SMU Data Science Review
Abstract
Plant diseases pose a significant threat to food security, particularly in developing countries where farmers often lack the resources and infrastructure for early detection. In nations like Mexico and the Dominican Republic, the spread of harmful plant diseases impacts key agricultural commodities, such as habanero peppers, leading to substantial yield losses. This study presents a computer vision system based on Convolutional Neural Networks (CNNs) and an object detection model (YOLO) to help farmers detect pepper diseases efficiently. The system uses a two-stage approach: YOLOv11n first detects pepper leaves in images, then a lightweight MobileNetV3Small model classifies whether the detected leaves show signs of disease. The MobileNet model has been fine-tuned specifically for pepper disease classification and optimized for deployment on smartphones, offering a cost-effective and accessible solution. The mobile application operates in real-time and offline, maintaining functionality even in areas without network connectivity. Farmers can diagnose diseases by capturing images of affected leaves, enabling early intervention and improved crop management. By leveraging deep learning (DL) for on-device disease detection, this approach can enhance agricultural productivity, reduce economic losses, and contribute to food security. This work demonstrates the potential of mobile DL solutions to transform small-scale farming through accessible, and scalable disease diagnosis technology.
Recommended Citation
Estevez, Carlos Jose; Dang, Mai; and Bass, Ryan
(2025)
"Mobile Computer Vision Application for Agricultural Disease Detection of Pepper Diseases using Two-Stage Deep Learning System,"
SMU Data Science Review: Vol. 9:
No.
2, Article 7.
Available at:
https://scholar.smu.edu/datasciencereview/vol9/iss2/7
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
