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Nitrogen (N) fertilizer is routinely applied by farmers to increase cropyields. At present, farmers often over-apply N fertilizer in some locations orat 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 andtimely estimation of N status in crops is crucial to improving croppingsystems' economic and environmental sustainability. Destructive approachesbased on plant tissue analysis are time consuming and impractical over largefields. Recent advances in remote sensing and deep learning have shown promisein addressing the aforementioned challenges in a non-destructive way. In thiswork, we propose a novel deep learning framework: a self-supervisedspectral-spatial attention-based vision transformer (SSVT). The proposed SSVTintroduces a Spectral Attention Block (SAB) and a Spatial Interaction Block(SIB), which allows for simultaneous learning of both spatial and spectralfeatures from UAV digital aerial imagery, for accurate N status prediction inwheat fields. Moreover, the proposed framework introduces local-to-globalself-supervised learning to help train the model from unlabelled data. Theproposed SSVT has been compared with five state-of-the-art models including:ResNet, RegNet, EfficientNet, EfficientNetV2 and the original visiontransformer on both testing and independent datasets. The proposed approachachieved high accuracy (0.96) with good generalizability and reproducibilityfor wheat N status estimation.

The self-supervised spectral-spatial attention-based transformer network for automated, accurate prediction of crop nitrogen status from UAV imagery