Authors - Vindiya Wickramathunga, Sapna Kumarapathirage Abstract - Accurate cinnamon plantation disease detection is crucial for planters, especially those with limited expertise. Traditional manual inspection methods are time-consuming and subjective, risking delays or incorrect diagnoses. This paper proposes an automated detection system using a Convolutional Neural Network (CNN) integrated with Explainable Artificial Intelligence (XAI) techniques. A publicly available dataset comprising three disease classes and one healthy class was used for training. The CNN achieved 75–80% accuracy with a 0.78 F1-score. Visual explanations via HiRes-CAM provided higher resolution compared to GradCAM++, enhancing user trust. The system demonstrated fast inference times (1–2 seconds), supporting real-time field deployment.
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