<p dir="ltr">Among the major row crops in the United States, corn and soybeans stand out due to their high nutritional value and economic importance. Achieving optimal yields is restrained by the challenge of fertilizer management. Many fields experience yield losses due to insufficient mineral nutrients like nitrogen (N), while excessive fertilization raises costs and environmental risks. The critical issue is the accurate determination of fertilizer quantity and timing, underscoring the need for precise, early-stage diagnostics. Emerging high-throughput plant phenotyping techniques, notably hyperspectral imaging (HSI), have been increasingly utilized to identify plant’s responses to abiotic or biotic stresses. Varieties of HSI systems have been developed, such as airborne imaging systems and indoor imaging stations. However, most of the current HSI systems’ signal quality is often compromised by various environmental factors. To address the issue, a handheld hyperspectral imager known as LeafSpec was recently developed at Purdue University and represents a breakthrough with its ability to scan corn or soybean leaves at exceptional spatial and spectral resolutions, improving plant phenotyping quality at reduced costs. Most of the current HSI data processing methods focus on spectral features but rarely consider spatially distributed information. Thus, the objective of this work was to develop a methodology utilizing spatial-spectral features for accurate and reliable diagnostics of crop N nutrient stress. The key innovations include the designing of spatial-spectral features based on the leaf venation structures and the feature mining method for predicting the plant nitrogen condition. First, a novel analysis method called the Natural Leaf Coordinate System (NLCS) was developed to reallocate leaf pixels and innovate the nutrient stress analysis using pixels’ relative locations to the venation structure. A new nitrogen prediction index for soybean plants called NLCS-N was developed, outperforming the conventional averaged vegetation index (Avg. NDVI) in distinguishing healthy plants from nitrogen-stressed plants with higher t-test p-values and predicting the plant nitrogen concentration (PNC) with higher R-squared values. In one of the test cases, the p-values and R-squared values were improved, respectively, from 2.1×10^-3 to 6.92×10^-12 and from 0.314 to 0.565 by Avg. NDVI and NLCS-N. Second, a corn leaf venation segmentation algorithm was developed to separate the venation structure from a corn leaf LeafSpec image, which was further used to generate 3930 spatial-spectral (S-S) features. While the S-S features could be the input variable to build a PNC prediction model, a feature selection mechanism was developed to improve the models’ accuracy in terms of reduced cross-validation errors. In one of the test cases, the cross-validation root mean squared errors were reduced compared with the leaf mean spectra from 0.273 to 0.127 using the selected features. Third, several novel spatial-spectral indexes for corn leaves were developed based on the color distributions at the venation level. The top-performing indexes were selected through a ranking system based on Cohen’s d values and the R-squared values, resulting in a best-performing S-S N prediction index with 0.861 R-squared values for predicting the corn PNC in a field assay. The discussion sections provided insights into how a robust PNC prediction index could be developed and related to plant science. The methodologies outlined offer a framework for broader applications in spatial-spectral analysis using leaf-level hyperspectral imagery, serving as a guide for scientists and researchers in customizing their future studies within this field.</p>