Unmanned aerial vehicle (UAV)-based multispectral imaging is one of the most widely used technologies for rapid crop monitoring, essential for crop-growth management. However, the technology's complex optical structure and difficulty in interpreting real-time crop-growth information seriously restrict its application. This paper presents a newly designed UAV-based snapshot multispectral imaging crop-growth sensor (SMICGS) aimed at simplifying the optical structure and realizing the online interpretation of crop spectral information. Mosaic filters based on the special spectral characteristics of crops were designed to achieve multiband co-optical imaging. A spectral crosstalk correction method based on the pixel response characteristics of SMICGS was proposed, and a processing system based on the coupling of sensor information and crop-growth monitoring models was developed to realize real-time online processing of crop spectral information. Field experiments showed that the vegetation indices obtained by SMICGS combined with the machine learning algorithm random forest (RF) achieved better results in predicting leaf area index (LAI) and above-ground biomass (AGB) for wheat and rice. For wheat, the R² and root mean square error (RMSE) values for the LAI and AGB prediction models were 0.81 and 0.85, and 0.682 and 1.127 t/ha, respectively. For rice, the R² and RMSE values for the LAI and AGB prediction models were 0.89 and 0.93, and 0.818 and 0.866 t/ha, respectively. Overall, SMICGS provides a reliable foundational tool for real-time, non-destructive monitoring of field crop growth information, offering significant potential for the precise management of agricultural production.

Ensuring food security has become a global challenge owing to climate change and population growth. High-throughput phenotyping can effectively drive crop genetic enhancement, which can potentially solve food crisis. Phenotyping robot is an essential part of crop ground phenotyping information monitoring, although there are challenges such as the inability to adjust the fixed track width, poor load capacity of the detection robotic arm, and inability to fuse information in real-time. This study reports a phenotyping robot with a gantry-style chassis featuring an adjustable wheeltrack (1400–1600 ​mm) to adapt to different row spacing arrangements and reduced damage, and function effectively in both dry field and paddy field environments. A six-degree-of-freedom sensor gimbal with high payload capacity is also developed to enable precise height (1016–2096 ​mm) and angle adjustments. Additionally, this study introduces an enhanced method for data acquisition from multiple imaging sensors through registration and fusion using Zhang's calibration and feature point extraction algorithm, calculating a homography matrix for high-throughput data collection at fixed positions and heights. The experimental validation results demonstrate that the RMSE of the registration algorithm does not exceed 3 pixels. The gimbal data strongly correlated with that of a handheld instrument data (r² ​> ​0.90). The robot is practical, reliable, and fully functional, offering a solid theoretical foundation and equipment support for high-throughput phenotyping.

Accurate nitrogen (N) nutrition diagnosis is essential for improving N use efficiency in crop production. The widely used critical N (Nc) dilution curve traditionally depends solely on agronomic variables, neglecting crop water status. With three-year field experiments with winter wheat, encompassing two irrigation levels (rainfed and irrigation at jointing and anthesis) and three N levels (0, 180, and 270 kg ha⁻¹), this study aims to establish a novel approach for determining the Nc dilution curve based on crop cumulative transpiration (T), providing a comprehensive analysis of the interaction between N and water availability. The Nc curves derived from both crop dry matter (DM) and T demonstrated N concentration dilution under different conditions with different parameters. The equation Nc = 6.43T^−0.24 established a consistent relationship across varying irrigation regimes. Independent test results indicated that the nitrogen nutrition index (NNI), calculated from this curve, effectively identifies and quantifies the two sources of N deficiency: insufficient N supply in the soil and insufficient soil water concentration leading to decreased N availability for root absorption. Additionally, the NNI calculated from the Nc-DM and Nc-T curves exhibited a strong negative correlation with accumulated N deficit (Nand) and a positive correlation with relative grain yield (RGY). The NNI derived from the Nc-T curve outperformed the NNI derived from the Nc-DM curve concerning its relationship with Nand and RGY, as indicated by larger R² values and smaller AIC. The novel Nc curve based on T serves as an effective diagnostic tool for assessing winter wheat N status, predicting grain yield, and optimizing N fertilizer management across varying irrigation conditions. These findings would provide new insights and methods to improve the simulations of water-N interaction relationship in crop growth models.

Extreme heat stress events are becoming more frequent under anticipated climate change, which can have devastating impacts on rice growth and yield. To quantify the effects of short-term heat stress at booting stage on nonstructural carbohydrates (NSC) remobilization in rice, two varieties (Nanjing 41 and Wuyunjing 24) were subjected to 32/22/27 ℃ (maximum/minimum/mean), 36/26/31 ℃, 40/30/35 ℃, and 44/34/39 ℃ for 2, 4 and 6 days in phytotrons at booting stage during 2014 and 2015. Yield and yield components, dry matter partitioning index (DMPI), NSC accumulation and translocation were measured and calculated. The results showed that the increase of high-temperature level and duration significantly reduced grain yield by suppressing spikelet number per panicle, seed-setting rate, and grain weight. Heat stress at booting decreased DMPI in panicles, increased DMPI in stems, but had no significant effect on photosynthetic rate. Stem NSC concentration increased whereas panicles NSC concentration, stem NSC translocation efficiency, and contribution of stem NSC to grain yield decreased. Severe heat stress even transformed the stem into a carbohydrate sink during grain filling. The heat-tolerant Wuyunjing 24 showed a higher NSC transport capacity under heat stress than the heat-sensitive Nanjing 41. Heat degree-days (HDD), which combines the effects of the intensity and duration of heat stress, used for quantifying the impacts of heat stress indicates the threshold HDD for the termination of NSC translocation is 9.82 ℃ day. Grain yield was negatively correlated with stem NSC concentration and accumulation at maturity, and yield reduction was tightly related to NSC translocation reduction. The results suggest that heat stress at booting inhibits NSC translocation due to sink size reduction. Therefore, genotypes with higher NSC transport capacity under heat stress could be beneficial for rice yield formation.