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Enhancing resource use efficiency in agricultural field management and breeding high-performance crop varieties are crucial approaches for securing crop yield and mitigating negative environmental impact of crop production. Crop stress sensing and plant phenotyping systems are integral to variable-rate (VR) field management and high-throughput plant phenotyping (HTPP), with both sharing similarities in hardware and data processing techniques. Crop stress sensing systems for VR field management have been studied for decades, aiming to establish more sustainable management practices. Concurrently, significant advancements in HTPP system development have provided a technological foundation for reducing conventional phenotyping costs. In this paper, we present a systematic review of crop stress sensing systems employed in VR field management, followed by an introduction to the sensors and data pipelines commonly used in field HTPP systems. State-of-the-art sensing and decision-making methodologies for irrigation scheduling, nitrogen application, and pesticide spraying are categorized based on the degree of modern sensor and model integration. We highlight the data processing pipelines of three ground-based field HTPP systems developed at the University of Nebraska-Lincoln. Furthermore, we discuss current challenges and propose potential solutions for field HTPP research. Recent progress in artificial intelligence, robotic platforms, and innovative instruments is expected to significantly enhance system performance, encouraging broader adoption by breeders. Direct quantification of major plant physiological processes may represent one of next research frontiers in field HTPP, offering valuable phenotypic data for crop breeding under increasingly unpredictable weather conditions. This review can offer a distinct perspective, benefiting both research communities in a novel manner.

Förster resonance energy transfer (FRET) is commonly utilized in organic solar cells (OSCs) to enhance the power conversion efficiency (PCE) by promoting molecular interactions. The PCE is greatly affected by the number of photo‐generated excitons that effectively reach interfaces between the donor and acceptor materials of OSCs. However, the correlation between FRET and exciton diffusion has received limited attention. Therefore, it is crucial to understand and manipulate FRET process for investigating exciton dynamics in OSCs. Herein, BTA3 and its derivatives are chosen as the third components and energy donors to elucidate the intrinsic photophysical mechanism of FRET in OSCs by manipulating their efficiency. The results unambiguously demonstrate that the addition of guest F‐BTA3 exhibits the highest FRET efficiency, leading to a significant enhancement in the PCE of both PM6:Y6 and PM6:PY‐IT host systems. By correlating FRET process and exciton dynamics, this enhancement to remarkable increment in exciton diffusion length is attributed. Experimental results and theoretical simulations indicate high FRET efficiency arises from strong dipole–dipole interaction, and exhibits a positive correlation with exciton diffusion length. This study showcases the effective regulation of exciton diffusion in OSCs through modulation of FRET, offering a novel perspective for optimizing the performance of organic photovoltaic devices. [ABSTRACT FROM AUTHOR]

Rapid and cost‐effective techniques for soil analysis are essential to guide sustainable land management and production agriculture. This study aimed at evaluating the performance and consistency of portable handheld Fourier‐transform near‐infrared spectrometers and the NeoSpectra scanners in estimating 12 common soil physical and chemical properties including pH; organic carbon (OC); inorganic carbon (IC); total nitrogen (TN); cation exchange capacity (CEC); clay, silt, and sand fractions; and exchangeable potassium (K), phosphorus (P), calcium (Ca), and magnesium (Mg). A diverse set of samples (n = 600) were retrieved from a national‐scale soil archive of the Kellogg Soil Survey Laboratory of USDA‐NRCS and scanned with five NeoSpectra scanners. Predictive models for the soil properties were developed using partial least squares regression (PLSR), Cubist, and memory‐based learning (MBL). Cubist outperformed PLSR and MBL, with the best prediction performance for clay, OC, and CEC (R2 > 0.7), followed by IC, sand, silt, and Mg (R2 > 0.6), and then pH, TN, and Ca (R2 > 0.5). K and P were predicted somewhat poorly with R2 of 0.48 and 0.22. All five NeoSpectra yielded comparable near‐infrared (NIR) spectral data and the PLSR models for the soil properties (in terms of model regression coefficients). However, the consistency assessment showed that the model performance was significantly decreased when the training and testing spectra were from different NeoSpectra scanners. It is concluded that NeoSpectra scanners could be rapid and cost effective for estimating certain soil properties, and calibration transfer should be considered for applications where multiple devices are involved and high estimation accuracy from NIR data is required. Core Ideas: Soil samples (n = 600) were retrieved from Kellogg Soil Survey Laboratory and analyzed with 5 NeoSpectra scanners.Partial least squares regression (PLSR), Cubist, and memory‐based learning (MBL) were employed to estimate 12 soil properties from NIR data.Cubist outperformed PLSR and MBL, with 10 of 12 properties having R2 > 0.5.The five NeoSpectra scanners yielded comparable NIR spectral data and PLSR models.Cross‐device assessment suggested that calibration transfer is needed to maintain higher accuracy of NIR models. [ABSTRACT FROM AUTHOR]