Implementation of the Convolutional Neural Network (CNN) Algorithm for Pest Detection in Green Mustard Plants

Gilang Wiwaha Soekarno (Universitas Teknologi Yogyakarta, Indonesia)
Agus Suhendar (Universitas Teknologi Yogyakarta, Indonesia)

G-Tech : Jurnal Teknologi Terapan
Vol 9 No 1 (2025): G-Tech, Vol. 9 No. 1 January 2025

Green mustard plants are of significant economic importance, making effective pest management essential. This study employed the Convolutional Neural Network (CNN) algorithm to detect pests on green mustard leaf images. The dataset, comprising 96 test images, was divided into two categories: pest-infested and healthy leaves. Using the NasNet Mobile architecture, the model was trained over 10 epochs with the Adam optimizer, achieving a training accuracy of 94.99% and a validation accuracy of 98.00%. Results indicate that CNN combined with NasNet Mobile effectively identifies pests, providing a robust and practical solution to enhance agricultural productivity and mitigate crop losses caused by pests. This study demonstrates the potential of leveraging deep learning for agricultural advancements, particularly in addressing pest-related challenges efficiently.