Search Results (9,861)

Crop aeroponic cultivation still faces issues such as insufficient precision in water supply control and scientifically-based irrigation scheduling. To address this challenge, the present study aims to establish a precision irrigation protocol adapted to the characteristics of crop aeroponic cultivation. Using coriander (Coriandrum sativum L.) as the experimental subject, crop water requirements were estimated utilizing both the FAO56 P-M equation and its revised form. The RMSE between the water requirement measured values and the calculated values using the P-M formula is 2.12 mm, the MAE is 2.0 mm, and the MAPE is 14.29%. The RMSE between the water requirement measured values and the calculated values using the revised P-M formula is 0.88 mm, the MAE is 0.82 mm, and the MAPE is 5.78%. The results indicate that the water requirement values calculated using the revised P-M formula are closer to the measured values. For model development, this study used coriander evapotranspiration as a basis. Major environmental variables influencing water requirement were selected as input features, and the daily reference water requirement served as the output. Three modeling approaches were implemented: Random Forest (RF), Bagging, and M5P Model Tree algorithms. The results indicate that, in comparing various input combinations (C1: air temperature, relative humidity, atmospheric pressure, wind speed, radiation, photoperiod; C2: air temperature, relative humidity, wind speed, radiation; C3: air temperature, relative humidity, radiation), the RF model based on C1 input demonstrated superior performance with RMSE = 0.121 mm/d, MAE = 0.134 mm/d, MAPE = 2.123%, and R² = 0.971. It significantly outperforms the RF models with other input combinations, as well as the Bagging and M5P models across all input scenarios, in terms of convergence rate, determination coefficient, and comprehensive performance. Its predictions aligned more closely with observed data, showing enhanced accuracy and adaptability. This optimized prediction model demonstrates particular suitability for forecasting water requirements in aeroponic coriander production and provides theoretical support for efficient, intelligent water-saving management in crop aeroponic cultivation. Full article

Lance nematodes, Hoplolaimus spp., feed on the roots of many kinds of plants, including agronomic crops. In this study, morphological and molecular analyses of several Hoplolaimus species and populations are provided. We were able to collect and characterize the topotype materials of H. galeatus from Arlington, Virginia; H. stephanus syn. n. from Nichols, South Carolina; and H. concaudajuvencus from Pensacola, Florida, and several additional populations and species from the United States, Israel, and India. Phylogenetic analyses of several hundred sequences of the D2–D3 expansion regions of 28S rRNA, ITS rRNA, and COI genes of Hoplolaimus species obtained from published and original datasets were given. Fifty-three new D2–D3 of 28S rRNA, 43 new ITS rRNA, and 47 new COI sequences from 23 isolates of Hoplolaimus spp. and one isolate of Peltamigratus christiei were obtained in this study. New molecular identities for H. concaudajuvencus and H. galeatus were proposed. Hoplolaimus stephanus syn. n. was considered a synonym of H. galeatus based on the morphological and molecular similarity of these two species. Analysis of morphology and molecular data did not reveal significant differences among H. columbus syn. n., H. indicus syn. n., and H. seinhorsti, and the first two species were synonymized with H. seinhorsti. A new species, H. floridensis sp. n., was described from many locations in Florida, USA. It was separated from other representatives of the genus Hoplolaimus by its morphological and molecular characteristics. Maps with geographical distribution of several lance nematode species in North America were reconstructed based on published and original molecular identification of samples. Full article

Emerging fungal plant pathogens are significant biotic stresses to crops that threaten global food security, biodiversity, and agricultural sustainability. Historically, these pathogens cause devastating crop losses and continue to evolve rapidly due to climate change, international trade, and intensified farming practices. Recent advancements in diagnostic technologies, including remote sensing, sensor-based detection, and molecular techniques, are transforming disease monitoring and detection. These tools, when combined with data mining and big data analysis, facilitate real-time surveillance and early intervention strategies. There is a need for extension and digital advisory services to empower farmers with actionable insights for effective disease management. This manuscript presents an inclusive review of the socioeconomic and historical impacts of fungal plant diseases, the mechanisms driving the emergence of these pathogens, and the pressing need for global surveillance and reporting systems. By analyzing recent advancements and the challenges in the surveillance and diagnosis of fungal pathogens, this review advocates for an integrated, multidisciplinary approach to address the growing threats posed by these emerging fungal diseases. Fostering innovation, enhancing accessibility, and promoting collaboration at both national and international levels are crucial for the agricultural community to protect crops from these emerging biotic stresses, ensuring food security and supporting sustainable farming practices. Full article

Potatoes are a strategic crop in Poland, particularly important for agriculture in the southern and southeastern parts of the country. Environmental variability makes assessing yield stability and quality traits of varieties crucial for food security. Research Objective and Methodology: This three-year field study (2021–2023) aimed to comprehensively assess the yield stability and quality traits of mid-early potato varieties. The research was conducted in four pedologically diverse locations (rendzinas, brown soils, alluvial soils, and pseudopodzolic soils), according to the COBORU methodology. Key yield parameters (total and marketable tuber yield) and quality traits (dry-matter and starch content and yield) were analyzed. Interregional stability was also assessed. The environmental characteristics were supplemented with detailed analyses of soil physicochemical and biological properties, monitoring of agroclimatic parameters, and an assessment of the impact of geographical location. The collected data was subjected to advanced statistical analyses (ANOVA, correlations, descriptive statistics). Results analyses revealed significant yield variation across soil types, with the highest yields on alluvial soils and the lowest on pseudopodzolic soils. Geographic location significantly influenced yield stability, highlighting the role of local factors. Strong correlations were also found between soil properties and starch content (r = 0.61–0.73), indicating a key influence of the soil matrix on tuber quality. Conclusions and Recommendations: This study provides practical recommendations for selecting potato varieties adapted to specific soil types, precision fertilization strategies, and climate-change-adaptation protocols. Further research should focus on the impact of extreme weather events, optimized water management, and the use of precision agriculture. Full article

Optimal application of nitrogen fertilizer is critical for soil characteristics and soil health. This study examined the effects of three rates of nitrogen fertilizer applications, which are lower rate (Treatment 1 (T1)-241 kg/ha), recommended rate (Treatment 2 (T2)-269 kg/ha), and higher rate (Treatment 3 (T3)-297 kg/ha), and their impacts on soil temperature, soil moisture and soil electrical conductivity at two different depths (0–30 cm and 30–60 cm) in maize cultivation at the Prairie View A & M university research farm in Texas. Soil moisture, soil temperature, and electrical conductivity (EC) sensors were installed in 27 plots to collect these data. Results showed that EC is lower at surface depth with all fertilizer application rates than at root zone soil depths. In the meantime, EC is increasing in the root zone soil depth with the increase in fertilizer rate. This study indicated that the moderate application (269 kg/ha, T2) which is also recommended rate, showed better soil health parameters and efficiency in comparison to other application rates maintaining stable and moderate electrical conductivity values (0.2 mS/cm at depth 2) and the highest median moisture content at the significant root zone depth (about 0.135 m³/m³), reducing nutrient leaching and salt accumulation. Also, a humid, warm climate in southern Texas specifically affects increasing nitrogen losses via leaching, denitrification, and volatilization compared to cooler regions, which requires higher application rates. Plant growth and yield results further confirmed that the recommended rate achieved the greatest plant height (157.48 cm) compared to T1 (153.07 cm). Ear diameters were also higher at the recommended rate, reaching 4.65 cm ears than in Treatment 3. However, grain productivity was highest under the lower fertilizer rate T1, with wet and dry yields of 11,567 kg/ha and 5959 kg/ha, respectively, compared to 10,033 kg/ha (wet) and 5047 kg/ha (dry) at T2, and 7446 kg/ha (wet) and 4304 kg/ha (dry) at T3. These findings suggest that while the moderate fertilizer rate (269 kg/ha) enhances soil health and crop growth consistency, the lower rate (241 kg/ha) can maximize productivity under the humid, warm conditions of southern Texas. This research highlights the need for precise nitrogen management strategies that balance soil health with crop yield. Full article

Using data collected from 294 farm households across Egypt, Morocco, and Tunisia, this study quantifies crop losses due to soil salinity and analyzes the key factors associated with it. Further, it analyzes the factors driving the farmers’ choice of adaptation measures against salinity. Almost 54% of households surveyed reported yield losses due to salinity, with a sizable portion experiencing losses above 20%. In response to salinization, farmers adopted five adaptation practices, including crop rotation, salt stress-tolerant varieties, drainage management, soil amendments, and improved irrigation practices. A generalized linear model is applied to examine the factors explaining crop loss due to salinity. Results show that a higher share of irrigated land correlates with greater salinity-related crop loss, particularly in areas with poor drainage and low water quality. Conversely, farms with good soil quality reported significantly lower losses. Crop losses due to salinity were much lower in quinoa compared to wheat. Farmers who received agricultural training or belonged to cooperatives reported lower losses. A multivariate probit model was employed to understand drivers of adaptive behaviors. The analysis shows credit access, cooperative membership, training, and resource endowments as significant predictors of adaptation choices. The results underscore the importance of expanding credit availability, strengthening farmer organizations, and investing in training for effective salinity management. Full article

Accurate monitoring of the crop water status is of great significance for agricultural water management. To address the limitations of traditional spectral models that neglect the synergistic effects of environmental factors, this study aimed to improve the prediction ability of winter wheat water status by integrating multi-source data and machine learning algorithms. The results demonstrated significant improvements in prediction accuracy when environmental factors were integrated with hyperspectral data. During the jointing, heading, and filling stages, the prediction accuracy of the winter wheat plant water content model based on canopy hyperspectral fusion environmental factors (temperature and soil water content) was significantly higher than that based on the canopy spectral data model. The model performance (R²) increased from 0.74, 0.59, and 0.70 to 0.82, 0.69, and 0.76, respectively. The SVM-based full-growth-stage fusion model exhibited superior performance (R² = 0.85, RMSE = 5.10%, RE = 7.79%), achieving accuracy improvements of 3.53%, 23.19%, and 11.84% compared to three key growth-period models. This study confirms that integrating canopy hyperspectral data with environmental factors systematically enhances the generalization capability and accuracy of winter wheat water content prediction, providing a reliable technical solution for precision irrigation and innovative agricultural development in the future. Full article

Citrus diseases severely impact fruit yield and quality. To facilitate in-field, non-destructive, and rapid detection of citrus leaf physiological and pathological conditions, this study proposes a classification method for citrus leaf physiological and pathological statuses that integrates visible/near-infrared multispectral technology with deep learning. First, a handheld spectrometer was employed to acquire spectral images of five sample categories—Healthy, Huanglongbing, Yellow Vein Disease, Magnesium Deficiency and Manganese Deficiency. Mean spectral data were extracted from regions of interest within the 350–2500 nm wavelength range, and various preprocessing techniques were evaluated. The Standard Normal Variate (SNV) transformation, which demonstrated optimal performance, was selected for data preprocessing. Next, we innovatively introduced an adaptive spectral positional encoding mechanism into the Transformer framework. A lightweight, learnable network dynamically optimizes positional biases, yielding the ASTransformer (Adaptive Spectral Transformer) model, which more effectively captures complex dependencies among spectral features and identifies critical wavelength bands, thereby significantly enhancing the model’s adaptive representation of discriminative bands. Finally, the preprocessed spectra were fed into three deep learning architectures (1D-CNN, 1D-ResNet, and ASTransformer) for comparative evaluation. The results indicate that ASTransformer achieves the best classification performance: an overall accuracy of 97.7%, underscoring its excellent global classification capability; a Macro Average of 97.5%, reflecting balanced performance across categories; a Weighted Average of 97.8%, indicating superior performance in classes with larger sample sizes; an average precision of 97.5%, demonstrating high predictive accuracy; an average recall of 97.7%, showing effective detection of most affected samples; and an average F1-score of 97.6%, confirming a well-balanced trade-off between precision and recall. Furthermore, interpretability analysis via Integrated Gradients quantitatively assesses the contribution of each wavelength to the classification decisions. These findings validate the feasibility of combining a handheld spectrometer with the ASTransformer model for effective citrus leaf physiological and pathological detection, enabling efficient classification and feature visualization, and offer a valuable reference for disease detection of physiological and pathological conditions in other fruit crops. Full article

Crop distribution represents crucial information in agriculture, playing a key role in ensuring food security and promoting sustainable agricultural development. However, existing methods for crop distribution primarily focus on modeling temporal dependencies while overlooking the interactions and dependencies among different remote sensing features, thus failing to fully exploit the rich information contained in multisource satellite imagery. To address this issue, we propose a deep learning-based method named HyperVTCN, which comprises two key components: the ModernTCN block and the TiVDA attention mechanism. HyperVTCN effectively captures temporal dependencies and uncovers intrinsic correlations among features, thereby enabling more comprehensive data utilization. Compared to other state-of-the-art models, it shows improved performance, with overall accuracy (OA) improving by approximately 2–3%, Kappa improving by 3–4.5%, and Macro-F1 improving by about 2–3%. Additionally, ablation experiments suggest that both the attention mechanism(Time-Feature Dual Attention, TiVDA) and the targeted loss optimization strategy contribute to performance improvements. Finally, experiments were conducted to investigate HyperVTCN’s cross-feature and cross-temporal modeling. The results indicate that this joint modeling strategy is effective. This approach has shown potential in enhancing model performance and offers a viable solution for crop classification tasks. Full article

The present study aimed to evaluate crop characteristics, including morpho-anatomical features and nutritional and health-promoting composition, of head lettuce cultivated in greenhouses covered with transparent glass (control) and glass containing a red luminophore (red). The plant material comprised two lettuce types: butterhead and iceberg. Alterations were observed in head dimensions, morphology, and leaf mesophyll structure of plants from the red greenhouse. Butterhead lettuce plants exhibited unaltered head area under tested conditions but displayed a reduction in accumulated sugars and amino acids, resulting in a decline in dry matter content. Conversely, an increase in soluble and insoluble sugars and amino acid content, along with no change in nitrate content, was observed in iceberg lettuce. However, the growth intensity of iceberg lettuce decreased, while its dry matter content increased. Moreover, phenols and vitamin C concentration were lower in iceberg lettuce than in the butterhead one. In the red greenhouse, the phenolic content declined in both lettuce types, but vitamin C levels were reduced in butterhead lettuce and remained unchanged in iceberg lettuce. The data clearly demonstrate that the extent of variation in crop characteristics observed in lettuce cultivated in the red greenhouse depended on the tested lettuce type, with notable alterations occurring in iceberg lettuce. Full article

Near-surface ozone (O₃) pollution has emerged as a growing threat to rice production in the Pearl River Delta (PRD), impairing photosynthesis, suppressing crop growth, and reducing yields. This study integrated long-term observational data with spatial crop distribution data and modeling approaches to assess O₃-induced impacts on rice yields and associated economic losses across the PRD from 2015 to 2023. The results showed that annual average O₃ concentrations during rice-growing periods increased from 41.3 to 66.0 μg/m³, with accumulated AOT40 values reaching 20.1 ppm·h. O₃ exposure led to annual average rice yield losses of 10.8% ± 0.8%, including 9.3% for double-early rice and 12.3% for double-late rice. Absolute yield losses totaled approximately 333,000 tons per year, equivalent to the caloric needs of 2.69 million people, with economic losses exceeding CNY 844 million. Vulnerability hotspots were identified in Zhaoqing and Jiangmen, each suffering over 100,000 tons of annual losses. Scenario simulations indicated that a 20% reduction in ambient O₃ could recover up to 54,700 tons annually. Future projections under RCP 2.6–8.5 suggested continued yield losses of 14,900 to 23,200 tons per year by 2050. Temporal adjustments to planting calendars may further mitigate these effects. This study highlights the urgent need for integrated mitigation strategies to enhance agricultural resilience in the face of ozone stress in industrialized delta regions. Full article

Solar greenhouses are an important component of modern facility agriculture, and the dynamic changes in their internal environment directly affect crop growth and yield. Among these factors, crop transpiration releases water vapor through transpiration, directly altering the indoor humidity balance and forming a dynamic coupling with factors such as temperature and light. The environment of solar greenhouses exhibits highly nonlinear and multivariate coupling characteristics, leading to insufficient prediction accuracy in existing models. However, accurate predictions are crucial for regulating crop growth and yield. However, current mainstream greenhouse environmental prediction models still have obvious limitations when dealing with such complexity: traditional machine learning models and single-variable-driven models have issues such as insufficient accuracy (average MAE is 15–20% higher than in this study) and weak adaptability to nonlinear environmental changes in multi-environmental factor coupling predictions, making it difficult to meet the needs of precision farming. A review of relevant research over the past five years shows that while LSTM-based models perform well in time series prediction, they ignore the spatial correlations between environmental factors. Models incorporating attention mechanisms can capture key variables but suffer from high computational costs. To address these issues, this study proposes a prediction model based on multi-strategy optimization and gradient-boosting (GBDT) algorithms. By introducing a multi-scale feature fusion module, it addresses the accuracy issues in multi-factor coupling prediction. Additionally, it employs a lightweight network design to balance prediction performance and computational efficiency, filling the gap in existing research applications under complex greenhouse environments. The model optimizes data preprocessing and model parameters through Sobol sequence initialization, adaptive t-distribution perturbation strategies, and Gaussian–Cauchy mixture mutation strategies and combines CatBoost for modeling to enhance prediction accuracy. Experimental results show that the MSCSO–CatBoost model performs excellently in temperature prediction, with the mean absolute error (MAE) and root mean square error (RMSE) reduced by 22.5% (2.34 °C) and 24.4% (3.12 °C), respectively, and the coefficient of determination (R²) improved to 0.91, significantly outperforming traditional regression methods and combinations of other optimization algorithms. Additionally, the model demonstrates good generalization capability in predicting multiple environmental variables such as temperature, humidity, and light intensity, adapting to environmental fluctuations under different climatic conditions. This study confirms that combining multi-strategy optimization with gradient-boosting algorithms can significantly improve the prediction accuracy of solar greenhouse environments, providing reliable support for precision agricultural management. Future research could further explore the model’s adaptive optimization in complex climatic regions. Full article

Smallholder irrigation farmers play a vital role in sustaining rural communities in South Africa. However, the performance of smallholder irrigators, both as income generators and job creators, has come under scrutiny in recent years. In Limpopo province, a study was conducted in the Vhembe District using cross-sectional data from 95 independent and 165 public smallholder irrigators, which are privately established farmers and users of government-supported and managed irrigation systems, respectively. Qualitative data were collected through questionnaires, key informant interviews, and group discussions. Quantitative data were analyzed by SPSS version 30 using themes and codes, employing inferential statistical methods such as chi-square and t-tests to assess variables related to agrifood systems, crop selection, and market access. The study found that smallholders predominantly favor the production of grains, vegetables, and horticultural crops, with a statistically significant (p < 0.05) similarity between independent and public irrigators. Public irrigators dominate within irrigation schemes at 64% of the total, with X² of 22.7 with 0.001 p-value. Amongst the groups, the income distribution shows a statistically significant difference in earnings between independent and public irrigators (χ² = 25.83, p < 0.001). Informal and formal markets are accessible and available to 59% of independent irrigators, but 30% of public irrigators only access the informal market (p < 0.001). The major identified challenge across all smallholders is the lack of food value addition and commercial packaging. The study recommends the development of food value addition initiatives, adoption of climate-smart practices, maintenance of infrastructure, and improvement of market access to enhance productivity and sustainability. Full article

Corn as a key food crop, has a wide range of varieties with similar appearances, making manual classification challenging. Thus, fast and non-destructive seed variety identification is crucial for improving yield and quality. Hyperspectral imaging is commonly used for non-destructive seed classification. For the advancement of smart agriculture and precision breeding, in this study, 30 corn varieties from Northwest China were analyzed using hyperspectral images (870–1709 nm) to extract spectral reflectance from the embryonic region. Traditional methods often involve selecting specific bands, which can lead to information loss and limited variety selection. In this study, information loss was reduced and manual intervention was minimized by using full-band spectral data. And preprocessing is performed using first-order derivatives to reduce the interference of noise and irrelevant information. Classification experiments were conducted using KNN, ELM, RF, 1DCNN, and an improved 1DCNN-LSTM-ATTENTION-ECA (CLA-CA) model. The CLA-CA model achieved the highest classification accuracy of 95.38%, significantly outperforming traditional machine learning and 1DCNN models. It is demonstrated that the innovative module combination method proposed in this study is able to successfully classify varieties of corn seeds, which provides a new option for the rapid and non-destructive identification of a variety of corn seeds. Full article

Electromagnetic induction (EMI) devices have become increasingly popular for their soil bulk properties, soil nutrient status, and use in taking non-invasive soil salinity measurements. However, the high cost of data acquisition (DAQ) systems has been a significant barrier to the widespread adoption of these devices. In this study, we addressed this challenge by developing a cost-effective, easy-to-use, open-source DAQ system, transferable to the end user. This system employs a Raspberry Pi 4 model, paired with various components, to monitor the speed and position of the EM38 (Geonics Ltd, Mississauga, ON, Canada) and compare these with a proprietary CR1000 system. Through our results, we demonstrate that the low-cost DAQ system can successfully extract the analogical signal from the device, which is strongly responsive to the variation in the soil’s physical properties. This cost-effective system is characterized by increased flexibility in software processes and provides performance comparable to the proprietary system in terms of its geospatial data and ECb measurements. This was validated by the strong correlation (R² = 0.98) observed between the data collected from both systems. With our zoning analysis, performed using the Kriging technique, we revealed not only similar patterns in the ECb data but also similar patterns to the Normalized Difference Vegetation Index (NDVI) map, suggesting that soil physical characteristics contribute to variability in crop vigor. Furthermore, the developed web application enabled real-time data monitoring and visualization. These findings highlight that the open-source DAQ system is a viable, cost-effective alternative for soil property monitoring in precision farming. Future enhancements will focus on integrating additional sensors for plant vigor and soil temperature, as well as refining the web application, supporting zone classification based on the use of multiple parameters. Full article