Identification of Potato Plant Pests Using the Convolutional Neural Network VGG16 Method

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

Journal Medical Informatics Technology
Volume 2 No. 2, June 2024

Pests are one of the main challenges in potato cultivation that can significantly reduce crop yields. Therefore, quick and accurate pest identification is crucial for effective pest control. This research aims to develop a pest identification system for potato plants using the Convolutional Neural Network (CNN) method with the VGG16 architecture. The dataset used consists of images of pests commonly found on potato plants. After the labeling process, these images were used to train the CNN VGG16 model. The research results show that the CNN VGG16 method can identify types of pests with an accuracy rate of 73%. The results serve as a reference to help farmers and agricultural practitioners detect the presence of pests earlier and take the necessary actions to reduce crop losses.