SmartPaddy: A Cross-Platform Mobile Application for Real-Time Paddy Crop Health Monitoring Using Image Processing and Cloud Integration

Abstract

SmartPaddy is a cross-platform mobile application developed using Flutter to support real-time paddy crop health monitoring through image processing and cloud-based integration. This study presents the system architecture, machine learning workflow, and a structured usability evaluation involving 10 farmers with 1–15 years of farming experience. The evaluation included task-based trials and a 20-item Likert-scale questionnaire measuring usefulness, ease of use, learning outcomes, and overall satisfaction. Results showed that 80% of users strongly agreed the app helped them detect diseases, 70% understood the recommended actions, and 90% could apply the guidance to real crops. The offline disease-treatment module was positively rated by 70% of respondents. Key constraints identified include occasional misclassification under low-light images, limited disease categories, and the need for clearer result presentation. SmartPaddy contributes to mobile-based agricultural decision support by integrating real-time image analysis with practical offline information for field users. Future improvements aim to expand the disease database, improve image capture guidance, and refine the result explanation interface to enhance diagnostic clarity.