby supporting food innovation and increasing sustainable agricultural productivity.

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