A Hybrid Decision Support System for Rice Plant Disease Diagnosis and Treatment Recommendation Using Dempster-Shafer, AHP-TOPSIS, and Fuzzy SAW

Sistem Pendukung Keputusan Hibrida untuk Diagnosis Penyakit Tanaman Padi dan Rekomendasi Pengobatan Menggunakan Dempster-Shafer, AHP-TOPSIS, dan Fuzzy SAW

Abstract

Rice diseases — blast (Magnaporthe oryzae), bacterial leaf blight (Xanthomonas oryzae pv. oryzae), and sheath blight (Rhizoctonia solani)—cause annual global yield losses of 10–100%, resulting in billions of U.S. dollars in economic damage. Smallholder farmers in remote regions often lack access to agronomy experts and face difficulties using image-based diagnostic systems on low-capacity devices. This study proposes and evaluates a hybrid three-module Decision Support System (DSS) framework based on non-image tabular data to address these challenges. The framework integrates: (1) Dempster–Shafer Theory for probabilistic disease diagnosis using 48 structured clinical symptom parameters from ESforRPD2; (2) a hybrid AHP–TOPSIS module with CRITIC-based objective weight verification for multicriteria treatment ranking; and (3) an adaptive Fuzzy SAW module employing dynamic weights based on crop growth stages derived from Paddy Doctor Metadata. Experimental results show that the Dempster–Shafer module achieved 88.9% accuracy, a macro F1-score of 0.877, and a macro AUC-ROC of 0.939, outperforming Certainty Factor (82.4%), Random Forest (85.7%), and XGBoost (86.1%). The AHP model produced a valid Consistency Ratio (CR = 0.030), while CRITIC analysis revealed substantial differences between expert-assigned and data-driven weights. The adaptive Fuzzy SAW module achieved 100% agreement with agronomy expert recommendations (Spearman’s rho = 0.941), surpassing static SAW (25%, rho = 0.487) and standalone TOPSIS (0%, rho = 0.412). The framework operates without image input and provides recommendations in under two seconds, making it suitable for low-capacity devices and remote agricultural environment