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Etika Dyah Puspitasari
Dept. Biology Education Universitas Ahmad Dahlan, Yogyakarta, Indonesia
Computer Science & IT

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Journal : International Journal of Advances in Intelligent Informatics

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AI-driven stress monitoring in melon crops via graph neural networks Son Ali Akbar; Jihad Rahmawan; Etika Dyah Puspitasari; Anton Yudhana; Novi Febrianti
International Journal of Advances in Intelligent Informatics Vol 12, No 2 (2026): May 2026
Publisher : Universitas Ahmad Dahlan

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Abstract

Melon cultivation is highly vulnerable to abiotic and biotic stress, and early detection remains difficult when monitoring relies on a single sensing modality. This study investigated a multimodal stress-classification framework that combined root-zone measurements and canopy reflectance descriptors for melon monitoring under greenhouse conditions. Soil pH, nitrogen, phosphorus, potassium, and temperature were acquired using an RS485 multi-parameter sensor, while canopy images were captured using a Raspberry Pi NoIR camera and converted into Normalized Difference Vegetation Index features. Each synchronized observation was represented as a graph with fixed variable nodes and correlation-based edges, enabling relation-aware learning through a Graph Convolutional Network. The proposed model was evaluated using cross-validation and compared against conventional machine learning and non-graph deep learning baselines. The graph-based model achieved the best overall classification performance, indicating that explicit modeling of soil-canopy dependencies improved discrimination between healthy and stressed plants. The results suggest that graph-structured multimodal fusion is a promising strategy for AI-assisted crop stress monitoring.
Co-Authors Anton Yudhana Jihad Rahmawan Novi Febrianti Son Ali Akbar