Search Results (715)

Maize is one of the top three crops globally, ranking only behind rice and wheat, making it an important crop of interest. Aboveground biomass is a key indicator for assessing maize growth and its yield potential. This study developed an efficient and stable biomass prediction model to estimate the aboveground biomass (AGB) of spring maize (Zea mays L.) under subsurface drip irrigation in arid regions, based on UAV multispectral remote sensing and machine learning techniques. Focusing on typical subsurface drip-irrigated spring maize in arid Xinjiang, multispectral images and field-measured AGB data were collected from 96 sample points (selected via stratified random sampling across 24 plots) over four key phenological stages in 2024 and 2025. Sixteen vegetation indices were calculated and 40 texture features were extracted using the gray-level co-occurrence matrix method, while an integrated feature-selection strategy combining Elastic Net and Random Forest was employed to effectively screen key predictor variables. Based on the selected features, six machine learning models were constructed, including Elastic Net Regression (ENR), Gradient Boosting Decision Trees (GBDT), Gaussian Process Regression (GPR), Partial Least Squares Regression (PLSR), Random Forest (RF), and Extreme Gradient Boosting (XGB). Results showed that the fused feature set comprised four vegetation indices (GRDVI, RERVI, GRVI, NDVI) and five texture features (R_Corr, NIR_Mean, NIR_Vari, B_Mean, B_Corr), thereby retaining red-edge and visible-light texture information highly sensitive to AGB. The GPR model based on the fused features exhibited the best performance (test set R² = 0.852, RMSE = 2890.74 kg ha⁻¹, MAE = 1676.70 kg ha⁻¹), demonstrating high fitting accuracy and stable predictive ability across both the training and test sets. Spatial inversions over the two growing seasons of 2024 and 2025, derived from the fused-feature GPR optimal model at four key phenological stages, revealed pronounced spatiotemporal heterogeneity and stage-dependent dynamics of spring maize AGB: the biomass accumulates rapidly from jointing to grain filling, slows thereafter, and peaks at maturity. At a constant planting density, AGB increased markedly with nitrogen inputs from N0 to N3 (420 kg N ha⁻¹), with the high-nitrogen N3 treatment producing the greatest biomass; this successfully captured the regulatory effect of the nitrogen gradient on maize growth, provided reliable data for variable-rate fertilization, and is highly relevant for optimizing water–fertilizer coordination in subsurface drip irrigation systems. Future research may extend this integrated feature selection and modeling framework to monitor the growth and estimate the yield of other crops, such as rice and cotton, thereby validating its generalizability and robustness in diverse agricultural scenarios. Full article

In the field of precision agriculture, accurately detecting rice panicles is crucial for monitoring rice growth and managing rice production. To address the challenges posed by complex field backgrounds, including variety differences, variations across growth stages, background interference, and occlusion due to dense distribution, this study develops an improved YOLO11-based rice panicle detection model, termed DRPU-YOLO11. The model incorporates a task-oriented CSP-PGMA module in the backbone to enhance multi-scale feature extraction and provide richer representations for downstream detection. In the neck network, DySample and CGDown are adopted to strengthen global contextual feature aggregation and suppress background interference for small targets. Furthermore, fine-grained P2 level information is integrated with higher-level features through a cross-scale fusion module (CSP-ONMK) to improve detection robustness in dense and occluded scenes. In addition, the PowerTAL strategy adapts quality-aware label assignment to emphasize high-quality predictions during training. The experimental results based on a self-constructed dataset demonstrate that DRPU-YOLO11 significantly outperforms baseline models in rice panicle detection under complex field environments, achieving an accuracy of 82.5%. Compared with the baseline model YOLO11 and RT-DETR, the mAP₅₀ increases by 2.4% and 5.0%, respectively. These results indicate that the proposed task-driven design provides a practical and high-precision solution for rice panicle detection, with potential applications in rice growth monitoring and yield estimation. Full article

Advances in plant nutrition have driven substantial progress in modern fertilization technologies. Nevertheless, excessive chemical fertilizer application, low nutrient-use efficiency, and the resulting environmental pollution remain widespread. We have reviewed the research progress and existing limitations in the field of plant nutrition and fertilization technology. Based on the traditional plant nutrition diagnosis and integrating visual diagnosis methods, this study explores the intrinsic relationship between plant growth status, nutrient supply conditions, and crop yield and proposed the concept of “status nutrition”. Variations in environmental nutrient conditions lead plants to exhibit distinct growth status in terms of vigor and phenotype. We define the plant nutritional status reflected by this growth status as “status nutrition”. Based on growth characteristics, plant growth status can be classified as weak, normal, or vigorous, corresponding to deficient, appropriate, and excessive environmental nutrient supply, respectively. Guided by this concept, an innovative rice “extremely dense planting” technology is integrated by increasing planting density, eliminating tiller-stage fertilization, and optimizing nitrogen management. The technology adapts to growth status with low nutrient demand, coordinates population growth and main-stem panicle formation, and achieves high yield with reduced fertilizer inputs. Further research is needed on the nutrient metabolism mechanisms of plants under different growth statuses and the growth status grading system. The promotion of “extremely dense planting” is constrained by crop variety traits and soil fertility, and its parameters urgently need to be optimized. Overall, the framework of “status nutrition” provides important theoretical support for the development and application of crop high-yield cultivation technologies. Full article

The worldwide population is anticipated to reach 10.12 billion by the year 2100, thereby amplifying the necessity for sustainable agricultural methodologies to secure food availability while reducing ecological consequences. Conventional synthetic pesticides, while capable of increasing crop yields by as much as 50%, present considerable hazards such as toxicity, the emergence of resistance, and environmental pollution. This review examines biopesticides, originating from microbial (e.g., Bacillus thuringiensis, Trichoderma spp.), plant, or animal sources, as environmentally sustainable alternatives which address pest control through mechanisms including antibiosis, hyperparasitism, and competition. Biopesticides provide advantages such as biodegradability, minimal toxicity to non-target organisms, and a lower likelihood of resistance development. The global market for biopesticides is projected to be valued between USD 8 and 10 billion by 2025, accounting for 3–4% of the overall pesticide sector, and is expected to grow at a compound annual growth rate (CAGR) of 12–16%. To mitigate production costs, agro-industrial byproducts such as rice husk and starch wastewater can be utilized as economical substrates in both solid-state and submerged fermentation processes, which may lead to a reduction in expenses ranging from 35% to 59%. Strategies for process intensification, such as the implementation of intensified bioreactors, continuous cultivation methods, and artificial intelligence (AI)-driven monitoring systems, significantly improve the upstream stages (including strain development and fermentation), downstream processes (such as purification and drying), and formulation phases. These advancements result in enhanced productivity, reduced energy consumption, and greater product stability. Patent activity, exemplified by 2371 documents from 1982 to 2021, highlights advancements in formulations and microbial strains. The integration of circular economy principles in biopesticide production through process intensification enhances the safety, quality, and sustainability of food systems. Projections suggest that by the 2040s to 2050s, biopesticides may achieve market parity with synthetic alternatives. Obstacles encompass the alignment of regulations and the ability to scale in order to completely achieve these benefits. Full article

Timely and accurate prediction of crop traits is critical for precision breeding and regional agricultural production. Previous studies have primarily focused on single crop yield traits, neglecting other crop traits and variety-specific analyses. To address this issue, we employed a Meta-Hybrid Regression Ensemble (MHRE) approach by using multiple machine learning (ML) approaches as base learners, integrating regional multi-year, multi-variety crop field trials with satellite remote sensing indices, meteorological and phenological data to predict major crop traits. Results demonstrated MHRE’s optimal performance for rice and cotton, significantly outperforming individual models (RF, XGBoost, CatBoost, and LightGBM). Specifically, for rice crop, MHRE achieved highest accuracy for yield trait (R² = 0.78, RMSE = 0.59 t ha⁻¹) compared to the best individual model (XGBoost: R² = 0.76, RMSE = 0.61 t ha⁻¹); traits like effective spike also showed strong predictability (R² = 0.64, RMSE = 27.81 10,000·spike ha⁻¹). Similarly, for cotton, MHRE substantially improved yield trait prediction (R² = 0.82, RMSE = 0.33 t ha⁻¹) compared to the best individual model (RF: R² = 0.77, RMSE = 0.36 t ha⁻¹); bolls per plant accuracy was highest (R² = 0.93, RMSE = 2.27 bolls plant⁻¹). Moreover, rigorous validation confirmed that crop-specific MHRE models are robust across five rice and three cotton varietal groups and are applicable across six distinct regions in China. Furthermore, we applied the SHAP (SHapley Additive exPlanations) method to analyze the growth stages and key environmental factors affecting major traits. Our study illustrates a practical framework for regional-scale crop traits prediction by fusing multi-source data and ensemble machine learning, offering new insights for precision agriculture and crop management. Full article

Silicon (Si) application is recognized for its beneficial roles in crop growth. This study examines the effects of two forms: zeolite and sodium metasilicate (SMS), on rice under hydroponic (EP I) and soil (EP II) conditions. Four treatments were used at the early stage of rice: 4 ppm and 2 ppm of Si from zeolite, 4 ppm of Si from SMS, and a control. In EP I, only 4 ppm of SMS significantly improved root traits: total root length (36%), surface area (34%), root volume (23%), tips (46%), and forks (34%) by day seven compared to the control. Zeolite-based Si had minimal effects, except on the average diameter. However, in EP II, all Si forms enhanced root traits: total root length (50–73%), surface area (51–58%), average diameter (32–50%), root volume (54–72%), tips (29–68%) and increased shoot and root dry weights by 19–24% and 79–106%, respectively, compared to the control. In EP II, starting from the first and fifth day of treatment, the Si applied groups showed a significant increase in photosynthetic traits and vegetative indices, respectively. On the last day of treatment, particularly for 2 ppm of Si zeolite, the electron transport rate increased by 5 times, the apparent transpiration by 3 times, total conductance and stomatal conductance by around 50%, normalized difference vegetative index by 6–8%, and photochemical reflectance index by 14–33%. These results suggest that the effectiveness of Si is highly dependent on the growth medium and the type of Si, with soil enabling better Si availability, uptake, and physiological response compared to hydroponics. The superior performance of zeolite in EP II indicates its potential as a slow-release Si source that enhances root development and photosynthetic efficiency over time. Thus, it is concluded that zeolite has more potential in soil, and soluble silicon sources should be selected in hydroponics. Full article

Efficient and reliable estimation of rice phenological stages is crucial for improving yield prediction, optimizing irrigation, and guiding fertilization management. Spectral indices (SIs) derived from remote sensing have demonstrated strong potential for phenology detection. However, the suitability of specific spectral indices (SIs) for individual growth stages remains unclear due to data limitations. This study addresses this gap using a 7-year (2019–2025) daily in situ hyperspectral dataset that includes shortwave infrared (SWIR) bands. We evaluated various SIs to determine their effectiveness in identifying key phenological stages. The results demonstrate that no single index captures the entire cycle; instead, a multi-index approach is required. The SWIR-based Normalized Difference Vegetation Index (SNDVI) proved superior for detecting irrigation, transplanting, and flowering. The Green–Red Vegetation Index (GRVI) effectively tracked tillering and heading, while the Normalized Difference Vegetation Index (NDVI) and Hue identified the maximum tillering stage. For the ripening phase, the Normalized Difference Yellowness Index (NDYI) exhibited the highest accuracy in detecting maturity. Validation against Sentinel-2 simulations revealed strong correlations ($R^2>0.81$) for greenness-related indices (NDVI, GRVI, SNDVI, EVI), whereas colorimetric indices showed weaker agreement. These findings establish a robust, multi-index framework for high-frequency rice phenology monitoring. Full article

Cold stress impairs crop productivity through cascading inhibition of root growth, nitrogen (N) metabolism, and photosynthesis, yet the systematic linkages among these physiological disruptions remain poorly understood. It is crucial to elucidate the mechanisms by which cold-tolerant varieties maintain root growth and N-metabolizing enzyme homeostasis. This two-year field study investigated how cold duration at the tillering stage impacted root traits, N metabolism, photosynthesis, and their relationships with the yield of two japonica rice varieties differing in cold tolerance. A cold-tolerant (Dongnong 428) and a cold-sensitive variety (Songjing 10) were grown in a paddy field for two consecutive growing seasons in 2021 and 2022. Cold water (15 °C) was irrigated for 0 (denoted as D0), 5 (D5), 10 (D10), and 15 days (D15) during the tillering stage. Compared to D0, cold-water treatments significantly reduced root traits and total dry weight of both varieties. Cold stress significantly impaired N metabolism and photosynthesis, leading to significant reductions in N efficiency. The magnitude of these changes turned to greater with cold-water treatment duration. Dongnong 428 showed stronger cold tolerance, attributed to its maintenance of superior root traits and photosynthetic performance, as well as higher activities of enzymes in the roots, which sustained N assimilation and utilization. These factors primarily contributed to Dongnong 428 achieving 11.6–20.9% higher yields compared to Songjing 10. Cold stress during the tillering stage disrupts root growth and photosynthesis, impairs plant N acquisition ability, resulting in substantial yield loss. Cold-tolerant varieties maintain superior root morphology/functionality and photosynthetic performance. Full article

Seed germination and early growth in rice are critical stages influenced by the hormonal balance between gibberellins (GA) and abscisic acid (ABA), particularly under low-temperature stress. This study investigated the effects of GA₃ and ABA on seed germination, embryonic growth, gene expression, and biochemical activities in rice cultivars with contrasting tolerance to low temperatures. GA₃ markedly improved germination in resistant cultivars Nagdong and CNDH77, whereas susceptible cultivars showed minimal improvement, while ABA strongly inhibited germination, especially under higher concentrations. GA₃ also promoted embryonic growth, with resistant cultivars displaying the longest embryo cells (10.10 µm and 13.49 µm, respectively), whereas ABA suppressed embryonic growth and completely inhibited germination in susceptible cultivars. Upregulation of GA biosynthesis (OsCPS1 and OsKS1) and signaling genes (OsGID1 and OsGID2) in resistant cultivars correlated with enhanced germination and growth, whereas ABA-induced ABI5 expression suppressed germination, particularly in susceptible cultivars. Hormone quantification confirmed increased endogenous GA₃ after GA₃ treatment and reduced ABA levels under ABA treatment. Additionally, GA₃ modulated ABA signaling genes, upregulating OSK3, ABI3, ABI4, and ABI5, while ABA treatment had contrasting effects, particularly between resistant and susceptible cultivars. GA₃ treatment also enhanced the expression of GA biosynthesis and signaling genes (OsCPS1, OsKS1, OsGID1, and OsGID2), whereas ABA treatment upregulated ABA catabolic genes (OsABA8ox2). GA₃ also enhanced amylase activity and sugar-related gene expression, supporting its role in energy mobilization during germination. Conversely, ABA suppressed cell elongation, reducing it to 4.45 µm in CNDH77 under 100 µM ABA. These findings provide valuable insights into the hormonal regulation of rice seed germination and growth under low-temperature stress, offering potential strategies to enhance seed vigor and stress tolerance in rice breeding. Full article

Agricultural soils surrounding mining areas are often polluted with heavy metals (HMs) due to long-term mining activities and high geological background values. In this study, we investigated the distribution and transport of Cu, Cr, Zn, Cd, Pb, and As in a soil–rice system near a century-old mining site, evaluated their toxic effects on rice (Oryza sativa L.) throughout the growth period, and assessed the associated health risks using the Nemerow index and potential ecological risk index. The results showed that HM contents in rice grown in contaminated soils were significantly higher than in the control. HMs mainly accumulated in roots, with the lowest contents in grains. Cd exhibited the highest enrichment capacity, with bioconcentration factors of 0.79, 1.04, and 1.95 at the tillering, heading, and maturity stages, respectively, and its accumulation increased with rice growth. Transport from stems to leaves was relatively strong. HM exposure significantly inhibited rice growth, reducing plant height, biomass, tiller number, and panicle emergence. In addition, oxidative stress indicators and antioxidant enzyme activities, as well as root amino acid exudation, were markedly altered under HM stress. According to soil–rice HM contents, the pollution level of agricultural soils reached a high class, with As, Pb, Cd, and Zn as the main contributors. The potential ecological risk reached a moderate level, with Cd identified as the dominant factor. Notably, the health risks to children were substantially higher than those to adults, and Monte Carlo simulation indicated a 100% probability of non-carcinogenic and carcinogenic risks for adults and children. The above results highlighting the urgent need for risk management in mining-affected regions. Full article

Assessing Leaf Nitrogen Content (LNC) is critical for evaluating crop nutritional status and monitoring growth. While Unmanned Aerial Vehicle (UAV) remote sensing has become a pivotal tool for nitrogen monitoring at the field scale, current research predominantly relies on uni-modal feature variables. Consequently, the integration of multidimensional feature information for nitrogen assessment remains largely underutilized in existing literature. In this study, the four types of feature variables (two kinds of spectral indices, color space parameters and texture features from UAV images of RGB and multispectral sensors) were extracted from three dimensions, and crop nitrogen-sensitive feature variables were selected by GCA (Gray Correlation Analysis), followed by one fused deep neural network (DNN-F2) for remote sensing monitoring of rice nitrogen and a comparative analysis with five common machine learning algorithms (RF, GPR, PLSR, SVM and ANN). Experimental results indicate that the DNN-F2 model consistently outperformed conventional machine learning algorithms across all three growth stages. Notably, the model achieved an average R² improvement of 40%, peaking at the rice jointing stage with R² of 0.72, RMSE of 0.08, and NRMSE of 0.019. The study shows that the fusion of multidimensional feature information from UAVs combined with deep learning algorithms has great potential for nitrogen nutrient monitoring in rice crops, and can also provide technical support to guide decisions on fertilizer application in rice fields. Full article

Ratoon rice cultivation is an efficient production system that achieves a second harvest from the stubble of the main crop, but its yield potential is largely constrained by variation in axillary bud regeneration capacity. Here, we identify OsPM19L, a plasma membrane–localized AWPM-19 domain protein, as a key regulator of rice ratooning ability. Transcriptome analysis revealed higher OsPM19L expression in strong-regeneration cultivars, followed by a sharp decline after harvest. Promoter assays and hormonal treatments demonstrated that OsPM19L is strongly induced by ABA and functions as a positive regulator in ABA signaling. Under field conditions, ospm19l mutants exhibited increased tiller number but reduced ratooning index, whereas OsPM19L-OE plants showed the opposite pattern, indicating stage-specific regulation of tillering and regeneration. Hormone profiling and gene expression analyses showed that OsPM19L is associated with altered levels of multiple phytohormones in regenerating axillary buds, showing higher CK and GA levels and lower IAA and ABA levels in OsPM19L-OE compared with the wild type. Consequently, OsPM19L appears to facilitate dormancy release and enhance early axillary bud growth during the ratoon season. These findings indicate OsPM19L may act as a central regulator linking ABA signaling with hormonal cross-talk, providing new insights into the molecular control of regeneration and potential targets for improving ratoon rice productivity. Full article

In the context of global warming, agricultural drought risks are exacerbated by increasing atmospheric aridity. This study pioneers the application of the Bayesian Estimator of Abrupt Change, Seasonality, and Trend (BEAST) algorithm at a provincial scale to detect change points in vapor pressure deficit (VPD), leveraging high-density meteorological station data from Hunan Province to delineate the nuanced evolution of VPD and its implications for early drought warning. Key findings reveal the following: (1) The VPD in Hunan exhibits a spatial pattern of “higher in the south than north, higher in the east than west” and a seasonal variation of “summer > autumn > spring > winter”. (2) BEAST identified abrupt changes in VPD coinciding with critical phenological periods, such as the early rice transplanting period in early April, with spatial and temporal gradient differences (up to 25 days) that can guide irrigation resource scheduling; moreover, the months of change points have been consistently advancing during the study period. (3) The dominant factors of VPD exhibit regional and seasonal differentiation. Annually, the maximum temperature (contribution rate 57.1–60.6%) is the primary factor. (4) Extreme events with VPD > 1.5 kPa for three consecutive days covered 92 stations in 2022. Combining this with the critical growth periods of double-cropping rice, it is recommended to set VPD = 1 kPa as the drought early warning threshold for the northern and southern regions. This study provides a scientific basis for the prevention and control of agricultural drought by integrating climate diagnostics and crop physiological needs. Full article

Prolonged low-light stress during growth significantly reduces rice yield in southwest China. In order to systematically study the dynamic response of rice to long-term shading, field experiments were conducted in Chongqing, China, from 2021 to 2022, investigating the effects of 50% and 75% shading from the seedling to heading stage on morphological characteristics, physiological traits, and yield formation in 12 rice cultivars. The results showed that shading reduced tiller number, leaf mass per area, total dry mass, leaf area index, panicle number, seed-setting rate, and yield. Meanwhile, rice acclimated to low light by increasing plant height, leaf chlorophyll content, and leaf-total mass ratio. In particular, leaf width in low-light treatments was narrower under short-term shading but became wider under long-term shading compared to natural light. Moreover, under 50% shading condition, rice optimized panicle structure by increasing grain number per panicle and primary and secondary branch numbers to compensate for adverse effects. Cultivars, including Le you 918 and Shen 9 you 28, exhibited high yield and strong shade tolerance. Overall, rice acclimates to low light through the synergistic interactions of various traits, with leaf phenotypic adjustments and panicle structure optimization being crucial for improving yield under low light. Full article

Late sowing has been common since the popularization of mechanical transplantation, and it has led to reduced grain yield due to low grain weight. However, the uneven contribution of the amount and weight of grains in different positions to grain yield remains unclear. A 2 yr field experiment was conducted with two super rice varieties (Ningjing 7th: N7 and Nanjing 9108th: N9108) and three sowing dates (S1: 30 April; S2: 30 May; and S3: 30 June) in Hefei City. In this study, delaying sowing shortened the growth duration, reduced temperature, and further reduced the grain yield mainly by suppressing the total grain weight. Compared with S1, the grain weight of grains in secondary branches (SGs) in S2 and S3 was reduced by 11.1% ± 2.9% and 18.5% ± 1.4%, respectively, due to a lower reproductive-stage temperature. However, no significant difference was found in the grains in primary branches (PGs). Moreover, a shorter growth duration enhanced the ratio of tillers in a lower position (LT) per unit area, which contained more SGs per panicle, and finally led to a higher ratio of SG amount per unit area (SG%). The SG% increased by 13.4% ± 1.4% and 21.1% ± 1.9% in S2 and S3 compared with S1, respectively. In conclusion, delaying the sowing date mainly reduced the grain weight of SGs and enhanced the SG%, leading to a lower apparent grain weight and further decreasing grain yield. Full article