Estimating plant nitrogen concentration (PNC) has been conducted using vegetation indices (VIs) from UAV-based imagery, but color features have been rarely considered as additional variables. In this study, the VIs and color moments (color feature) were calculated from UAV-based RGB images, then partial least square regression (PLSR) and random forest regression (RF) models were established to estimate PNC through fusing VIs and color moments. The results demonstrated that the fusion of VIs and color moments as inputs yielded higher accuracies of PNC estimation compared to VIs or color moments as input; the RF models based on the combination of VIs and color moments (R² ranging from 0.69 to 0.91 and NRMSE ranging from 0.07 to 0.13) showed similar performances to the PLSR models (R² ranging from 0.68 to 0.87 and NRMSE ranging from 0.10 to 0.29); Among the top five important variables in the RF models, there was at least one variable which belonged to the color moments in different datasets, indicating the significant contribution of color moments in improving PNC estimation accuracy. This revealed the great potential of combination of RGB-VIs and color moments for the estimation of rice PNC.

Rice yield is not only influenced by factors of varieties and managements, but also by environmental factors. In this study, agronomic trait data of rice and climate data in eastern China were collected, and rice yields were predicted using a variety of algorithms, including the non-linear tool of feed-forward backpropagation neural networks (FFBN) and the linear model of partial least squares regression (PLSR). The results showed that both the agronomic traits and the climate data were significantly related with rice yield. The PLSR model showed that covariates occurred among the parameters, and modifications should be considered for climate data-based modelling. The FFBN model demonstrated better prediction performance than that of PLSR, in which the relation coefficient (R²) and root mean square error (RMSE) were 0.611 vs. 0.374 and 0.578 vs. 0.865 ton/ha using climate data, respectively; and 0.742 vs. 0.689 and 0.556 vs. 0.608 using agronomic trait data, respectively. When using fused data the R² and RMSE improved to 0.843 vs. 0.746 and 0.440 vs. 0.549, respectively. The optimum architecture of the FFBN consisted of one hidden layer with 29 neurons. Therefore, the FFBN algorithm is an effective option for the prediction of rice yield in complex systems of rice production.

The accurate estimation of the key growth indicators of rice is conducive to rice production, and the rapid monitoring of these indicators can be achieved through remote sensing using the commercial RGB cameras of unmanned aerial vehicles (UAVs). However, the method of using UAV RGB images lacks an optimized model to achieve accurate qualifications of rice growth indicators. In this study, we established a correlation between the multi-stage vegetation indices (VIs) extracted from UAV imagery and the leaf dry biomass, leaf area index, and leaf total nitrogen for each growth stage of rice. Then, we used the optimal VI (OVI) method and object-oriented segmentation (OS) method to remove the noncanopy area of the image to improve the estimation accuracy. We selected the OVI and the models with the best correlation for each growth stage to establish a simple estimation model database. The results showed that the OVI and OS methods to remove the noncanopy area can improve the correlation between the key growth indicators and VI of rice. At the tillering stage and early jointing stage, the correlations between leaf dry biomass (LDB) and the Green Leaf Index (GLI) and Red Green Ratio Index (RGRI) were 0.829 and 0.881, respectively; at the early jointing stage and late jointing stage, the coefficient of determination (R²) between the Leaf Area Index (LAI) and Modified Green Red Vegetation Index (MGRVI) was 0.803 and 0.875, respectively; at the early stage and the filling stage, the correlations between the leaf total nitrogen (LTN) and UAV vegetation index and the Excess Red Vegetation Index (ExR) were 0.861 and 0.931, respectively. By using the simple estimation model database established using the UAV-based VI and the measured indicators at different growth stages, the rice growth indicators can be estimated for each stage. The proposed estimation model database for monitoring rice at the different growth stages is helpful for improving the estimation accuracy of the key rice growth indicators and accurately managing rice production.

Rice (Oryza sativa L.) is a staple food crop for over half of the world’s population, with China being the largest producer. However, the growth rate of rice yield per hectare has slowed in recent years, emphasizing the need for in-depth studies on the evolution of rice cultivar performance. This study presents a comprehensive analysis of the yield and key agronomic traits of rice cultivars across China over three decades, utilizing data from 11,811 cultivar trials conducted between 1990 and 2023. We assessed the spatial distribution and temporal evolution of rice cultivar performance, exploring regional differences and the interplay between agronomic traits and environmental factors. Our results reveal significant variations in growth duration, plant height, grains per panicle, thousand-grain weight, effective panicle number, and seed setting rate across different regions. Temporal trends showed diverse patterns of improvement, with some regions experiencing rapid advancements (up to 1.42% annual yield increase in Jiangxi Province of Central China) and others nearing yield plateaus (0.16% in Jilin Province and 0.45% in Heilongjiang Province of Northeast China). Correlation analysis between agronomic traits and grain yield highlighted the complex relationships and potential for further genetic gains through targeted breeding. This study underscores the importance of region-specific breeding strategies to optimize rice production in the face of environmental challenges and yield ceilings. The insights gained provide a scientific basis for future rice cultivar development and regional agricultural policies aimed at enhancing sustainability and efficiency in China’s diverse rice-growing regions.

Accurate nitrogen diagnosis is essential for optimizing rice yield and sustainability. This study investigates the potential of using multi-leaf SPAD measurements combined with machine learning models to improve nitrogen nutrition diagnostics in rice. Conducted across five locations with 15 rice cultivars, SPAD values from the first to fifth fully expanded leaves were collected at key growth stages. The study demonstrates that integrating multi-leaf SPAD data with advanced machine learning models, particularly Random Forest and Extreme Gradient Boosting, significantly improves the accuracy of Leaf Nitrogen Concentration (LNC) and Nitrogen Nutrition Index (NNI) estimation. The second fully expanded Leaf From the Top (2LFT) emerged as the most critical variable for predicting LNC, while the 3LFT was pivotal for NNI estimation. The inclusion of statistical metrics, such as maximum and median SPAD values, further enhanced model performance, underscoring the importance of considering both original SPAD measurements and derived indices. This approach provides a more precise method for nitrogen assessment, facilitating improved nitrogen use efficiency and contributing to sustainable agricultural practices through targeted and effective nitrogen management strategies in rice cultivation.

Maize is a staple crop in China, playing a crucial role in agriculture and food security. However, current planting densities are suboptimal, leading to lower yields and unrealized potential. This study explores the potential to maximize maize yields by optimizing planting density and implementing region-specific agronomic measures across China’s diverse agro-ecological zones. We compiled a dataset consisting of 1974 independent field trials from 720 publications across China’s main maize-growing areas, spanning the period from 2000 to 2023, to assess the impact of optimal planting density and agronomic practices on China’s maize production. Our findings reveal that increasing the planting density to optimal levels—49.34% higher than current farmer practices—can significantly boost national maize yields by 16.28%. Furthermore, adopting agronomic techniques like precision irrigation, soil tillage, and plant growth regulators enhances this effect, raising planting density by 69.91% and yield by 27.26%. Notably, the irrigated maize-growing areas in Northwest China showed the highest yield potential, whereas the southern hilly regions had the lowest. This underscores the significance of tailoring optimal density and agronomic practices to each region. Combining agronomic measures with adjusted planting densities can reduce this disparity. Precision irrigation, soil tillage, and plant growth regulators were particularly effective in optimizing planting density and maximizing yield potential, especially in Northwest China and the North China Plain. In contrast, plant growth regulators proved most effective in Southwest China and Southern China. This study underscores the potential of integrating optimized planting density with agronomic measures to significantly improve maize productivity, thereby supporting sustainable agriculture. It provides a scientific basis for regionalized agricultural management.