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All Journal INOVTEK Polbeng - Seri Informatika
Erlin Sabri
Institut Bisnis dan Teknologi Pelita Indonesia
Computer Science & IT

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Soursop Leaf Disease Detection With CNNs:   From Training to Deployment Siti Hidayatullah Nuriadi; Erlin Sabri; Alyauma Hajjah; Ramalia Noratama Putri
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/sn8avr92

Abstract

Soursop (Annona muricata) is a valuable tropical fruit crop that is highly susceptible to leaf diseases caused by fungal, bacterial, and viral infections. These diseases can significantly impact crop yield and quality, posing challenges for farmers, especially when early detection is delayed. This study proposes an automated solution using Convolutional Neural Networks (CNNs) to detect soursop leaf diseases through image classification. A dataset of 400 labelled leaf images, including healthy and diseased leaves (Leaf Rust, Leaf Spot, and Sooty Mold), was collected and preprocessed for the dataset. Three CNN architectures—MobileNetV2, VGG19, and ResNet50—were evaluated based on accuracy, precision, recall, and F1-score. Among them, MobileNetV2 outperformed the others, achieving 73% accuracy, 72% precision, 65% recall, and 66% F1-score and demonstrated strong consistency across classes. The best-performing model was deployed using the Flask web framework, enabling users to upload soursop leaf images and receive instant disease classification along with suggested treatments and preventive measures. This study’s novelty lies in the end-to-end pipeline, from model training to deployment via Flask, providing a ready-to-use solution for farmers.
Co-Authors Alyauma Hajjah Ramalia Noratama Putri Siti Hidayatullah Nuriadi