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Assessing soil organic carbon (SOC) stocks is crucial for understanding the carbon sequestration potential of agroecosystems and for mitigating climate change. This study presents a novel method for assessing SOC and mineral content at various soil depths in sorghum crops using hyperspectral remote sensing. Conducted at Planthaven Farms, MO, the research encompassed ten genotypes across 30 plots, yielding 180 soil samples from six depth intervals (0–150 cm) of bare soil. Chemical analyses determined the SOC and mineral levels, which were then compared to spectral data from HySpex indoor sensors. We utilized time-frequency analysis methods, including discrete wavelet transformation (DWT), continuous wavelet transformation (CWT), and frame transformation along with traditional spectral transformations, specifically fractional derivatives and continuum removal. The analysis revealed the shortwave infrared (SWIR) region, particularly the 1800–2000 nm range, as having the strongest correlations with SOC content (with R2 exceeding 0.8). The visible near-infrared (VNIR) region also provided valuable insights. Models incorporating CWT achieved high accuracy (test R2 exceeding 0.9), while frame transformation achieved strong accuracy (test R2 between 0.7 and 0.8) with fewer features. The random forest regressor (RFR) proved to be most robust, demonstrating superior accuracy and reduced overfitting compared to support vector regression (SVR), partial least squares regression (PLSR), and deep neural network (DNN) models. The models demonstrated the efficacy of hyperspectral data for SOC estimation, suggesting potential for future applications that integrate this data with above-ground biomass to improve SOC mapping across larger scales. This research offers a promising spectral transformation approach for effective carbon management and sustainable agriculture in a changing climate. 

The potential of artificial intelligence (AI) and machine learning (ML) in agriculture for improving crop yields and reducing the use of water, fertilizers, and pesticides remains a challenge. The goal of this work was to introduce Hyperfidelis, a geospatial software package that provides a comprehensive workflow that includes imagery visualization, feature extraction, zonal statistics, and modeling of key agricultural traits including chlorophyll content, yield, and leaf area index in a ML framework that can be used to improve food security. The platform combines a user-friendly graphical user interface with cutting-edge machine learning techniques, bridging the gap between plant science, agronomy, remote sensing, and data science without requiring users to possess any coding knowledge. Hyperfidelis offers several data engineering and machine learning algorithms that can be employed without scripting, which will prove essential in the plant science community.