Search Results (629)

Cereal production is increasingly constrained by soil degradation, water scarcity, climate variability, and rising disease and weed pressure. This review synthesizes current knowledge on the role of regenerative agronomic practices in cereal production, with particular emphasis on soil health, plant disease management, water-use efficiency, and yield stability. Available evidence consistently indicates that the greatest benefits arise not from individual practices but from integrated systems combining reduced tillage, crop residue retention, diversified crop rotations including legumes, cover crops, organic fertilization, and biologically based pest management. Such practices can increase soil biological activity and its ability to limit disease by enriching functionally beneficial microbial communities and limiting pathogens through competition for resources and niches, antibiosis, hyperparasitism, and the induction of plant resistance. They can also improve soil structure, water infiltration, water retention, and crop resilience to drought stress and, under certain conditions, reduce erosion, nutrient losses, and yield variability. However, the effects of regenerative practices are strongly dependent on soil type, climate, nitrogen balance, pest pressure, and the extent of adoption of regenerative practices. Risks may arise during the transition period, including yield declines, nitrogen immobilization, weed infestation, and increased disease pressure. Evaluation of these systems should encompass not only yield but also the grain quality and phytosanitary status, soil organic carbon stocks throughout the soil profile, N₂O emissions, and production profitability. The review covers cereal systems from temperate, humid, arid and semi-arid zones, and the results were interpreted considering climate, soil quality, water availability, and agronomic practices, as the same practice can produce different effects in different agroecological zones. Further research should prioritize long-term, multifactorial experiments conducted across diverse agroecological environments that integrate agronomic performance, environmental sustainability, and crop quality. Full article

Accurate estimation of canopy water content (OWC) is essential for precision irrigation, crop growth monitoring, and yield prediction. Unmanned aerial vehicle (UAV)-based multispectral remote sensing provides a rapid and non-destructive approach for monitoring crop water status; however, the selection of effective spectral features and appropriate machine learning algorithms for robust OWC estimation remains insufficiently investigated, particularly across multiple growing seasons. This study evaluated the potential of UAV multispectral imagery for estimating oilseed rape canopy water content using two feature selection strategies and four representative machine learning algorithms. Field experiments were conducted during two consecutive growing seasons (2023–2024 and 2024–2025). Different sowing dates, nitrogen application rates, and planting densities were used to create a broad range of canopy water conditions. UAV multispectral images were acquired at ten representative growth stages during the reproductive period, from stem elongation to physiological maturity. Fourteen vegetation indices (VIs) were extracted from the multispectral imagery. Pearson correlation analysis and principal component analysis (PCA) were used to select informative features. These features were then used to develop multiple linear regression (MLR), partial least squares (PLS), support vector machine (SVM), and random forest (RF) models. Model performance was evaluated using each single-year dataset and the combined two-year dataset to assess robustness under different seasonal conditions. The RF model consistently achieved the highest prediction accuracy. The correlation-based RF model developed from the combined two-year dataset produced the best performance. It achieved an R² of 0.966, an RMSE of 1.734%, and an RRMSE of 2.360% for the training dataset. For the independent testing dataset, the corresponding values were 0.901, 2.794%, and 3.830%, respectively. The PCA-based models showed similar performance and effectively reduced feature redundancy. However, they did not consistently outperform the correlation-based models. These results indicate that combining UAV multispectral imagery with appropriate feature selection and machine learning algorithms can accurately estimate oilseed rape canopy water content under field conditions. Integrating data from multiple growing seasons further improves model robustness and provides a practical basis for UAV-assisted crop water monitoring and precision agricultural management. Full article

Arid sandy soils are characterized by poor water retention, low nutrient availability, and limited productivity, posing major constraints to sustainable date palm cultivation under increasing water scarcity. A three-year field experiment was conducted to evaluate the combined effects of bentonite (BN), humic substances (HS), and Bacillus polymyxa (BP) under three irrigation regimes (70, 85, and 100% of crop evapotranspiration (ETc)) on soil hydro-physical properties, nutrient uptake, and fruit quality of ‘Siwi’ date palm (Phoenix dactylifera L.). Twelve treatment combinations (3 irrigation regimes × 4 soil amendment treatments) were evaluated over three consecutive growing seasons. The integrated application of BN, HS, and BP significantly improved soil hydro-physical properties by increasing field capacity and plant-available water while reducing bulk density. These improvements were associated with enhanced leaf N, P, and K concentrations and greater accumulation of total soluble solids, total and reducing sugars, phenolic compounds, flavonoids, and β-carotene compared with the untreated control. The combined application of BN (12 kg palm⁻¹) + HS (1 L palm⁻¹) + BP (28 mL palm⁻¹) produced the most favorable overall responses. Moderate deficit irrigation (85% ETc) provided the best balance between fruit quality and water conservation, maintaining superior fruit biochemical quality while reducing irrigation water use by approximately 15% compared with full irrigation. Multivariate analyses further supported these findings by revealing strong positive associations among soil water availability, nutrient status, sugars, and antioxidant-related compounds, while climatic variables were closely associated with seasonal variation in fruit biochemical characteristics. Overall, the integrated application of bentonite, humic substances, and B. polymyxa under 85% ETc irrigation represents an effective management strategy for improving soil performance, enhancing fruit nutritional quality, and increasing water-use irrigation efficiency in sandy soils under arid conditions. Full article

Fruit and vegetable by-products can be interpreted more usefully as postharvest crop-derived tissue fractions than as generic waste streams or sources of recoverable compounds. This integrative review synthesizes evidence on plant tissues, postharvest quality, stabilization, safety, and analytical characterization to develop a tissue-to-raw-material framework for plant-based food development. The framework begins with crop source, cultivar or maturity, organ/tissue identity, and postharvest history, and then links these variables to stabilization, safety screening, analytical evidence, intended-use specifications, and route assignment. Brassicaceae crops—including napa cabbage, radish, cabbage, broccoli, and cauliflower—serve as representative cases because their leafy, root, stem, core, stalk, and trimming fractions differ in water status, tissue fragility, sulfur-related traits, sensory constraints, and stabilization needs. Application suitability is therefore assessed from tissue identity, postharvest condition, stabilization history, safety status, and intended-use specifications rather than compound richness alone. Within this framework, zero-waste development means assigning a documented tissue fraction to a supported food, fermentation, coating/film, or selected secondary-material route, while using lower-risk assignment, downgrading, or exclusion where traceability, safety, stability, or specification evidence remains insufficient. Full article

Accurate assessment of crop water status is critical for precision irrigation and sustainable water management in agriculture. This study develops a UAV-based thermal infrared inversion framework for high-resolution canopy temperature retrieval and irrigation decision support in tea plantations. The proposed approach integrates multi-frame image mosaicking, threshold-based canopy extraction, and a gray–temperature calibration model to generate spatially continuous canopy temperature maps. Crop water stress was quantified using the Crop Water Stress Index (CWSI), and its reliability was further evaluated by analyzing its relationship with stomatal conductance. The framework further estimates soil moisture status and irrigation requirements based on a threshold-based irrigation strategy. The results show that the linear gray-temperature calibration model achieved a maximum absolute error of less than 0.3 °C and that the calculated CWSI and estimated irrigation requirement were strongly correlated with measured stomatal conductance, with R² up to 0.91. The proposed method provides a practical technical workflow from UAV thermal imagery acquisition to canopy temperature retrieval and quantitative irrigation decision-making, demonstrating its potential for precision irrigation management in tea plantations. Full article

Cereal crops, including wheat and barley, are essential for global food security, but their productivity is strongly affected by nitrogen availability and water limitation. This study investigated the phenotypic responses of two commercially significant spring wheat cultivars, Videodur (DU) and Sensas (SW), and two spring barley cultivars, Tiroler Imperial (SG1) and Amidala (SG2), exposed to two nitrogen regimes, low nitrogen at 25 kg N/ha (N25) and high nitrogen at 130 kg N/ha (N130), under drought and well-watered conditions. Plants were monitored from the late vegetative stage through maturity under controlled multivariable climatic conditions similar to field settings. A high-throughput phenotyping workflow was applied, combining precision watering, RGB imaging, infrared thermography, and VNIR–SWIR hyperspectral imaging to quantify plant growth, projected digital biomass, plant temperature, water use efficiency, and spectral vegetation indices associated with pigment dynamics, water status, maturation, and senescence. The results revealed cultivar-specific responses to combined nitrogen and drought stress. Under drought conditions, the high nitrogen treatment (N130) increased plant temperature (Tplant) for barley (cv. SG1) and wheat (cv. SW) compared to N25, thereby accelerating early maturation. However, the decline in chlorophyll was not uniformly faster across all cultivars tested. The DU cultivar exhibited superior chlorophyll absorption and reflectance, indicating better drought adaptation compared to other tested species. The high nitrogen treatment (N130) reduced water use efficiency (WUE) in the SW and SG2 cultivars compared to N25, implying that these cultivars used more water. Enhanced nitrogen did not consistently improve water use efficiency but did accelerate the growth cycle. SG2 was particularly sensitive to drought, showing declines in vegetation indices, except for the Water Content Index, highlighting the need for precise water and nitrogen management. Overall, the integration of hyperspectral, thermal, RGB, and water use measurements enabled the identification of trait signatures linked to drought adaptation, nitrogen response, maturation, and senescence. These findings provide practical insights for optimizing nitrogen and irrigation management and for supporting breeding strategies aimed at improving cereal crop resilience under climate-change-associated stress conditions. Full article

Efficient nitrogen (N) management is essential for sustaining crop productivity while minimizing environmental impacts associated with nitrogen losses. However, the high spatial and temporal variability of soil nitrogen dynamics and crop nitrogen status makes field-scale monitoring challenging, while conventional soil and plant sampling methods are labor-intensive, destructive, and provide limited spatial coverage. Recent advances in remote sensing technologies and machine learning (ML) offer promising alternatives for high-throughput, non-destructive monitoring of crop nitrogen status and related nitrogen dynamics in agroecosystems. This review synthesizes current progress in the use of proximal and remote sensing platforms, including unmanned aerial vehicles (UAVs), satellites, and ground-based sensors for assessing crop nitrogen status and inferring soil nitrogen availability. We examine spectral, thermal, and structural indicators, together with emerging sensor-fusion and time-series approaches. We also evaluate ML algorithms, including emerging foundation model approaches, for estimating crop nitrogen status and inferring soil nitrogen indicators, highlighting their performance, limitations, and transferability across environments. Particular emphasis is placed on field-scale applications in heterogeneous and water-limited systems, where nitrogen-water interactions critically influence crop responses. Finally, we discuss current challenges, including data scarcity, model generalization, and operational constraints, and outline future directions toward integrated, real-time decision support systems for precision nitrogen management. Overall, this review provides a comprehensive framework for leveraging remote sensing and data-driven approaches to improve nitrogen monitoring and enhance nitrogen use efficiency in diverse cropping systems. Full article

Continuous monoculture alters rhizosphere soil conditions and microbial community structure, but the integrated soil biochemical, microbial, and metabolomic responses of pepper (Capsicum annuum L.) rhizosphere soils remain insufficiently characterized. Here, we compared uncropped/non-continuously cropped pepper soil (Y0) with soil under 10 years of pepper monoculture (Y10) using soil physicochemical assays, enzyme measurements, 16S rRNA and ITS amplicon sequencing, untargeted UHPLC-Q Exactive HFX metabolomics, and predictive functional profiling. Long-term monoculture markedly separated Y10 from Y0 in multivariate analyses. Y10 soils showed higher organic matter, available nitrogen, available phosphorus, available potassium, and electrical conductivity in soil-water extracts, whereas microbial biomass carbon and pH were lower. Soil enzyme profiles also differed between treatments. Microbial alpha diversity declined under Y10, and bacterial and fungal community structures were clearly separated between treatments. At the taxonomic level, Acidobacteriota and several oligotrophic bacterial taxa were relatively enriched in Y0, whereas Proteobacteria, Bacteroidota, Chloroflexi, Bacillota, Pseudomonas, Bacillus, and several fungal genus-level taxa increased in Y10. Untargeted metabolomics revealed extensive remodeling of rhizosphere metabolites, with 413 up-regulated and 21 down-regulated differential metabolites in Y10. Differential metabolites were mainly associated with carboxylic acids and derivatives, benzene and substituted derivatives, fatty acyls, aromatic-compound transformation, sulfur metabolism, alkaloid biosynthesis, and microbial metabolism. Correlation analyses further linked key microbial taxa with soil pH, microbial biomass carbon, available nutrients, electrical conductivity, and enzyme activities. These results indicate that long-term pepper monoculture is associated with coordinated shifts in soil chemical status, microbial community composition, and metabolite profiles. The study provides an integrated basis for understanding rhizosphere changes under pepper continuous-cropping systems while recognizing that functional predictions and metabolite annotations require experimental validation. Full article

Detecting soil–water conditions ranging from dry to fully ponded in rice fields using solely multispectral remote sensing is crucial for irrigation water management practices focused on water savings in Semi-Arid Australia. To this end, this research employed the Minimum Redundancy Maximum Relevance (mRMR) algorithm to identify a set of multispectral remote sensing indices for use with Machine Learning (ML) to predict three soil–water conditions in irrigated rice: “Flooded”, “Saturated”, and “Dry”. Two models were developed: Model 1, using the most frequently used remote sensing indices in the literature; Model 2, including multispectral variables selected by the mRMR algorithm. Model 2 achieved the highest performance, with an accuracy of 0.64 and a $\mathrm{k}\mathrm{a}\mathrm{p}\mathrm{p}\mathrm{a}$ of 0.46, and ROC-AUC values of 0.87, 0.62, and 0.83 for “Flooded”, “Saturated”, and “Dry”, respectively. All models exhibit high confusion rates between “Flooded” and “Saturated” conditions, suggesting that multispectral remote sensing doesn’t provide sufficient information to distinguish these soil–water conditions. The SHAP analysis revealed that vegetation-sensitive indices encoding information on crop biomass, plant moisture, and senescence status were the primary drivers of soil–water condition prediction. Full article

With the rapid development of artificial intelligence, UAV remote sensing, and agricultural Internet of Things technologies, crop growth monitoring is evolving from manual inspection and single-source analysis toward intelligent decision-making based on multisource perception. However, existing methods still suffer from limited robustness under environmental variations, insufficient integration between UAV imagery and sparse ground sensor observations, and weak capability for transforming predictions into practical agricultural management recommendations. This study proposes a UAV–ground sensor collaborative lightweight framework for crop growth assessment and agricultural decision support. The proposed framework integrates UAV RGB and multispectral imagery with ground sensor observations through a region-level aerial–ground alignment mechanism and a sensor-guided attention fusion module, enabling environmental conditions to enhance visual feature interpretation. Furthermore, a fact-constrained decision module is developed to generate management recommendations based on crop status, environmental risks, and field information. Experimental results demonstrate that the proposed method achieves superior performance in crop growth classification and yield-trend prediction, reaching Accuracy, Precision, Recall, and F1-score values of $92.47\%$, $91.86\%$, $91.39\%$, and $91.62\%$, respectively, with an RMSE of $0.381$ and an $R^2$ of $0.902$. The lightweight framework requires only $6.18$M parameters and $0.91$G FLOPs, achieving $39.56$ ms inference latency and $25.28$ FPS on edge devices. The proposed framework also improves decision reliability, achieving an expert agreement rate of $89.34\%$ and a risk identification accuracy of $90.18\%$. Economic analysis indicates that the proposed framework reduces labor cost, water consumption, and fertilizer input by $49.7\%$, $26.7\%$, and $23.0\%$, respectively, while increasing net benefit by $46.1\%$ compared with conventional field management practices. These results demonstrate that the proposed method provides an accurate, interpretable, and deployable AI-driven solution for intelligent crop management in smallholder and medium-sized farming systems. Full article

Fresh ginseng is a high-value medicinal root crop whose postharvest quality is affected by field heterogeneity, washing, packaging, and storage. This study evaluated two packaging films—50 µm polyethylene (PE50) and a 30 µm polyethylene/30 µm oriented polypropylene laminate (PE30/OPP30)—and a post-wash 80 ppm peracetic acid (PAA) spray applied at 20 °C at approximately 60 mL kg⁻¹ fresh root mass. Field-sorted six-year-old roots from one commercial ridge were stored at −2 °C for 14 weeks. The primary factorial design comprised film and PAA treatment, while farmer grade class was retained as a stratification factor and tissue position as a within-root factor for destructive analyses. Package atmosphere, post-storage ambient CO₂ evolution, cumulative weight loss, tissue moisture, color and visual marketability, 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity, total phenolic content (TPC), and targeted liquid chromatography (LC) quantification of Rg1 and Rb1 were evaluated using three independent biological replicates. PE30/OPP30 generated lower O₂ and higher CO₂ than PE50 and reduced cumulative weight loss; however, O₂ reached 0.63% and CO₂ reached 14.04% at 4 weeks, indicating potentially hypoxic conditions. PAA did not consistently improve the measured physicochemical traits, and microbial efficacy was not evaluated. Fine roots showed the lowest moisture status but the highest Rg1 and Rb1 contents. These findings provide a single-field baseline for integrating packaging response, tissue water status, and chemical markers, but require validation across production sites, seasons, and packaging-film types before broad commercial application. Full article

Mango (Mangifera indica L.) cultivation is expanding into arid regions where high evaporative demand and restricted irrigation frequently constrain tree performance, creating a need for management strategies that reconcile water saving with sustained productivity. The present two-season field study evaluated the responses of “Keitt” mango grown in a hot, sandy environment to three foliar treatments, Ascophyllum nodosum extract (2%), nano-silicon (100 mg L⁻¹), and ascorbic acid (200 mg L⁻¹), under three deficit irrigation regimes (100%, 75%, and 50% of crop evapotranspiration, ETc). Growth, yield, plant–water relations, photosynthetic pigments, oxidative-stress markers, and antioxidant defenses were assessed and integrated through multivariate analysis. Deficit irrigation progressively reduced shoot growth, yield, relative water content, stomatal conductance, and leaf chlorophyll, while elevating proline, malondialdehyde, and hydrogen peroxide. Both A. nodosum and nano-silicon were consistently associated with attenuated responses across all irrigation levels, with higher antioxidant enzyme activity and retention of a disproportionate share of yield under severe deficit, corresponding to an increase in water productivity of up to approximately 31% at 50% ETc. The closely correlated, opposing shifts in growth and oxidative traits indicate associations among water status, photosynthetic characteristics, and antioxidant responses. These findings describe patterns of co-variation and should not be interpreted as evidence of direct causal mechanisms. Overall, these foliar biostimulants, especially A. nodosum, offer a promising, practical means of complementing deficit irrigation in arid mango production. Full article

Global climate change is intensifying water scarcity in Mediterranean agroecosystems, demanding a transition from rainfed to irrigated management for high-value crops like vineyards and olive groves. This study introduces a novel hydrologic framework to assess field-scale rainwater harvesting potential across nearly 60,000 individual vineyard and olive grove parcels in continental Portugal. Unlike conventional valley-focused models that delineate catchments at drainage junctions, our approach uses high-resolution digital elevation models and open-source spatial libraries (Python’s Fiona, Rasterio, Whitebox) to link every agricultural pixel to its unique upstream hillslope catchment. We quantify and compare “in-parcel” resources (direct precipitation, Vp) with “ex-parcel” resources (upstream runoff, Vup) under historical (1981–2010) and future (2041–2070) climate scenarios (CMIP6; SSP1-2.6, SSP3-7.0, and SSP5-8.5). A central contribution of this study is the evaluation of water security, defined here as the relative safety buffer between harvested water and the biological irrigation requirements (Vip) prescribed for both cultures in each of seven agroclimatic zones defined across the country. Security categories are based on the ratio (Vp − Vip)/Vip for in-parcel resources and (Vup − Vip)/Vip for ex-parcel resources, where values above zero indicate a sustainable surplus, and negative values signify a state of insecurity. Results demonstrate that ex-parcel resources are significantly more substantial, offering 2.5 to 35 times the potential of in-parcel counterparts. While vineyards currently exhibit high security nationwide, southern olive groves face a critical degradation from “secure” to “insecure” status by 2070 under fossil-fueled pathways (SSP5-8.5), with security indices dropping as low as −33.2 in the southern Alentejo region. This highlights ex-parcel runoff as a vital, underutilized hydrologic buffer that can safeguard Mediterranean agriculture against projected climate-induced deficits. Full article

To address the difficulty of directly incorporating crop growth-stage information into industrial control processes and the limited adaptability of control parameters to different developmental stages in conventional greenhouse fertigation management, this study developed a vision-based growth-stage determination and PLC-based fertigation parameter invocation system for greenhouse blueberry cultivation. The system integrated greenhouse blueberry image acquisition, edge-based visual recognition, STM32-based encoding conversion, PLC control, human–machine interaction, and actuator linkage. Image samples were collected from greenhouse blueberry plants, whereas system-level linkage verification was conducted on a small greenhouse prototype platform. The edge vision module was used to output preliminary blueberry growth-stage labels, while environmental and substrate sensor data were used for sensor status verification and control safety validation. The final growth-stage label was converted by the STM32 unit into a discrete coded signal and then transmitted to the PLC. Based on a predefined stage-strategy table, the PLC invoked the corresponding target parameters and drove the irrigation, fertilizer delivery, supplemental lighting, ventilation, and shading devices for coordinated control. The image-level stage classification evaluation based on an independent test set showed that different lightweight YOLO classification models exhibited different performance levels in identifying the major growth stages of blueberry. YOLO11n-cls achieved the highest Accuracy and Macro F1-score, reaching 85.71% and 84.81%, respectively. YOLOv8n-cls achieved an Accuracy, Macro F1-score, and Macro AP of 80.95%, 81.10%, and 91.25%, respectively, showing a favorable balance between model size and recognition performance. The confusion matrix indicated that misclassifications mainly occurred between the fruit expansion stage and the ripening stage, reflecting the morphological continuity of blueberry fruit development during the transitional period. The system linkage test results showed that blueberry growth-stage labels could be output by the edge vision terminal, converted by the STM32 unit, read by the PLC, and used for stage-specific target parameter invocation. Sensor acquisition, HMI display, and actuator response were completed cooperatively. The single determination and output time of the edge terminal was 500–1000 ms, and the remote-control response delay was 0.3–1.0 s. No obvious communication interruption, command loss, or abnormal shutdown occurred during system operation. These results indicate that blueberry growth-stage recognition results can serve as input conditions for PLC parameter invocation and device-control testing on a small greenhouse prototype platform. This study did not conduct a complete closed-loop cultivation experiment under real production greenhouse conditions or establish long-term blueberry cultivation control treatments. Therefore, no quantitative conclusions are drawn regarding water and fertilizer use efficiency, fertilizer application reduction, plant physiological responses, yield, or fruit quality improvement. Full article

Water scarcity caused by irregular rainfall and high evapotranspiration rates in the Brazilian semi-arid region is one of the main factors limiting watermelon cultivation, underscoring the need for irrigation strategies capable of mitigating the adverse effects of water deficit. This study aimed to evaluate the effects of foliar salicylic acid application on the induction of water-deficit tolerance in watermelon plants subjected to water restriction at different phenological stages under semi-arid conditions. The experiment was conducted using a randomized block design in a split-plot arrangement, with five irrigation management strategies based on crop evapotranspiration (ETc) and four salicylic acid (SA) concentrations, with three replications and three plants per plot. Water deficit adversely affected the morphophysiological traits of the plants and the physical and chemical attributes of ‘Crimson Sweet’ watermelon fruits, with water restriction during the vegetative and flowering stages causing the most severe effects. Foliar application of salicylic acid at concentrations ranging from 1.2 to 2.6 mM increased relative water content by 9.4%, reduced electrolyte leakage by 11.60%, and enhanced CO₂ assimilation by 18.82%, instantaneous water-use efficiency in 128.10%, and instantaneous carboxylation efficiency by 46.92%. Salicylic acid concentrations within this range also improved plant water status, gas exchange, photosynthetic pigment content, growth, and the physical and chemical attributes of the fruits. In contrast, concentrations above 2.6 mM reduced gas exchange, photosynthetic pigment accumulation, chlorophyll a fluorescence, and the physical and chemical quality of ‘Crimson Sweet’ watermelon fruits. Thus, salicylic acid may be an alternative to modulate the tolerance of watermelon plants under water deficit during phenological phases. Full article