Search Results (2,440)

The study investigates the use of silver nanoparticles (AgNPs) in agriculture, focusing on their potential to enhance the immune response of potato (Solanum tuberosum L.) plants against phytopathogenic attacks. The research highlights how AgNPs, stabilized by biologically active polymers polyhexamethylene biguanide and tallow amphopolycarboxyglycinate, can induce oxidative stress. Triple foliar application of 0.1–9.0 g/ha silver nanoparticles at the budding and later stages demonstrated significant efficacy in suppressing diseases caused by Phytophthora infestans and Alternaria solani (over 60%). This effect was linked to the increased activity of peroxidase—over 30–50%—and the decreased catalase activity, indicative of a well-coordinated oxidative stress response to the invasion of P. infestans and A. solani. The results suggest that AgNPs in low concentrations can prime the plant’s innate immune system, enhancing its resistance without detrimental effects on growth parameters, thus contributing to the improved crop yield. These findings underscore the potential of AgNPs not as traditional biocides, but as intelligent elicitors of plant-induced resistance, positioning them as next-generation tools for sustainable crop protection and yield optimization, which can be applied at extremely low doses (less than 10 g/ha of active substance). Full article

The cotton boll opening rate (BOR) is an important indicator for evaluating the physiological maturation process of cotton and the critical stage of yield formation, and it provides essential guidance for subsequent defoliant application and mechanical harvesting. The investigation of cotton BOR usually relies on manual field surveys, which are time-consuming and destructive, making it difficult to achieve large-scale and efficient monitoring. UAV remote sensing technology has been widely used in crop growth monitoring due to its operational flexibility and high image resolution. However, because of the dense growth of the cotton canopy in UAV remote sensing imagery, the boll opening condition in the lower parts of the canopy cannot be completely observed. In contrast, UAV imagery can effectively monitor cotton leaf chlorophyll content (SPAD) and leaf area index (LAI), both of which undergo continuous changes during the boll opening process. Therefore, this study proposes using SPAD and LAI retrieved from UAV multispectral imagery as physiological intermediary variables to construct an empirical statistical equation and compare it with end-to-end machine learning baselines. Multispectral and ground synchronous data (n = 360) were collected in Baibi Town, Anyang, Henan Province, across four dates (8/28, 9/6, 9/13, 9/24). Twenty-eight commonly used vegetation indices were calculated from multispectral imagery, and Pearson’s correlation analysis was conducted to select indices sensitive to cotton SPAD, LAI, and BOR. Prediction models were constructed using the Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Support Vector Machine (SVM), and Partial Least Squares (PLS) models. The results showed that GBDT achieved the best prediction performance for SPAD (R² = 0.86, RMSE = 1.19), while SVM performed best for LAI (R² = 0.77, RMSE = 0.38). The quadratic polynomial equation constructed using SPAD and LAI achieved R² = 0.807 and RMSE = 0.109 in BOR testing, which was significantly better than the baseline model using vegetation indices to directly regress BOR. The method demonstrated stable performance in spatial mapping of BOR during the boll opening period and showed promising potential for guiding defoliant application and harvest timing. 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

Chilli (Capsicum annuum) is a popular spice and vegetable crop of significant economic importance that is cultivated worldwide in warm and humid climatic zones. Although chilli is a thermophilic crop, its quality and yield potential are significantly affected due to various abiotic factors, including extremely fluctuating temperatures beyond the optimum temperatures (18–30 °C). Global warming and anthropogenic activities lead to adverse climatic changes, imposing severe stress on growth, development, and productivity. High temperatures above 43–45 °C adversely affect chilli crops, especially during the reproductive stages, by causing immature fruit dropping, poor seed vigour, reduced number of flowers, flower abscission, aborted reproductive organs, reduced fruit set, and significant yield loss by 50%. Therefore, to reduce quantitative and qualitative losses, heat management is necessary from April to June in Pakistan, when the temperature rises beyond 40 °C. For heat management, the hybridisation of heat-resilient and high-yielding genotypes to develop heat-tolerant high-yielding hybrids appears to be a rational approach. These genetically improved hybrids inherit such characteristics that assist in maintaining vigorous growth, fruit quality, and stable yield without significant yield losses even under heat-stressed conditions. Hence, the thermotolerant chilli hybrids developed through hybridisation help to satisfy the escalating demand for chilli and guarantee the financial stability of farmers. 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

Cotton (Gossypium hirsutum L.) is a primary global commercial crop, and accurate monitoring of its growth and yield prediction are essential for optimizing water management. This study integrates leaf area index (LAI) data derived from unmanned aerial system (UAS) imagery into the Decision Support System for Agrotechnology Transfer (DSSAT) model to improve cotton growth simulation and yield estimation. The results show that the normalized difference vegetation index (NDVI) exhibited higher estimation accuracy for the cotton LAI during the squaring stage (R² = 0.56, p < 0.05), whereas the modified triangle vegetation index (MTVI) and enhanced vegetation index (EVI) demonstrated higher and more stable accuracy in the flowering and boll-setting stages (R² = 0.64 and R² = 0.76, p < 0.05). After assimilating LAI data, the optimized DSSAT model accurately represented canopy development and yield variation under different irrigation levels. Compared with the DSSAT, the assimilated model reduced yield prediction error from 40–52% to 3.6–6.3% under 30%, 60%, and 90% irrigation. These findings demonstrate that integrating UAS-derived LAI data with the DSSAT substantially enhances model accuracy and robustness, providing an effective approach for precision irrigation and sustainable cotton management. Full article

Perilla (Perilla frutescens) is an important oilseed crop valued for its rich content of nutraceutical compounds and polyunsaturated fatty acids. While triacylglycerol biosynthesis has been studied, the role of polar lipids during seed development remains poorly characterized. Here, we performed a comprehensive lipidomic analysis of polar lipids in developing perilla seeds across three key stages. A total of 147 molecular species from 10 polar lipid classes were identified. Phosphatidylcholine and phosphatidylethanolamine were the predominant phospholipids, and both decreased markedly during development, with phosphatidylcholine showing the most significant reduction. In contrast, lysophosphatidic acid increased substantially by 62.4%. Conversely, the galactolipids monolactodiacylglycerol and digalactosyldiacylglycerol showed a decline in perilla during cotyledon development. Additionally, the unsaturation index of most polar lipids decreased during development. These variation characteristics of polar lipids during growth and development may suggest an adaptive strategy for oil accumulation in perilla. 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

With the advancement of modern agriculture and increasing scarcity of water and fertilizer resources, determining optimal water and nitrogen (N) management strategies for intercropping systems is critical for ensuring system productivity and enhancing resource-use efficiency. This study employed field experiments to investigate the effects of different water and N treatments on grain yield, aboveground biomass, leaf area index (LAI), photosynthetic parameters, chlorophyll fluorescence characteristics, and radiation use efficiency (RUE) in a maize–soybean intercropping system. The experiment consisted of three cropping systems (maize monoculture, soybean monoculture, and maize–soybean intercropping), two irrigation regimes (rain-fed and supplemental irrigation), and three N-application rates for maize (240, 180, and 120 kgN ha⁻¹). The results demonstrated that supplementary irrigation significantly enhanced the LAI and photosynthetic capacity of both maize and soybean during critical growth stages, thereby promoting increases in grain yield and aboveground biomass. Intercropping significantly improved the productivity and photosynthetic performance of maize compared to monoculture, whereas soybean exhibited a reduction under intercropping conditions. Furthermore, irrigation regime and N rate had significant interactive effects on the photosynthetic performance of maize at the tasseling stage. In the intercropping system, a 25% reduction in the conventional application rate of N for maize maintained system productivity, whereas a 50% reduction substantially decreased maize yield and photosynthetic performance. The intercropping system achieved land equivalent ratios (LERs) ranging from 1.06 to 1.11 and RUE advantages (ΔRUE) of 1.52 to 1.64, demonstrating significant superiority in land and light resource utilization. Considering both productivity and resource-use efficiency, supplemental irrigation combined with 180 kgN ha⁻¹ N application for maize represents the optimal water and N management strategy for achieving high yield and efficiency in maize–soybean intercropping systems in the Guanzhong plain. Full article

To address the localization and mapping challenges for quadruped robots autonomously scouting under corn canopies, this paper proposes CK-SLAM, a SLAM algorithm integrating robot motion constraints and crop row features. The algorithm is implemented on the Jueying Mini quadruped robot, fusing data from 3D LiDAR, IMU, and joint sensors. First, an Invariant Extended Kalman Filter (InEKF) fuses multi-source motion information, dynamically adjusting observation noise via a foot contact probability model (derived from joint torque data) to achieve initial motion state estimation and reliable pose references for point cloud deskewing. Second, three feature extraction schemes are designed, inheriting line/plane features from LeGO-LOAM and adding an innovative crop plane feature extraction module, which uses grid filtering, differential evolution for crop row detection, and RANSAC plane fitting to capture corn plant structural features. Finally, a two-step Levenberg–Marquardt iteration realizes feature matching and pose optimization, with factor graph optimization fusing motion constraints and laser odometry for global trajectory and map refinement. CK-SLAM effectively adapts to gait-induced measurement noise and enhances feature matching stability under canopies. Experimental validation across four corn growth stages shows it achieves an average Absolute Pose Error (APE) RMSE of 2.0939 m (15.7%/56.4%/72.2% lower than A-LOAM/LeGO-LOAM/Point-LIO) and an average Relative Pose Error (RPE) RMSE of 0.0946 m, providing high-precision navigation support for automated field monitoring. Full article

Fusarium verticillioides (F. verticillioides) is an important fungal pathogen known to infect a variety of economically critical crops, particularly maize, causing substantial yield reductions and economic losses worldwide. In addition to its direct damage to agricultural productivity, F. verticillioides threatens public health by producing/secreting potent compounds, including well-known fumonisins (FUMs), which pose significant health threats to both livestock and humans due to their toxicity and carcinogenicity. However, current knowledge of the materials secreted/produced by F. verticillioides, such as secreted proteins and additional secondary metabolites, remains limited. In the present study, we conducted an integrated secretome analysis of F. verticillioides at the exponential growth stage by using proteomic and metabolomic technologies. The results of the present study showed that proteomic analysis identified 185 proteins, including 138 fungus-specific proteins. GO enrichment of these 138 fungus-specific proteins yielded 24 significant terms spanning carbohydrate/polysaccharide and aminoglycan metabolic/catabolic processes, extracellular and membrane-anchored components, and hydrolase/peptidase activities. Meanwhile, KEGG analysis identified starch and sucrose metabolism as the sole significantly enriched pathway. Metabolomic analysis of medium supernatant showed that a total of 2352 metabolites were identified, with 110 unique to the medium supernatant of the fungal group, including fumonisins (A1, B2, B3, B4), fatty acids, and other bioactive compounds. KEGG pathway enrichment highlighted key metabolic pathways, including the TCA cycle, unsaturated fatty acid biosynthesis, and arachidonic acid metabolism. These findings provide new insights into the pathogenic mechanisms of F. verticillioides, suggesting candidates for virulence-associated functions and metabolic adaptations that potentially contribute to its pathogenicity. Full article

Climate change exacerbates soil moisture deficits, necessitating efficient water retention strategies. Superabsorbent polymers (SAPs) offer a potential solution to enhance water availability for crops during dry periods. Faba bean (Vicia faba L.) and pea (Pisum sativum L.) were selected as model legumes due to their high nutritional value, agricultural importance in temperate regions, and sensitivity to drought stress This study evaluated the effects of different SAP application rates on the yield and physiological performance of two legume species: faba bean (cv. Granit) and pea (cv. Batuta). The two-year (2017–2018) field experiments employed a randomized block design with four replicates. Treatments included three SAP doses: 0 (control, SAP0), 20 (SAP20) and 30 (SAP30) kg·ha⁻¹. The study was conducted over two years with contrasting weather: 2017 was wetter but had uneven rainfall distribution, while 2018 was drier and characterized by moisture deficits during critical growth stages. SAP application significantly increased seed yield in faba bean and pea, with the most favorable effect observed at 20 kg ha (average yield increase of 23.6% and 17.3%, respectively). SAP did not affect yield components in faba bean. However, in peas, an increase in pod number and seed number per plant was observed with the SAP30 dose compared to the SAP20 dose. Application of superabsorbent at a dose of 20 kg ha⁻¹ significantly increased photosynthesis rate in faba bean, the Fv/Fm index in the tested species, and the PI in peas compared to the control. However, the superabsorbent did not affect transpiration rate or the WUE coefficient in the tested legume species. Significantly higher yields in faba bean and pea and all tested plant structure parameters in pea were recorded in 2018 compared to 2017. The tested parameters of gas exchange and chlorophyll fluorescence were higher in pea in 2018 (except for transpiration intensity) and in faba bean in 2017. The findings suggest that SAPs can be a useful tool to mitigate water stress effects in legumes, although their effectiveness depends on environmental conditions. Therefore, SAP application may be a promising agronomic strategy in regions prone to irregular rainfall or moderate drought. Full article

Mustard (Brassica juncea), an essential leaf and oil crop in China, exhibits notable yield potential and adaptability, both of which are influenced by the morphology of the leaf margin. Despite its agronomic importance, the genetic regulatory mechanisms governing this trait remain poorly understood, posing a challenge to molecular breeding efforts. In this study, mustard varieties with lobed and non-lobed leaf margins were used to systematically investigate the genetic basis of leaf margin differentiation through BSA-seq, RNA-seq, and bioinformatics analyses. BSA-seq screening identified four LMI1 homologous genes, including BjuOA10G33260, which may fissure the leaf margin by suppressing cytokinin signaling. RNA-seq analysis revealed significant enrichment of ethylene and growth hormone pathways during key stages of leaf development (at 12 days post-sowing). Integrated analysis of BSA-seq and RNA-seq data identified 15 genes involved in leaf morphogenesis, including BjuOB05G34700 (ADF4, an actin depolymerization factor), BjuOA08G35830 (GATA transcription factor 11), BjuOA09G42060 (ERF transcription factor), and BjuOA07G29650 (GATA transcription factor). Notably, BjuOA10G30380 (TGA2) and BjuOA10G34680 (LAX1) may regulate cytoskeletal dynamics and hormonal signaling, contributing to the development of leaf morphology. This study presents the first molecular network regulating the morphogenesis of the leaf edge in mustard, offering a theoretical foundation and valuable genetic resources for breeding new varieties with optimized leaf architecture. Full article

Numerous commercial products are used in poultry farms to maintain water quality and prevent pathogen dispersion, but their actual impact on broiler chicks’ performance and gut health remains underreported. This study aimed to investigate the effects of three commercial poultry water disinfectants on broiler chicks’ performance and the physicochemical characteristics of gastrointestinal content when continuously added to drinking water. A total of 144 one-day-old Ross^® 308 broiler chicks were randomly allocated into four treatment groups: Group A (negative control), Group B (0.01–0.025% v/v Product A [H₂O₂ + silver complex]), Group C (0.01–0.04% v/v Product B [H₂O₂ + peracetic acid]), and Group D (0.05–0.1% w/v Product C [peroxides]). Body weight (BW) was measured weekly, while average daily weight gain (ADWG), average daily feed intake (ADFI), and feed conversion ratio (FCR) were calculated for different time periods. Additionally, on days 15 and 40, the pH of the crop, gizzard, duodenum, jejunum, and cecum contents was assessed, while the viscosity of jejunal and ileal contents were also measured. Statistical analysis revealed that all water disinfectants significantly (p ≤ 0.05) reduced BW, ADWG, and ADFI during the early growth phase, followed by either recovery or stabilization in the later stages. Drinking water disinfectants induced significant changes in intestinal physicochemical parameters, including reductions in pH of the content in the jejunum (p ≤ 0.05) during early growth and increased gizzard pH (p ≤ 0.05) and digesta viscosity (p ≤ 0.05) at later ages. These findings suggest that continuous water disinfection can suppress broiler chicks’ performance during the early stages of growth while significantly altering the physicochemical characteristics of gastrointestinal content. Further research is needed to investigate the mechanism that underlaying these results and optimize dosage schemes that balance pathogen control with the health, welfare, and performance of broilers. Full article