AI-Driven Decision Support System for Sustainable Agarwood Cultivation and Export Readiness

Abstract

Agarwood cultivation and export involve multiple critical decision points that directly affect resin quality, economic value, and market acceptance. In current practice, decisions related to resin induction timing, disease identification, and export readiness are largely based on manual inspection and subjective judgment, leading to inconsistent assessments and avoidable losses. This paper presents an AI-based decision support system to support sustainable agarwood cultivation and export by integrating three analytical components: resin induction stage classification, export readiness and quality assessment, and leaf disease detection with remedy recommendation. A multimodal deep learning approach combining bark images and numerical tree parameters is used for resin induction stage classification, achieving a test accuracy of 93% using an EfficientNetB0 with a Multi-Layer Perceptron (MLP). Agarwood resin and chip quality grading is performed using an EfficientNetB0-based Convolutional Neural Network (CNN), while export readiness is evaluated using a Random Forest-based numerical model. Leaf disease detection is implemented using a CNN-based classifier, achieving an overall accuracy of 80% across four common agarwood leaf disease classes. Explainability mechanisms, including Gradient-weighted Class Activation Mapping (Grad-CAM) and reason based readiness analysis, are incorporated to enhance transparency and user trust. Experimental results indicate that the proposed system reduces subjectivity and supports data-driven decision-making across key stages of the agarwood value chain.