Abstract
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.
Highlights
Publisher’s Note: MDPI stays neutralNitrogen is an essential plant nutrient and is vital for plant growth and development.The application of N fertilizers has revolutionized farming, increasing crop yields and food production to meet the nutritional needs of billions of people
We evaluate the effect of the proposed model, compared to the original vision transformer
We have proposed a novel spectral–spatial attention-based vision transformer (SSVT)
Summary
The application of N fertilizers has revolutionized farming, increasing crop yields and food production to meet the nutritional needs of billions of people. N fertilizers enhances soil fertility and increases crop yields. N inputs are costly for farmers but do not deliver any additional yield benefits, instead resulting in the pollution of natural ecosystems, increases in emissions of the potent greenhouse gas nitrous oxide, and reductions in biodiversity [3,4]. Wheat crops invariably require fertilizer to grow optimally and are the world’s most commonly consumed cereal grain and one of the worldwide staple foods. About 35–40% of the global population depend on wheat as their major food crop [5]. Accurate monitoring of the N status in wheat informs farmer decisions on nitrogen fertilizer application rates and timing. It is with regard to jurisdictional claims in published maps and institutional affiliations
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