  Nitrogen (N) fertilizer is routinely applied by farmers to increase crop
yields. At present, farmers often over-apply N fertilizer in some locations or
at certain times because they do not have high-resolution crop N status data.
N-use efficiency can be low, with the remaining N lost to the environment,
resulting in higher production costs and environmental pollution. Accurate and
timely estimation of N status in crops is crucial to improving cropping
systems' economic and environmental sustainability. Destructive approaches
based on plant tissue analysis are time consuming and impractical over large
fields. Recent advances in remote sensing and deep learning have shown promise
in addressing the aforementioned challenges in a non-destructive way. In this
work, we propose a novel deep learning framework: a self-supervised
spectral-spatial attention-based vision transformer (SSVT). The proposed SSVT
introduces a Spectral Attention Block (SAB) and a Spatial Interaction Block
(SIB), which allows for simultaneous learning of both spatial and spectral
features from UAV digital aerial imagery, for accurate N status prediction in
wheat fields. Moreover, the proposed framework introduces local-to-global
self-supervised learning to help train the model from unlabelled data. The
proposed SSVT has been compared with five state-of-the-art models including:
ResNet, RegNet, EfficientNet, EfficientNetV2 and the original vision
transformer on both testing and independent datasets. The proposed approach
achieved high accuracy (0.96) with good generalizability and reproducibility
for wheat N status estimation.
