Search Results (1,853)

Insect pests cause severe damage and yield losses to many agricultural crops globally. The use of chemical pesticides on agricultural crops is not recommended because of their toxic effects on the environment and consumers. In addition, pesticide toxicity reduces soil fertility, poisons ground waters, and is hazardous to soil biota. Therefore, applications of entomopathogenic nematodes (EPNs) and plant growth-promoting rhizobacteria (PGPR) are an alternative, eco-friendly solution to chemical pesticides and mineral-based fertilizers to enhance plant health and promote sustainable food security. This review focuses on the biological and ecological aspects of these organisms while also highlighting the practical application of molecular communication approaches in developing a novel plant health product. This insight will support this innovative approach that combines PGPR and EPNs for sustainable crop production. Several studies have reported positive interactions between nematodes and bacteria. Although the combined presence of both organisms has been shown to promote plant growth, the molecular interactions between them are still under investigation. Integrating molecular communication studies in the development of a new product could help in understanding their relationships and, in turn, support the combination of these organisms into a single plant health product. Full article

High nighttime temperature (HNT) due to asymmetric diurnal warming threatens wheat productivity. This study evaluated the effect of HNT on wheat phenology, physiology, and yield through field and controlled environment experiments in Central Queensland, Australia. Two wheat genotypes, Faraday and AVT#6, were assessed under three sowing dates—1 May (Early), 15 June (Mid), and 1 August (Late)—within the recommended sowing window for the region. In a parallel growth chamber study, the plants were exposed to two nighttime temperature regimes, of 15 °C (normal) and 20 °C (high), with consistent daytime conditions from booting to maturity. Late sowing resulted in shortened vegetative growth and grain filling periods and increased exposure to HNT during the reproductive phase. This resulted in elevated floret sterility, lower grain weight, and up to 40% yield loss. AVT#6 exhibited greater sensitivity to HNT despite maturing earlier. Leaf gas exchange analysis revealed increased nighttime respiration (Rn) and reduced assimilation (A), resulting in higher Rn/A ratio for late-sown crops. The results from controlled environment chambers resembled trends of the field experiment, producing lower grain yield and biomass under HNT. Cumulative nighttime hours above 20 °C correlated more strongly with yield losses than daytime heat. These findings highlight the need for HNT-tolerant genotypes and optimized sowing schedules under future climate scenarios. Full article

To address freshwater scarcity and the underutilization of low-saline water in the North China Plain, a field study was conducted to evaluate the effects of alternating sprinkler irrigation using saline and fresh water on soil water–salt dynamics and corn growth. Two salinity levels (3 and 5 g·L⁻¹, representing S1 and S2, respectively) and three irrigation strategies—saline–fresh–saline–fresh (F1), saline–fresh (F2), and mixed saline–fresh (F3)—were tested, resulting in six treatments: S1F1, S1F2, S1F3, S2F1, S2F2, and S2F3. S1F1 significantly improved soil water retention at a 30–50 cm depth and reduced surface electrical conductivity (EC) and Na⁺ concentration (p < 0.05). S1F1 also promoted more uniform Mg²⁺ distribution and limited Ca²⁺ loss. Under high salinity (5 g·L⁻¹), surface salt accumulation and ion concentration (Na⁺, Mg²⁺, and Ca²⁺) increased, particularly in S2F3. Corn growth under alternating irrigation (F1/F2) outperformed the mixed mode (F3), with S1F1 achieving the highest plant height, leaf area, grain number, and 100-grain weight. The S1F1 yield surpassed others by 0.4–3.0% and maintained a better ion balance. These results suggest that alternating irrigation with low-salinity water (S1F1) effectively regulates root-zone salinity and improves crop productivity, offering a practical strategy for the sustainable use of low-saline water resources. Full article

Cotton (Gossypium spp.) is an important industrial crop, but it is vulnerable to waterlogging stress. The relationship between cotton yields and waterlogging indicators (CY-WI) is fundamental for waterlogging disaster reduction. This review systematically summarized and analyzed literature containing CY-WI relations across 1970s–2020s. China conducted the most CY-WI experiments (67%), followed by Australia (17%). Recent decades (2010s, 2000s) contributed the highest proportion of CY-WI works (49%, 15%). Surface waterlogging form is mostly employed (74%) much more than sub-surface waterlogging. The flowering and boll-forming stage, followed by the budding stage, performed the most CY-WI experiments (55%), and they showed stronger negative relations of CY-WI than other stages. Some compound stresses enhance negative relations of CY-WI, such as accompanying high temperatures, low temperatures, and shade conditions, whereas some others weaken the negative CY-WI relations, such as prior/post drought and waterlogging. Anti-waterlogging applications significantly weaken negative CY-WI relations. Regional-scale CY-WI research is increasing now, and they verified the influence of compound stresses. In future CI-WI works, we should emphasize the influence of compound stresses, establish regional CY-WI relations regarding cotton growth features, examine more updated cotton cultivars, focus on initial and late cotton stages, and explore the consequence of high-deep submergence. Full article

Air pollution acts as a pervasive oxidative stressor, disrupting global crop production and ecosystem health through the overproduction of reactive oxygen species (ROS). Hazardous pollutants impair critical physiological processes—photosynthesis, respiration, and nutrient uptake—triggering oxidative damage and yield losses. This review synthesizes current knowledge on plant defense mechanisms, emphasizing the integration of enzymatic (SOD, POD, CAT, APX, GPX, GR) and non-enzymatic (polyphenols, glutathione, ascorbate, phytochelatins) antioxidant systems to scavenge ROS and maintain redox homeostasis. We highlight the pivotal roles of transcription factors (MYB, WRKY, NAC) in orchestrating stress-responsive gene networks, alongside MAPK and phytohormone signaling (salicylic acid, jasmonic acid, ethylene), in mitigating oxidative stress. Secondary metabolites (flavonoids, lignin, terpenoids) are examined as biochemical shields against ROS and pollutant toxicity, with evidence from transcriptomic and metabolomic studies revealing their biosynthetic regulation. Furthermore, we explore biotechnological strategies to enhance antioxidant capacity, including overexpression of ROS-scavenging genes (e.g., TaCAT3) and engineering of phenolic pathways. By addressing gaps in understanding combined stress responses, this review provides a roadmap for developing resilient crops through antioxidant-focused interventions, ensuring sustainability in polluted environments. Full article

Some herbicides, such as saflufenacil, can persist as residues in sprayer tanks even after cleaning, causing phytotoxicity in sensitive crops. This study aimed to simulate potential injury caused by saflufenacil residues, applied alone or combined with glyphosate, on soybean. The field experiment was conducted using a randomized complete block design with four replicates. The treatments included glyphosate (1440 g ha⁻¹), eight saflufenacil doses ranging from 1.09 to 70.00 g ha⁻¹, each tested alone or combined with glyphosate, and a weed-free control, totaling 18 treatments. Phytotoxicity was assessed at 7, 14, 21, 28, and 35 days after treatment (DAT). Physiological variables were measured at 21 DAT, and grain yield components were evaluated at harvest. Saflufenacil caused increasing phytotoxicity at doses exceeding 4.38 g ha⁻¹ when applied alone and above 2.17 g ha⁻¹ when combined with glyphosate. The highest doses negatively affected soybean physiology and grain yield components. Soybean tolerated up to 2.17 g ha⁻¹ saflufenacil alone and up to 1.09 g ha⁻¹ combined with glyphosate without significant yield loss. These results highlight the importance of thorough and correct cleaning of the sprayer tank and suggest limit residue levels that avoid crop damage, helping to prevent unexpected damage to soybean in crop rotations. Full article

The detection of plant constituents is a crucial issue in precision agriculture, as monitoring these enables the automatic analysis of factors such as growth rate, health status, and crop yield. Tomatoes (Solanum sp.) are an economically and nutritionally important crop in Mexico and worldwide, which is why automatic monitoring of these plants is of great interest. Detecting leaves on images of outdoor tomato plants is challenging due to the significant variability in the visual appearance of leaves. Factors like overlapping leaves, variations in lighting, and environmental conditions further complicate the task of detection. This paper proposes modifications to the Yolov11n architecture to improve the detection of tomato leaves in images of complex outdoor environments by incorporating attention modules, transformers, and WIoUv3 loss for bounding box regression. The results show that our proposal led to a 26.75% decrease in the number of parameters and a 7.94% decrease in the number of FLOPs compared with the original version of Yolov11n. Our proposed model outperformed Yolov11n and Yolov12n architectures in recall, F1-measure, and mAP@50 metrics. Full article

Rice lodging severely affects crop growth, yield, and mechanized harvesting efficiency. The accurate detection and quantification of lodging areas are crucial for precision agriculture and timely field management. However, Unmanned Aerial Vehicle (UAV)-based lodging detection faces challenges such as complex backgrounds, variable lighting, and irregular lodging patterns. To address these issues, this study proposes SWRD–YOLO, a lightweight instance segmentation model that enhances feature extraction and fusion using advanced convolution and attention mechanisms. The model employs an optimized loss function to improve localization accuracy, achieving precise lodging area segmentation. Additionally, a grid-based lodging ratio estimation method is introduced, dividing images into fixed-size grids to calculate local lodging proportions and aggregate them for robust overall severity assessment. Evaluated on a self-built rice lodging dataset, the model achieves 94.8% precision, 88.2% recall, 93.3% mAP@0.5, and 91.4% F1 score, with real-time inference at 16.15 FPS on an embedded NVIDIA Jetson Orin NX device. Compared to the baseline YOLOv8n-seg, precision, recall, mAP@0.5, and F1 score improved by 8.2%, 16.5%, 12.8%, and 12.8%, respectively. These results confirm the model’s effectiveness and potential for deployment in intelligent crop monitoring and sustainable agriculture. Full article

The simplex strategies of fertilizer management and problems caused by simultaneous precipitation and high-temperature (SPH) climate were the main factors that led to yield loss and quality decline in the continuous cropping of tea chrysanthemum (Dendranthema morifolium ‘Jinsi Huang’). In this study, with sustainable biofertilizers being proposed as a potential solution. However, their effects under such constraints are underexplored. In this study, we compared different proportions of a sustainable dark tea biofertilizer, made with two commonly used fertilizers, by their contributions to the morphological, photosynthetic, and flowering traits of D. morifolium ‘Jinsi Huang’. The results showed that increasing the dark tea biofertilizer application to 4.5 kg·m⁻² significantly enhanced the soil alkali hydrolyzed nitrogen (596.53% increase), available phosphorus (64.11%), and rapidly available potassium (75.56%) compared to the levels in yellow soil. This nutrient enrichment in soil caused D. morifolium ‘Jinsi Huang’ to produce more leaves (272.84% increase) and flower buds (1041.67%), along with a strengthened photosynthetic capacity (higher Fv/Fm values and light saturation point). These improvements alleviated the photoinhibition caused by SPH climate conditions, ultimately leading to significantly higher contents of chlorogenic acid (38.23% increase) and total flavonoids (80.28%) in the harvested flowers compared to the control group. Thus, dark tea biofertilizer is a cost-effective and efficient additive for growing tea chrysanthemum in SPH regions due to improving soil quality and causing nutritional and functional components to accumulate in harvest flowers, which greatly promotes the commercial value of rural revitalization industries centered around tea chrysanthemum. Full article

Transpiration is the dominant process driving water loss in crops, significantly influencing their growth, development, and yield. Efficient monitoring of transpiration rate (Tr) is crucial for evaluating crop physiological status and optimizing water management strategies. The three-temperature (3T) model has potential for rapid estimation of transpiration rates, but its application to low-altitude remote sensing has not yet been further investigated. To evaluate the performance of 3T model based on land surface temperature (LST) and canopy temperature (T_C) in estimating transpiration rate, this study utilized an unmanned aerial vehicle (UAV) equipped with a thermal infrared (TIR) camera to capture TIR images of summer maize during the nodulation-irrigation stage under four different moisture treatments, from which LST was extracted. The Gaussian Hidden Markov Random Field (GHMRF) model was applied to segment the TIR images, facilitating the extraction of T_C. Finally, an improved 3T model incorporating fractional vegetation coverage (FVC) was proposed. The findings of the study demonstrate that: (1) The GHMRF model offers an effective approach for TIR image segmentation. The mechanism of thermal TIR segmentation implemented by the GHMRF model is explored. The results indicate that when the potential energy function parameter β value is 0.1, the optimal performance is provided. (2) The feasibility of utilizing UAV-based TIR remote sensing in conjunction with the 3T model for estimating Tr has been demonstrated, showing a significant correlation between the measured and the estimated transpiration rate (T_r-3T_C), derived from T_C data obtained through the segmentation and processing of TIR imagery. The correlation coefficients (r) were 0.946 in 2022 and 0.872 in 2023. (3) The improved 3T model has demonstrated its ability to enhance the estimation accuracy of crop Tr rapidly and effectively, exhibiting a robust correlation with T_r-3T_C. The correlation coefficients for the two observed years are 0.991 and 0.989, respectively, while the model maintains low RMSE of 0.756 mmol H₂O m⁻² s⁻¹ and 0.555 mmol H₂O m⁻² s⁻¹ for the respective years, indicating strong interannual stability. Full article

Soybean is a dual-purpose crop for food and oil, playing a crucial role in China’s grain production. Seed size and weight are key agronomic traits directly influencing the yield. Cytokinin oxidases/dehydrogenases (CKXs) specifically degrade certain isoforms of endogenous cytokinins (CKs), thereby modulating plant growth and seed development. However, their role in soybeans remains largely uncharacterized. In a previous genome-wide association study of 250 soybean core germplasms, we identified GmCKX3 as a yield-related gene. To elucidate its function, we developed GmCKX3-deficient mutants using CRISPR/Cas9 gene editing in soybean Williams82 and conducted a three-year phenotypic analysis. Loss of GmCKX3 function significantly enhanced the seed size and weight, which was attributed to an increased cell size and fat accumulation in the endosperm. This enhancement was driven by elevated endogenous CK levels resulting from suppressed GmCKX3 expression. Subcellular localization revealed that GmCKX3 resides in the endoplasmic reticulum and predominantly degrades the isopentenyladenine (iP)-type CK. Integrated transcriptomic and metabolomic analyses uncovered key genes and pathways involved in CK regulation, supporting GmCKX3’s central role in seed-trait modulation. These findings advance our understanding of cytokinin-mediated seed development and offer promising targets for molecular breeding aimed at improving the soybean yield. Full article

Kiwifruit is a high-value subtropical crop with significant nutritional and economic importance, but its cultivation faces growing challenges due to climate change, particularly in Caucasus. This study aims to study the impact of abiotic stressors such as temperature extremes, drought, and frost on the yield of the ‘Hayward’ cultivar over a 20-year period (from 2003 to 2022). Using a combination of agroclimatic data analysis, measurements of soluble solid content, and soil moisture assessments, this research identified key factors which limit kiwifruit cultivation productivity. The results revealed a high yield variability—68%, with the mean value declining by 16.6% every five years due to increasing aridity and heat stress. Extreme temperature rises of up to 30 °C caused yield losses of 79–89%, and the presence of frost led to declines of 71–94%. In addition, it is objectively proven that the vulnerability of kiwifruit is subject to climate-driven water imbalances. This highlights the need for adaptive strategy formation in the area of optimized irrigation for the sustainable cultivation of fruit in the subtropics. One of the study’s limitations was that it was organized around a single variety of kiwifruit (‘Hayward’). In view of the fact that there are significant differences in growth characteristics among kiwifruit varieties, future research should focus on overcoming this shortcoming. Full article

Wheat (Triticum aestivum L.) is a major cereal crop globally, but its production is increasingly threatened by fungal pathogens, particularly Fusarium culmorum (Wm. G. Sm.) Sacc., which causes seedling blight and root rot, leading to yield losses and mycotoxin contamination. Conventional control strategies, such as crop rotation and the use of fungicides, are often inadequate and contribute to the development of resistance, particularly with the overuse of similar modes of action. This study investigated quaternary pyridinium salts—nicotinamide and isonicotinamide derivatives—as potential sustainable antifungal agents. In vivo tests involved treating sterilized wheat seeds grown in sterile sand that had been inoculated with F. culmorum, using compounds previously confirmed to be active in vitro. Disease index, shoot and root length, and fresh and dry biomass were measured. Among the tested compounds, nicotinamide derivatives (2) and (3) showed the lowest disease index (0.9) at a concentration of 10 µg/mL. Most compounds promoted plant and root growth. Isonicotinamide derivatives (6) and (7) at 100 µg/mL increased root dry weight, while compound (6) at 10 µg/mL resulted in the most significant increase in plant length. These findings highlight the dual antifungal and growth-promoting potential of certain eco-friendly derivatives for managing F. culmorum and supporting wheat seedling development. Full article

Timely and accurate identification of plant diseases is critical to mitigating crop losses and enhancing yield in precision agriculture. This paper proposes AgriFusionNet, a lightweight and efficient deep learning model designed to diagnose plant diseases using multimodal data sources. The framework integrates RGB and multispectral drone imagery with IoT-based environmental sensor data (e.g., temperature, humidity, soil moisture), recorded over six months across multiple agricultural zones. Built on the EfficientNetV2-B4 backbone, AgriFusionNet incorporates Fused-MBConv blocks and Swish activation to improve gradient flow, capture fine-grained disease patterns, and reduce inference latency. The model was evaluated using a comprehensive dataset composed of real-world and benchmarked samples, showing superior performance with 94.3% classification accuracy, 28.5 ms inference time, and a 30% reduction in model parameters compared to state-of-the-art models such as Vision Transformers and InceptionV4. Extensive comparisons with both traditional machine learning and advanced deep learning methods underscore its robustness, generalization, and suitability for deployment on edge devices. Ablation studies and confusion matrix analyses further confirm its diagnostic precision, even in visually ambiguous cases. The proposed framework offers a scalable, practical solution for real-time crop health monitoring, contributing toward smart and sustainable agricultural ecosystems. Full article