Efficiently obtaining leaf nitrogen content (LNC) in rice to monitor the nutritional health status is crucial in achieving precision fertilization on demand. Unmanned aerial vehicle (UAV)-based hyperspectral technology is an important tool for determining LNC. However, the intricate coupling between spectral information and nitrogen remains elusive. To address this, this study proposed an estimation method for LNC that integrates hybrid preferred features with deep learning modeling algorithms based on UAV hyperspectral imagery. The proposed approach leverages XGBoost, Pearson correlation coefficient (PCC), and a synergistic combination of both to identify the characteristic variables for LNC estimation. We then construct estimation models of LNC using statistical regression methods (partial least-squares regression (PLSR)) and machine learning algorithms (random forest (RF); deep neural networks (DNN)). The optimal model is utilized to map the spatial distribution of LNC at the field scale. The study was conducted at the National Agricultural Science and Technology Park, Guangzhou, located in Baiyun District of Guangdong, China. The results reveal that the combined PCC-XGBoost algorithm significantly enhances the accuracy of rice nitrogen inversion compared to the standalone screening approach. Notably, the model built with the DNN algorithm exhibits the highest predictive performance and demonstrates great potential in mapping the spatial distribution of LNC. This indicates the potential role of the proposed model in precision fertilization and the enhancement of nitrogen utilization efficiency in rice cultivation. The outcomes of this study offer a valuable reference for enhancing agricultural practices and sustainable crop management.

Soil and plant analyzer development (SPAD) value and leaf nitrogen concentration (LNC) based on dry weight are important indicators affecting rice yield and quality. However, there are few reports on the use of machine learning algorithms based on hyperspectral monitoring to synchronously predict SPAD value and LNC of indica rice. Meixiangzhan No. 2, a high-quality indica rice, was grown at different nitrogen rates. A hyperspectral device with an integrated handheld leaf clip-on leaf spectrometer and an internal quartz-halogen light source was conducted to monitor the spectral reflectance of leaves at different growth stages. Linear regression (LR), random forest (RF), support vector regression (SVR), and gradient boosting regression tree (GBRT) were employed to construct models. Results indicated that the sensitive bands for SPAD value and LNC were displayed to be at 350–730 nm and 486–727 nm, respectively. Normalized difference spectral indices NDSI (R497, R654) and NDSI (R729, R730) had the strongest correlation with leaf SPAD value (R = 0.97) and LNC (R = −0.90). Models constructed via RF and GBRT were markedly superior to those built via LR and SVR. For prediction of leaf SPAD value and LNC, the model constructed with the RF algorithm based on whole growth periods of spectral reflectance performed the best, with R² values of 0.99 and 0.98 and NRMSE values of 2.99% and 4.61%. The R² values of 0.98 and 0.83 and the NRMSE values of 4.88% and 12.16% for the validation of leaf SPAD value and LNC were obtained, respectively. Results indicate that there are significant spectral differences associated with SPAD value and LNC. The model built with RF had the highest accuracy and stability. Findings can provide a scientific basis for non-destructive real-time monitoring of leaf color and precise fertilization management of indica rice.

Flooding is a critical factor that limits the establishment of a symbiosis between rice and arbuscular mycorrhizal fungi (AMF) in wetland ecosystems. The distribution of carbon resources in roots and the acclimation strategies of rice to flooding stress in the presence of AMF are poorly understood. We conducted a root box experiment, employing nylon sheets or nylon meshes to create separate fungal chambers that either prevented or allowed the roots and any molecules to pass through. We found that the mycorrhizal colonization rate and the expression of genes OsD14L and OsCERK1, which are involved in fungal perception during symbiosis, both increased in mycorrhizal rice roots following intermittent flooding compared to continuous flooding. Furthermore, AMF inoculation affected root morphological traits, facilitating both shallower and deeper soil exploration. Increased submergence intensity led to carbohydrate deprivation in roots, while high mycorrhizal colonization increased soil oxygen consumption and decreased the neutral lipid concentration in roots. However, mycorrhizal inoculation increased the rice photosynthesis rate and facilitated acclimation to submergence by mediating the expression of the genes OsCIPK15 and OsSUB1A to enhance rice shoot elongation and the sugar concentration in roots as a result of reduced competition for carbon between rice and AMF under different flooding conditions.

The continuous-cropping obstacles of Pogostemon cablin (patchouli) is severely constrained by autotoxic phenolic acids accumulated in the rhizosphere soil. Biochar adsorption and chemical oxidation are common remediation strategies; they often fail to simultaneously and efficiently remove phenolic allelochemicals while improving the soil micro-ecological environment. To address this issue, this study developed a novel biochar–urea peroxide composite particle (BC-UP). Batch degradation experiments and electron paramagnetic resonance (EPR) analysis confirmed the synergistic adsorption-oxidation function of BC-UP. A pot experiment demonstrated that application of BC-UP (5.0 g/kg) significantly alleviated phenolic acid stress. Specifically, BC-UP application significantly enhanced shoot biomass by 28.8% and root surface area by 49.3% compared to the phenolic acid-stressed treatment and concurrently reduced the total phenolic acid content in the rhizosphere soil by 37.3%. This growth promotion was accompanied by the enhanced accumulation of key bioactive compounds (volatile oils, pogostone, and patchouli alcohol). BC-UP amendment also improved key soil physicochemical properties (e.g., pH, and organic matter) and enhanced the activities of critical enzymes. Furthermore, BC-UP reshaped the microbial community, notably reducing the fungi-to-bacteria OTU ratio by 49.7% and enriching the relative abundance of Firmicutes and Nitrospirota but suppressing the Ascomycota phylum abundance. Redundancy analysis identified soil sucrase and catalase activity, total phenolic acid content, and Ascomycota abundance as key factors influencing patchouli biomass. In conclusion, BC-UP effectively mitigates phenolic acid stress through combined adsorption and radical oxidation, subsequently improving soil properties and restructuring the rhizosphere microbiome, offering a promising soil remediation strategy for patchouli and other medicinal crops.