AI-Driven Early Detection Systems for Plant Pathogens

Abstract

Plant pathogens--including fungal, bacterial, viral, and oomycete agents--cause an estimated 10-16% annual global crop yield loss, with early and accurate detection being the most critical determinant of effective disease management and yield protection. This study develops and benchmarks an integrated AI-driven early detection system combining hyperspectral imaging, volatile organic compound (VOC) sensor arrays, and environmental monitoring with deep learning classification for six high-priority plant pathogens across three crops: Fusarium head blight (wheat), Phytophthora infestans (potato), Botrytis cinerea (tomato), Xanthomonas oryzae (rice), Erwinia amylovora (apple), and Puccinia striiformis (wheat) at experimental facilities in Estonia, Austria, and Switzerland. A multi-modal fusion architecture integrating ResNet-50 hyperspectral image features, 1D-CNN VOC sensor fingerprints, and environmental condition embeddings achieved overall pathogen detection F1-score of 0.934 at pre-symptomatic infection stages (2-12 days post-inoculation), compared to F1=0.847 for hyperspectral-only and F1=0.791 for VOC-only detection. Detection lead time relative to visible symptom appearance ranged from 3 days (Botrytis, tomato) to 11 days (Fusarium, wheat). The system achieved sensitivity of 91.4% and specificity of 93.8% across all pathogen-crop combinations, demonstrating practical viability for deployment as an integrated greenhouse and field crop health monitoring system.