Pixel-level high-throughput estimation of crop photosynthetic phenotyping parameters using a multi-task deep learning framework

Hyperspectral remote sensing holds promise for estimating crop physiological and biochemical traits, yet conventional approaches are often constrained by small sample sizes, limited accuracy in multi-trait retrieval, and poor robustness. To alleviate these challenges, we present an integrated framework that combines a pixel-wise dataset construction strategy with a multi-task deep learning architecture for estimating photosynthetic phenotyping parameters (PPPs)—a suite of traits encompassing gas exchange, pigment composition, and structural properties. Specifically, under controlled experimental conditions, we developed MTI-Net, a lightweight multi-task inversion network that simultaneously estimates multiple PPPs in rice and tomato with high precision ( R 2 > 0.90). Since physiological ground-truth measurements were obtained at the leaf level, the pixel-wise predictions should be interpreted as weakly supervised, model-derived spatial estimates or relative distributions rather than directly validated pixel-level physiological measurements. This methodological framework demonstrated stable performance across data partitions and random initializations, and maintained stable performance when applied to pixel-averaged spectra. A complementary differentiable band selection module (DRS-Net) further reduced spectral dimensionality to 40 informative bands while maintaining retrieval accuracy above 0.75, suggesting potential pathways for simplified sensor design. Importantly, the estimated PPPs revealed biologically interpretable patterns in tomato, including stage-dependent dynamics, irradiance-driven shifts, and canopy-layer differentiation—where middle leaves dominated early photosynthesis and upper leaves prevailed at later stages. Collectively, this study provides a methodological foundation for proximal hyperspectral phenotyping under specific environments. While further validation in diverse field conditions is required, the framework demonstrates potential for high-throughput multi-parameter estimation and provides a methodological basis for future cost-effective hyperspectral systems.