ReLeaf: A Mobile Application for Solanaceous Crop Disease Detection Using Lightweight YOLOv8n

Authors

  • Adha Rizwan Faculty of Electronics and Computer Technology and Engineering, Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia
  • A. R. Syafeeza Faculty of Electronics and Computer Technology and Engineering, Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia
  • Asar Khan Faculty of Electronics and Computer Technology and Engineering, Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia
  • Shahid Rahman Department of Computer Science, University of Buner, Pakistan

Keywords:

Solanaceous crops, plant disease, deep learning, mobile deployment, disease diagnostics

Abstract

Solanaceous crops such as tomatoes, eggplants, potatoes, and peppers are highly susceptible to visually similar diseases, making accurate manual diagnosis difficult and error-prone. This study presents ReLeaf, a mobile application that leverages a lightweight deep learning model (YOLOv8n) for on-device plant disease detection in solanaceous crops. The trained YOLOv8n model was converted into multiple deployment formats (PyTorch, ONNX, TFLite), benchmarked, and tested for inference accuracy, latency, and performance across platforms. Experimental evaluation on test data achieved a mean Average Precision (mAP) of 98.7%, with PyTorch providing the best balance of accuracy and efficiency. However, when applied to real-world samples, the deployed model achieved an overall detection accuracy of 70.2%, highlighting the gap between controlled validation and field performance. The final prototype, built with Flutter SDK, integrated the PyTorch model with a user-friendly interface to deliver real-time detection and treatment recommendations. Field validation confirmed the feasibility of mobile deployment with acceptable latency, while also identifying limitations in distinguishing visually similar leaf diseases. This study demonstrates a practical pathway for deploying AI-based crop disease diagnostics on smartphones, with potential for scaling to other crops and improved robustness through future dataset expansion and model optimization.

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Published

25-08-2026

How to Cite

Adha Rizwan, A. R. Syafeeza, Khan, A., & Rahman, S. (2026). ReLeaf: A Mobile Application for Solanaceous Crop Disease Detection Using Lightweight YOLOv8n. Applications of Modelling and Simulation, 10, 213–225. Retrieved from http://arqiipubl.com/ojs/index.php/AMS_Journal/article/view/1065

Issue

Vol. 10 (2026)

Section

Articles

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Copyright (c) 2026 Adha Rizwan, A. R. Syafeeza, Asar Khan, Shahid Rahman

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