The reduction of nitrogen (N) fertilizer during the rice growing season is an important practice in rice production and is ecologically beneficial. Will different N reduction stages affect rice yields and NUE? The timing of the reduction in N-efficient varieties (NEVs) is yet to be identified, especially under moderate N rate applications. We investigated the effectiveness of various N reduction stages (NRSs) on grain yield and N-use efficiency (NUE) in NEVs in the lower reaches of the Yangtze River in China. Two NEVs were grown in the field, and five N reduction treatments, including basal N reduction (BR) at pre-transplanting (PT), tillering N reduction (TR) at early tillering (ET), promoting-spikelet N reduction (PR) at panicle initiation (PI), keeping-spikelet N reduction (KR) at spikelet differentiation (SD), and N split reduction (SR) at all four stages, were adopted, with no N reduction (CK) and no N application (N0) as controls. The results showed that grain yield and NUE varied substantially with the NRSs. Yield decreases were observed in descending order of magnitude in BR, PR, SR, TR, and KR when compared to CK. For both NEVs, BR and PR were the most effective treatments in decreasing yield and NUE at the same N reduction rate. BR and PR markedly decreased the panicles per unit area or spikelets per panicle, root biomass, root length, root length density, and root oxidation activity and exhibited simultaneously decreased leaf area index, grain leaf ratio, shoot biomass, and crop growth rate from joining to the heading and from heading to maturity. According to the results, PT and PI were considered to be N reduction sensitive stages, and ET and SD were considered to be N reduction insensitive stages. According to the results, an N reduction strategy was suggested as follows: N reduction at SD and ET, with increased N proportions at PT and PI for NEVs when adopting moderate N application rates.

Wheat (Triticum aestivum L.) leaf rust is the most common and widely distributed wheat disease. Non‐destructive and real‐time methods for monitoring wheat leaf rust can help prevent and control plant diseases in agricultural production. In this study, we obtained multispectral imagery of the wheat canopy acquired by an unmanned aerial vehicle, selected the vegetation index using the K‐means algorithm (KA) and genetic algorithm (GA), and established a wheat leaf rust monitoring model based on the backpropagation neural network (BPNN) method. The results showed that the R² and RMSE of the KA‐BPNN model were 0.902% and 5.45% for the modeling set, respectively, and 0.784% and 4.76% for the validation set, respectively; and the R² and RMSE of the GA‐BPNN model was 0.922% and 4.88% for the modeling set, respectively, and 0.780% and 4.28% for the validation set, respectively. The prediction model after optimizing the variables using KA and GA had higher accuracy than the BPNN model, implying that using variable dimensionality reduction methods and complex machine learning algorithms to construct estimation models can improve model accuracy significantly. These models accurately monitored leaf rust in winter wheat, providing a theoretical basis and technical support for assessing plant diseases and screening disease‐resistant wheat varieties.

Wheat ears in unmanned aerial vehicles (UAV) orthophotos are characterized by occlusion, small targets, dense distribution, and complex backgrounds. Rapid identification of wheat ears in UAV orthophotos in a field environment is critical for wheat yield prediction. Three improvements were achieved based on YOLOX-m: mosaic optimized, using BiFPN structure, and attention mechanism, then ablation experiments were performed to verify the effect of each improvement. Three scene datasets were established: images were acquired during three different growing periods, at three planting densities, and under three scenarios of UAV flight heights. In ablation experiments, three improvements had increased recognition accuracies on the experimental dataset. Compared the accuracy of the standard model with our improved model on three scene datasets. Our improved model during three different periods, at three planting densities, and under three scenarios of the UAV flight height, obtaining 88.03%, 87.59%, and 87.93% accuracies, which were, respectively, 2.54%, 1.89%, and 2.15% better than the original model. The results of this study showed that the improved YOLOX-m model can achieve UAV orthophoto wheat recognition under different practical scenarios in large fields, and that the best combination were obtained images from the wheat milk stage, low planting density, and low flight altitude.

Nitrogen (N) management is of great importance in rice production, but most previous studies have focused on high N rates and there is a lack of research on management plans under a moderate N rate. This study aimed to explore the agronomic and physiological traits of N-efficient rice varieties (NEVs) and to optimize the management strategy at an N rate below the inflection point of the parabolic curve between N rate and grain yield. Two NEVs and two N-inefficient rice varieties (NIVs) were planted, and three treatments were designed according to the panicle N application method. A larger amount of N applied at panicle initiation (PI) led to higher rice yield and N-use efficiency (NUE). This was mainly due to increases in the total number of spikelets per unit area, root oxidation activity, leaf area duration, and leaf photosynthesis rate as well as to the increased carbon (C) and N utilization rates. Compared with NIVs, NEVs exhibited improved root and shoot functions and higher C and N transport characteristics at the moderate N rate. We suggest that increasing the application of N at PI and that planting of NEVs are important ways to increase rice yield and NUE when adopting moderate N rates.