Coordinating light and nitrogen (N) distribution within a canopy is crucial to improve rice yield and resource use efficiency.  However, little attention has been paid to light and N distribution in response to planting density and N rate, and its relationships with grain yield, radiation use efficiency (RUE), and N use efficiency for grain production (NUEg) in rice.  Here, a two-year field experiment was conducted with two hybrid varieties under three N levels, 0 (N1), 90 (N2) and 180 kg ha^-1 (N3), and two planting densities, 22.2 (D1) and 33.3 hills m^-2 (D2).  On average, a 3.4% higher yield and 4.4% higher NUEg were observed under N2D2 compared with N3D1.  The extinction coefficient for N (K_N) and light (K_L) and their ratio (K_N/K_L) at the heading stage were significantly affected by the N rate, planting density, and their interaction.  K_N decreased with the increase of N input or planting density.  Compared with N1, K_N decreased by 43.5 and 58.8% under N2 and N3, respectively, and K_N under D2 decreased by 16.0% compared with D1.  Higher K_L and K_N/K_L values were observed under a low N rate, while the opposite trend was shown under a high N rate.  Moreover, increasing planting density resulted in a decrease in K_L and K_N/K_L values.  Compared with N3D1, N2D2 had higher K_L and K_N, and thus comparable K_N/K_L.  Correlation analysis further revealed that K_L was negatively correlated with RUE, while K_N and K_N/K_L were positively correlated with NUEg.  Therefore, increasing planting density under reduced N input could ensure rice yield while improving resource use efficiency via regulating canopy light and N distribution. 

The breakthrough in super hybrid rice yield has significantly contributed to China's and global food security.  However, the inherent conflict between high productivity and environmentally sustainable agriculture poses challenges. Issues like water scarcity, energy crises, escalating greenhouse gas emissions, and diminishing farm profitability all threaten agricultural sustainability.  In response to these challenges, we applied a holistic food-carbon-nitrogen-water-energy-profit (FCNWEP) nexus framework to comprehensively evaluate sustainability of distinct crop management strategies across three sub-sites in central China.  Field experiments were conducted in Hubei and Hunan Provinces from 2017 to 2021, with a widely adopted elite super hybrid rice (Y-liangyou900).  Four crop management treatments were implemented: a control (CK, 0 kg N ha^-1), conventional crop management (CCM, 210-250 kg N ha^-1, 7:3 basal: mid-tiller fertilizer ratio), and two integrated crop management treatments (ICM1, 180-210 kg N ha^-1, 5:2:3 basal: mid-tiller: panicle initiation fertilizer ratio; ICM2, 240-270 kg N ha^-1, 5:2:2:1 basal: mid-tiller: panicle initiation: flowering fertilizer ratio).  Grain yield, carbon footprint, nitrogen footprint, energy footprint, nitrogen use efficiency and economic benefits were among the assessed variables.  Our results showed that significant yield variations were observed, with ICM2 consistently outperforming CCM and ICM1 across the three sites.  In Jingzhou, Suizhou, and Changsha, ICM2's grain yield was 30.2, 24.7, and 13.3% higher than CCM, respectively.  Additionally, net profits for ICM2 exceeded those of CCM and ICM1 by 31.8 and 115.2% in Jingzhou, 32.2 and 109.9% in Suizhou, and 15.4 and 34.0% in Changsha.  Integrated crop managements, specifically ICM2, demonstrated improved nitrogen and energy use efficiency, resulting in reduced carbon, nitrogen, water, and energy footprints.  Overall, composite sustainability scores, calculated using the FCNWEP framework, indicated that both ICM2 and ICM1 generally exhibited higher sustainability levels compared to CCM.  This study offers valuable insights into practical management methodologies and provides recommendations for enhancing agricultural sustainability.

Agromyzid leafminers cause significant economic losses in both vegetable and horticultural crops, and precise assessments of pesticide needs must be based on the extent of leaf damage. Traditionally, surveyors estimate the damage by visually comparing the proportion of damaged to intact leaf area, a method that lacks objectivity, precision, and reliable data traceability. To address these issues, an advanced survey system that combines augmented reality (AR) glasses with a camera and an artificial intelligence (AI) algorithm was developed in this study to objectively and accurately assess leafminer damage in the field. By wearing AR glasses equipped with a voice-controlled camera, surveyors can easily flatten damaged leaves by hand and capture images for analysis. This method can provide a precise and reliable diagnosis of leafminer damage levels, which in turn supports the implementation of scientifically grounded and targeted pest management strategies. To calculate the leafminer damage level, the DeepLab-Leafminer model was proposed to precisely segment the leafminer-damaged regions and the intact leaf region. The integration of an edge-aware module and a Canny loss function into the DeepLabv3+ model enhanced the DeepLab-Leafminer model's capability to accurately segment the edges of leafminer-damaged regions, which often exhibit irregular shapes. Compared with state-of-the-art segmentation models, the DeepLab-Leafminer model achieved superior segmentation performance with an Intersection over Union (IoU) of 81.23% and an F₁ score of 87.92% on leafminer-damaged leaves. The test results revealed a 92.38% diagnosis accuracy of leafminer damage levels based on the DeepLab-Leafminer model. A mobile application and a web platform were developed to assist surveyors in displaying the diagnostic results of leafminer damage levels. This system provides surveyors with an advanced, user-friendly, and accurate tool for assessing agromyzid leafminer damage in agricultural fields using wearable AR glasses and an AI model. This method can also be utilized to automatically diagnose pest and disease damage levels in other crops based on leaf images.