Investigating the Mechanisms of Hyperspectral Remote Sensing for Belowground Yield Traits in Potato Plants

Potatoes, as the world’s fourth-largest staple crop, are vital for global food security. Efficient methods for assessing yield and quality are essential for policy-making and optimizing production. Traditional yield assessment techniques remain destructive, labor-intensive, and unsuitable for large-scale monitoring. While remote sensing has offered a promising alternative, current approaches largely depend on empirical correlations rather than physiological mechanisms. This limitation arises because potato tubers grow underground, rendering their traits invisible to aboveground sensors. This study investigated the mechanisms underlying hyperspectral remote sensing for assessing belowground yield traits in potatoes. Field experiments with four cultivars and five nitrogen treatments were conducted to collect foliar biochemistries (chlorophyll, nitrogen, and water and dry matter content), yield traits (tuber yield, fresh/dry weight, starch, protein, and water content), and leaf spectra. Two approaches were developed for predicting belowground yield traits: (1) a direct method linking leaf spectra to yield via statistical models and (2) an indirect method using structural equation modeling (SEM) to link foliar biochemistry to yield. The SEM analysis revealed that foliar nitrogen exhibited negative effects on tuber fresh weight (path coefficient b = −0.57), yield (−0.37), and starch content (−0.30). Similarly, leaf water content negatively influenced tuber water content (0.52), protein (−0.27), and dry weight (−0.42). Conversely, chlorophyll content showed positive associations with both tuber protein (0.59) and dry weight (0.56). Direct models (PLSR, SVR, and RFR) achieved higher accuracy for yield (R2 = 0.58–0.84) than indirect approaches (R2 = 0.16–0.45), though the latter provided physiological insights. The reduced accuracy in indirect methods primarily stemmed from error propagation within the SEM framework. Future research should scale these leaf-level mechanisms to canopy observations and integrate crop growth models to improve robustness across environments. This work advances precision agriculture by clarifying spectral–yield linkages in potato systems, offering a framework for hyperspectral-based yield prediction.