AI-Enabled Decision Support System for Rice Disease Identification Under Operational Constraints
Abstract
Timely and consistent identification of crop diseases remains a persistent challenge in agricultural operations due to environmental variability, resource constraints, and the time-sensitive nature of intervention decisions. Although convolutional neural network (CNN)–based image classification systems have demonstrated high diagnostic accuracy in plant disease detection, their contribution to decision quality within decision support systems (DSS) has received comparatively limited analytical attention. This study adopts a secondary analytical research design to evaluate an AI-enabled rice disease identification system using validated performance data reported in prior empirical studies. No new experiments, model training, or data collection were conducted. Instead, reported classification accuracy and category-level performance are re-examined through a DSS lens, drawing on established theories of decision quality and information quality. Results from the source studies indicate high validation accuracy and robust performance under defined operating conditions. Interpreted from a decision support perspective, these findings suggest that reliable and consistent diagnostic outputs can reduce uncertainty and support timely intervention decisions, thereby enhancing decision quality, provided that decision-maker competence and system use conditions are appropriately aligned.
Keywords
Decision Support Systems; Decision Quality; Artificial Intelligence; Rice Disease Detection; Convolutional Neural Networks
Citations
IRE Journals:
Michael A. Castro, Herbert O. Tulan, Rennel M. Mallari, Marvin O. Mallari "AI-Enabled Decision Support System for Rice Disease Identification Under Operational Constraints" Iconic Research And Engineering Journals Volume 9 Issue 8 2026 Page 132-135 https://doi.org/10.64388/IREV9I8-1714109
IEEE:
Michael A. Castro, Herbert O. Tulan, Rennel M. Mallari, Marvin O. Mallari
"AI-Enabled Decision Support System for Rice Disease Identification Under Operational Constraints" Iconic Research And Engineering Journals, 9(8) https://doi.org/10.64388/IREV9I8-1714109
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
Important Links
For Authors
Volume 8:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 7:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 6:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 5:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 4:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 3:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12