Presymptomatic plant disease detection with PSNet: A low-cost hyperspectral imaging and RGB fusion framework.

Abstract

Plant pathogens cause yield losses worldwide, threatening food security and livelihoods. Because early infection is difficult to diagnose, management often relies on prophylactic pesticide use, increasing costs and environmental impact. Here we present PSNet, a multimodal framework that fuses hyperspectral imaging with RGB information for presymptomatic plant disease detection, together with a low-cost hyperspectral camera incorporating a 3D-printed housing, costing under {pound}500. We validate PSNet using Arabidopsis thaliana infected with the oomycete Albugo candida. Imaging at 2 and 4 days post inoculation, prior to visible symptoms, revealed spectral signatures that distinguished infected from healthy plants, while imaging at 6 days post inoculation captured the transition toward early symptom emergence. Discriminative spectral regions overlapped wavelengths associated with plant responses to biotic stress, supporting the biological plausibility of these signatures. Performance was evaluated using strict plant-level partitioning, ensuring samples from the same plant were confined to a single split. On a four-class task (healthy, 2 dpi, 4 dpi, 6 dpi), PSNet achieved 90.00% accuracy and 97.50% accuracy for binary classification. Together, these results demonstrate that presymptomatic detection is feasible under controlled conditions using low-cost hardware and multimodal learning, underscoring the potential of scalable multimodal systems for early disease monitoring.