Analysis of Image Recognition Algorithms for Detecting Common Calamansi [Citrofortunella microcarpa (Bunge) Wijnands] Diseases in the Philippines - Goloy, Rean T.; Khan, Concepcion L. (International Journal of Computing Sciences Research; Vol 9 (2025): Volume 9, 2025)
Purpose – Plant diseases are a major challenge for agriculture in the Philippines, particularly for calamansi [ Citrofortunella microcarpa (Bunge) Wijnands], a key citrus crop. This study addresses this issue by employing image processing and machine learning techniques to enhance the detection of calamansi diseases.   Method – The research analyzed three image recognition algorithms—YOLOv5, Faster R-CNN, and MobileNetSSDv2—using a dataset of 2,990 images of calamansi leaves and fruits collected from farms in Victoria, Oriental Mindoro. The images were processed, and the models were evaluated based on precision, recall, and mean average precision (mAP). Results – YOLOv5 outperformed the other models with a precision of 96%, recall of 96.2%, and mAP of 98.1%. This model was deployed in a mobile application, achieving a field-testing accuracy of 77.5%. The findings highlight the potential of using machine learning for real-time detection of plant diseases. Conclusion —The study demonstrates the feasibility of integrating advanced algorithms into mobile technology to assist Calamansi farmers in monitoring crop health effectively. Practical Implications – Adopting mobile-based disease detection systems can significantly improve crop management, productivity, and sustainability. However, the research is limited to specific diseases and data from a single region, underscoring the need for broader datasets and extended testing.