Classification using Convolutional Neural Network Algorithms for Pest Detection in Water Spinach - Hikmianto, Riki; Utami, Ema (Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer, 2025)
This study examines the application of Convolutional Neural Network (CNN) algorithms in detecting pests on water spinach plants using two CNN architectures: MobileNetV2 and VGG16. Monitoring diseased leaves during the plant growth phase is a critical step, and AI-based solutions are essential to enhance the accuracy of automated pest detection. In this research, a dataset of water spinach leaf images, both pest-infected and healthy, was collected to train the two CNN architectures. MobileNetV2 was selected for its ability to deliver high performance with low computational complexity, while VGG16 was used as a benchmark due to its deeper architecture and widespread use in various image recognition tasks. The testing results indicate that MobileNetV2 achieved a detection accuracy of 84%, while VGG16 yielded an accuracy of 83%. Thus, MobileNetV2 is considered superior for this pest detection application as it provides a balance between high accuracy and optimal computational efficiency. The study concludes that MobileNetV2 is a more suitable architecture for pest detection systems in water spinach plants, particularly for applications requiring high performance on resource-constrained devices.