Linking physiological nitrogen responses to spectral signatures: a multi-spectral imagery dataset (RGB and NDVI) for nitrogen deficiency detection in rice (Oryza sativa) across growth stages
Year: 2026
Type: preprint
Abstract: Open-access imagery datasets are essential for training, validating and comparing machine-learning models in agriculture, yet none exists for nitrogen (N) deficiency in rice grown by smallholder farmers in East Africa. This study produced paired Red-Green-Blue (RGB) and Normalised Difference Vegetation Index (NDVI) images of rice leaves showing N deficiency symptoms at six growth stages (40–90 days after planting, DAP). A field experiment with a randomised complete block design (four replications) tested four N rates: 0 (T0), 50 (T1), 100 (T2) and 150 kg N ha − 1 (T3), using the variety SARO 5 (TXD306) in Morogoro, Tanzania. Images were captured with a GoPro Hero 10 (RGB) and a Mapir Survey3 (NDVI) camera. From 12,000 raw images, automated and manual quality control retained 8,000 images (4,000 RGB and 4,000 NDVI; 1,000 per class). Plant height, leaf area, leaf tissue N and grain yield were analysed by one-way analysis of variance (ANOVA) with Tukey’s honestly significant difference (HSD) test (P ≤ 0.05). All four variables increased with N rate; T2 and T3 did not differ at most sampling dates, while T0 and T1 showed clear deficiency. Mean NDVI was strongly correlated with laboratory-measured tissue N (r = 0.893, R 2 = 0.798, P < 0.001), and a baseline MobileNetV3 classifier reached 75.3% accuracy in distinguishing the four N classes from RGB images. The openly available dataset links visible symptoms to measured N status and provides a foundation for deep-learning tools for N management in smallholder rice systems.
Source: Research Square
Institution: Sokoine University of Agriculture
Language: English
Subfield: Ecology
Field: Environmental Science
Domain: Physical Sciences
SDG: Zero hunger
Open Access status: green
OpenAlex ID: W7225851167