Vol.14, No.2, May 2025.                                                                                                                                                                          ISSN: 2217-8309

                                                                                                                                                                                                                        eISSN: 2217-8333

TEM Journal

TECHNOLOGY, EDUCATION, MANAGEMENT, INFORMATICS

Association for Information Communication Technology Education and Science

MaTangDetect: Identification of Potato Pests Using Freezing Layers Ensemble-Based Deep Learning

Sri Hadianti, Dwiza Riana, Daning Nur Sulistyowati, Faruq Aziz, Ridwan Saputra, Kurniawantoro Kurniawantoro

© 2025 Sri Hadianti, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)

Citation Information: TEM Journal. Volume 14, Issue 2, Pages 1107-1116, ISSN 2217-8309, DOI: 10.18421/TEM142-12, May 2025.

Received: 01 August 2024.

Revised:  20 December 2024.
Accepted: 26 February 2025.
Published: 27 May 2025.

Abstract:

One of the challenges in the agricultural world is pest attacks that cause crop failure. Potatoes are one of the crops that are often attacked by pests. Early identification of potato pests is crucial because it significantly helps farmers implement appropriate and effective control measures. This not only increases crop yields but also reduces economic losses caused by pest attacks. This study introduces MaTangDetect, a pest detection system that uses an ensemble-based deep learning model with a freezing layer technique. The proposed system uses a freezing layer on the ensemble of DenseNet201, EfficientNetB5, and InceptionV3 models, to improve classification accuracy. By applying the freezing layer technique, the system utilizes previously learned features to the maximum, thereby accelerating the training process and improving overall performance. MaTangDetect was evaluated using various potato pest images and had an accuracy rate of 93%. These results demonstrate the effectiveness of deep learning based on the freezing layer ensemble-based model in identifying potato pest species. In addition, this system offers great potential for application in the field, providing practical and innovative solutions for farmers in pest management in potato cultivation, and contributing to increased yields and reduced economic losses.

Keywords – Freezing layer, deep learning, potato pests, identification, ensemble model-based

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