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

Plant pathogens cause major 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, portable hyperspectral camera incorporating a 3D-printed housing and optical mounts, costing under {pound}500. We validate the approach using Arabidopsis thaliana infected with the oomycete Albugo candida. Imaging at 2 and 4 days post inoculation, prior to visible symptoms, revealed consistent spectral signatures that distinguished infected from healthy plants, while imaging at 6 days post inoculation captured the transition toward early symptom emergence. The most discriminative spectral regions overlapped wavelengths previously associated with plant responses to biotic stress, supporting the biological plausibility of these signatures. On a four-class task (healthy, 2 dpi, 4 dpi, 6 dpi), PSNet achieved 92.7% overall accuracy and 97.1% accuracy for binary healthy versus infected 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.