The accurate quantification of yield in rapeseed is important for evaluating the supply of vegetable oil, especially at regional scales. This study developed an approach to estimate rapeseed yield with remotely sensed canopy spectra and abundance data by spectral mixture analysis. A six-band image of the studied rapeseed plots was obtained by an unmanned aerial vehicle (UAV) system during the rapeseed flowering stage. Several widely used vegetation indices (VIs) were calculated from canopy reflectance derived from the UAV image. And the plot-level abundance of flower, leaf and soil, indicating the fraction of different components within the plot, was retrieved based on spectral mixture analysis on the six-band image and endmember spectra collected in situ for different components. The results showed that for all tested indices VI multiplied by leaf-related abundance closely related to rapeseed yield. The product of Normalized Difference Vegetation Index and short-stalk-leaf abundance was the most accurate for estimating yield in rapeseed under different nitrogen treatments with the estimation errors below 13%. This study gives an important indication that spectral mixture analysis needs to be considered when estimating yield by remotely sensed VI, especially for the image containing obviously spectral different components or for crops which have conspicuous flowers or fruits with significantly different spectra from their leave.

Leaf area index (LAI) estimation is very important, and not only for canopy structure analysis and yield prediction. The unmanned aerial vehicle (UAV) serves as a promising solution for LAI estimation due to its great applicability and flexibility. At present, vegetation index (VI) is still the most widely used method in LAI estimation because of its fast speed and simple calculation. However, VI only reflects the spectral information and ignores the texture information of images, so it is difficult to adapt to the unique and complex morphological changes of rice in different growth stages. In this study we put forward a novel method by combining the texture information derived from the local binary pattern and variance features (LBP and VAR) with the spectral information based on VI to improve the estimation accuracy of rice LAI throughout the entire growing season. The multitemporal images of two study areas located in Hainan and Hubei were acquired by a 12-band camera, and the main typical bands for constituting VIs such as green, red, red edge, and near-infrared were selected to analyze their changes in spectrum and texture during the entire growing season. After the mathematical combination of plot-level spectrum and texture values, new indices were constructed to estimate rice LAI. Comparing the corresponding VI, the new indices were all less sensitive to the appearance of panicles and slightly weakened the saturation issue. The coefficient of determination (R²) can be improved for all tested VIs throughout the entire growing season. The results showed that the combination of spectral and texture features exhibited a better predictive ability than VI for estimating rice LAI. This method only utilized the texture and spectral information of the UAV image itself, which is fast, easy to operate, does not need manual intervention, and can be a low-cost method for monitoring crop growth.

Aims: Most crops are supplemented with selenium (Se) through the exogenous addition of inorganic Se fertilizer. There is a lack of in-depth research on organic Se fertilizers. Meanwhile, the dosage range between human Se deficiency and Se toxicity is narrow, so the Se content of agricultural products needs to be controlled within a reasonable interval. Methods: Se accumulation and speciation in rice were analyzed and compared during three different growth stages (late tillering stage, initiate heading stage, and full heading stage) using three Se fertilizers: selenite, fermented Se, and potassium selenocyanoacetate (Se-AAF) via the foliar application. Results: The Se content in rice sprayed with organic Se fertilizer was controlled in the relatively safe range and met the human Se supplement requirement compared to the sprayed sodium selenite, which was too high of a dose. The percentage of organic Se and protein Se in brown rice was found to be similar in all three Se fertilizers. The highest organic Se content of 91.57% was found in the grain of rice at the full heading stage by spraying Se-AAF. The main Se species in the grain was selenomethionine (SeMet), which reached 80% of the total Se. Se-methyl selenocysteine(SeMeCys) was found only in Se-AAF. The grain quality showed that all three Se fertilizers increased the consistency of gelatinization. Conclusions: Appropriately delaying the spraying time and selecting organic Se fertilizer as the Se source can help to produce green and safe selenium-rich rice.

Lodging is being increasingly recognized as a critical concern among crop breeders worldwide as it causes a significant reduction in the grain yield and quality of multiple cereal crops. The lodging index is frequently used for evaluating the lodging resistance of rice (Oryza sativa L.) crops. However, the application of lodging index for comparing lodging susceptibility between different rice cultivars is inadequate under certain special conditions. This study was aimed to identify optimized parameters that would evaluate the lodging susceptibility and could then be used along with the lodging index for further assessing the lodging resistance. A total of 32 representative rice accessions from different countries were grown at two different locations on three different sowing dates over a period of 3 yr. The analysis of the lodging index of the different rice cultivars revealed the lodging index value of 200 as the critical threshold value of the evaluated rice accessions. Subsequently, an optimized parameter from the lodging index was proposed. This parameter could be interpreted as the magnitude of the external force that the basal second internode could withstand. A comparison of the optimized parameter values of different rice accessions revealed a previously unknown lodging‐resistant variety R379. The application of the optimized parameter would enable an accurate determination and comparison of the lodging resistance among different rice cultivars, thereby contributing to the effective selection of lodging‐resistant cultivars in rice breeding programs.

Identification of high Nitrogen Use Efficiency (NUE) phenotypes has been a long-standing challenge in breeding rice and sustainable agriculture to reduce the costs of nitrogen (N) fertilizers. There are two main challenges: (1) high NUE genetic sources are biologically scarce and (2) on the technical side, few easy, non-destructive, and reliable methodologies are available to evaluate plant N variations through the entire growth duration (GD). To overcome the challenges, we captured a unique higher NUE phenotype in rice as a dynamic time-series N variation curve through the entire GD analysis by canopy reflectance data collected by Unmanned Aerial Vehicle Remote Sensing Platform (UAV-RSP) for the first time. LY9348 was a high NUE rice variety with high Nitrogen Uptake Efficiency (NUpE) and high Nitrogen Utilization Efficiency (NUtE) shown in nitrogen dosage field analysis. Its canopy nitrogen content (CNC) was analyzed by the high-throughput UAV-RSP to screen two mixed categories (51 versus 42 varieties) selected from representative higher NUE indica rice collections. Five Vegetation Indices (VIs) were compared, and the Normalized Difference Red Edge Index (NDRE) showed the highest correlation with CNC (r = 0.80). Six key developmental stages of rice varieties were compared from transplantation to maturation, and the high NUE phenotype of LY9348 was shown as a dynamic N accumulation curve, where it was moderately high during the vegetative developmental stages but considerably higher in the reproductive developmental stages with a slower reduction rate. CNC curves of different rice varieties were analyzed to construct two non-linear regression models between N% or N% × leaf area index (LAI) with NDRE separately. Both models could determine the specific phenotype with the coefficient of determination (R²) above 0.61 (Model I) and 0.86 (Model II). Parameters influencing the correlation accuracy between NDRE and N% were found to be better by removing the tillering stage data, separating the short and long GD varieties for the analysis and adding canopy structures, such as LAI, into consideration. The high NUE phenotype of LY9348 could be traced and reidentified across different years, locations, and genetic germplasm groups. Therefore, an effective and reliable high-throughput method was proposed for assisting the selection of the high NUE breeding phenotype.

The accurate estimation of rice yield using remote sensing (RS) technology is crucially important for agricultural decision-making. The rice yield estimation model based on the vegetation index (VI) is commonly used when working with RS methods, however, it is affected by irrelevant organs and background especially at heading stage. The spectral mixture analysis (SMA) can quantitatively obtain the abundance information and mitigate the impacts. Furthermore, according to the spectral variability and information complexity caused by the rice cropping system and canopy characteristics of reflection and scattering, in this study, the multi-endmember extraction by the pure pixel index (PPI) and the nonlinear unmixing method based on the bandwise generalized bilinear mixing model (NU-BGBM) were applied for SMA, and the VI_E (VIs recalculated from endmember spectra) was integrated with abundance data to establish the yield estimation model at heading stage. In two paddy fields of different cultivation settings, multispectral images were collected by an unmanned aerial vehicle (UAV) at booting and heading stage. The correlation of several widely-used VIs and rice yield was tested and weaker at heading stage. In order to improve the yield estimation accuracy of rice at heading stage, the VI_E and foreground abundances from SMA were combined to develop a linear yield estimation model. The results showed that VI_E incorporated with abundances exhibited a better estimation ability than VI alone or the product of VI and abundances. In addition, when the structural difference of plants was obvious, the addition of the product of VI_F (VIs recalculated from bilinear endmember spectra) and the corresponding bilinear abundances to the original product of VI_E and abundances, enhanced model reliability. VIs using the near-infrared bands improved more significantly with the estimation error below 8.1%. This study verified the validation of the targeted SMA strategy while estimating crop yield by remotely sensed VI, especially for objects with obvious different spectra and complex structures.