Work

Implementation of AlexNet and Xception Architectures for Disease Detection in Orange Plants

Year: 2024

Type: article

Abstract: Oranges are one of Indonesia's primary horticultural commodities, with production increasing each year. However, pest and disease infestations often go undetected, leading to significant reductions in crop yields. This study implements Convolutional Neural Network (CNN) technology to identify diseases in orange plants using two architectures: AlexNet and Xception. The implementation results show that the Xception architecture achieved a high accuracy of 96% after 100 training epochs, indicating its effectiveness in disease detection tasks. This research highlights the potential of integrating CNN technology, particularly the Xception model, into web-based systems for disease detection in orange plants. Such systems can assist farmers in maintaining crop health, improving productivity, and ensuring harvest quality.

Source: Journal of Applied Informatics and Computing

DOI: 10.30871/jaic.v8i2.8700

Authors: Venus Al Fatah, Moh. Ali Romli

Institution: Universitas Teknologi Yogyakarta

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $35