As one of the largest supplied grain crops, corn plants often require a significant amount of nitrogen fertilizer for optimal yield. However, excessive fertilizer usage can lead to adverse environmental consequences, especially for the nearby hydrological network. To precisely manage nitrogen application, accurate measurement of corn crop nitrogen deficiency is necessary. Hyperspectral imaging (HSI) techniques are widely applied in plant phenotyping to effectively measure plant traits caused by biotic or abiotic stresses. While previous HSI processing methods primarily focus on the overall color change, they rarely analyze the signal from the leaf-level spatial domain. However, early-stage nitrogen deficiency symptoms may not significantly alter the overall color, resulting in limited model performance in such cases. A newly developed HSI device called LeafSpec can scan an entire corn leaf with a high signal-over-noise ratio paired with high spatial-spectral resolution, capturing the detailed color changes at the leaf structure level. This study focused on identifying distinctive nitrogen deficiency indicators using an innovative methodology that applies spectral analysis to the details of leaf venation structures. The study started with developing an automated venation segmentation algorithm to separate a whole corn leaf into structural components. An in-depth examination of the spectral profiles associated with different leaf components introduced a new spatial-spectral index, demonstrating a higher correlation with the nitrogen content data than the averaged spectral indices. The high-resolution spectral-structural features discovered with this method provided new potential to improve the performance of the nitrogen prediction model in terms of both accuracy and robustness.
Hyperspectral imaging (HSI) is being widely applied in plant phenotyping platforms. Some new HSI devices such as LeafSpec was introduced recently which can provide a high signal-over-noise ratio along with higher spectral and spatial resolutions. However, most of the previous image processing algorithms only calculated the averaged spectrum from the leaf, but rarely include the spatially distributed information on the leaf level. Meanwhile, different nutrient stresses could result in different color patterns on the leaf which can be used to furtherly improve the quality of plant phenotyping. This study focused on the development of a new methodology that applies spatial distribution analysis on HSI soybean leaf images. Firstly, a novel way of encoding all the leaf pixels to a new coordinate system called Natural Leaf Coordinate System (NLCS) was developed. NLCS defined the coordinates of every pixel relative to the leaf venation so that the following spatial distribution analysis could be conducted more intuitively. Second, a new nitrogen index based on NLCS called NLCS-N was developed and able to outperform the whole leaf averaged NDVI by having a better correlation with the plants' nitrogen contents, and a more significant differentiation between the nitrogen-sufficient versus the nitrogen-deficient plants.