Search Results (11,548)

Information and communication technology (ICT) components, especially actuators in automated irrigation systems, are essential for managing precise irrigation and optimal soil moisture, enhancing orchard growth and yield. However, actuator malfunctions can lead to inefficient irrigation, resulting in water imbalances that impact crop health and reduce productivity. The objective of this study was to develop a signal processing technique to detect potential malfunctions based on the power consumption level and operating status of actuators for an automated orchard irrigation system. A demonstration orchard with four apple trees was set up in a 3 m × 3 m soil test bench inside a greenhouse, divided into two sections to enable independent irrigation schedules and management. The irrigation system consisted of a single pump and two solenoid valves controlled by a Python-programmed microcontroller. The microcontroller managed the pump cycling ‘On’ and ‘Off’ states every 60 s and solenoid valves while storing and transmitting sensor data to a smartphone application for remote monitoring. Commercial current sensors measured actuator power consumption, enabling the identification of normal and abnormal operations by applying threshold values to distinguish activation and deactivation states. Analysis of power consumption, control commands, and operating states effectively detected actuator operations, confirming reliability in identifying pump and solenoid valve failures. For the second solenoid valve in channel 2, with 333 actual instances of normal operation and 60 actual instances of abnormal operation, the model accurately detected 316 normal and 58 abnormal instances. The proposed method achieved a mean average precision of 99.9% for detecting abnormal control operation of the pump and solenoid valve of channel 1 and a precision of 99.7% for the solenoid valve of channel 2. The proposed approach effectively detects actuator malfunctions, demonstrating the potential to enhance irrigation management and crop productivity. Future research will integrate advanced machine learning with signal processing to improve fault detection accuracy and evaluate the scalability and adaptability of the system for larger orchards and diverse agricultural applications. Full article

To characterize the naturalized population of burr medic (Medicago polymorpha L.), a valuable pasture legume, in subtropical Queensland, Australia, a collection of 1747 lines from 107 sites in 11 regions was grown, and 26 phenotypic and agronomic attributes were recorded. This data matrix was analyzed by cluster, principal co-ordinates, and discriminant and correlation analyses to examine line relationships based on plant attributes and their association with site characteristics of climate and soil. Among the wide polymorphism of attributes across the collection zone, there were a number of notable phenotypic associations. One of these, with large green leaves, minimally dentate leaf margins, and light purple petioles, was widely distributed. Three others, one with a distinctive magenta leaf mark, dark purple petioles, and an upright habit; one with those same attributes but with a prostrate habit; and one with grey-green leaves, high frost resistance, and the ability to stay green and to produce high pod yields, were associated with climatic and soil characteristics in the north, east, and south of the collection zone, respectively. Days to flowering were longer in lines from saline soils at lower altitude, and plant vigor was greatest in lines from more fertile soils with higher rainfall. A wide variation in time to flower of lines at all collection sites contributes to the adaptation of M. polymorpha in subtropical Queensland and potentially to its persistence with future climate change. Full article

Invasive pests cause major ecological and economic damages to forests around the world including reduced carbon sequestration and biodiversity and loss of forest revenue. In this study, we used Random Forest to model forest mortality resulting from a 2015–2017 Spongy moth outbreak in the temperate deciduous forests of Rhode Island (northeastern U.S.). Mortality was modeled with a 100 m spatial resolution based on Landsat-derived defoliation maps and geospatial data representing soil characteristics, drought condition, and forest characteristics as well as proximity to coast, development, and water. Random Forest was used to model forest mortality with two classes (low/high) and three classes (low/med/high). The best models had overall accuracies of 82% and 65% for the two-class and three-class models, respectively. The most important predictors of forest mortality were defoliation, distance to coast, and canopy cover. Model performance improved only slightly with the inclusion of more than three variables. The models classified 35% of forests as having canopy mortality >5 trees/ha and 21% of Rhode Island forests having mortality >11 trees/ha. The study shows the benefit of Random Forest models that use both defoliation maps and geospatial environmental data for classifying forest mortality caused by Spongy moth. Full article

Background: In this study, the unconfined compressive strength (q_u) of a mixture consisting of clay reinforced with 24 mm-long basalt fiber was estimated using extreme learning machine (ELM). The aim of this study is to estimate the results closest to the data obtained through experimental studies without the need for experimental studies. The literature review reveals that the ELM technique has not been applied to predict the compressive strength of basalt fiber-reinforced clay, and this study aims to provide a novel contribution in this area. Methods: The experimental studies included data derived from a series of mixtures where water contents of 20%, 25%, 30%, and 35% were combined with kaolin clay reinforced with 24 mm-long basalt fiber at reinforcement rates of 0%, 1%, 2%, and 3%. Based on the experimental results obtained for these mixtures, an ELM model was developed to predict the q_u. Results: ELM, recognized for its computational efficiency and high predictive accuracy, demonstrated exceptional performance in this application, achieving an R value of 0.9976 and an RMSE of 0.0001. Furthermore, this study includes a figure representation illustrating that the ELM-based predictions align closely with the experimental results, underscoring its reliability. Conclusions: To further validate its performance, ELM was compared with other artificial intelligence models through a 5-fold cross-validation approach. The analysis revealed that ELM outperformed its counterparts, achieving a remarkable RMSE value of 0.000174, thereby solidifying its capability to accurately estimate the compressive strength of the soil under varying reinforcement and water content conditions. Thus, it is aimed to save labor, material, and time. Full article

In recent decades, excessive human activities have led to large-scale rocky desertification in karst areas. Vegetation restoration is one of the most important ways to control rocky desertification. In this study, vegetation surveys were conducted on three typical plantations in Jianshui County, Yunnan Province, a typical karst fault basin area, in 2016 and 2021. The plantations were Pinus massoniana forest (PM), Pinus yunnanensis forest (PY), and mixed forests of Pinus yunnanensis and Quercus variabilis (MF). Plant diversity and soil nutrients were compared during the five-year period. This paper mainly draws the following results: The plant diversity of PM, PY, and MF increased. With the increase of time, new species appeared in the tree layer, shrub layer, and herb layer of the three forests. Tree species with smaller importance values gradually withdrew from the community. In the tree layer, the Patrick index, Simpson index, and Shannon–Wiener index of the three forests increased significantly. The Pielou index changed from the highest for PM in 2016 to the highest for PY in 2021. In the shrub layer, the Pielou index of the three forests increased. The Patrick index changed from the highest for MF in 2016 to the highest for PY in 2021. There was no significant difference in species diversity index for the herb layer. With the increase of vegetation restoration time, the soil bulk density (BD) of the three forests decreased. There was no significant difference in soil total porosity (TP), soil capillary porosity (CP), and non-capillary porosity (NCP). The pH of PM increased significantly from 5.88~6.24 to 7.24~7.34. The pH of PY decreased significantly (p < 0.05). The contents of total nitrogen (TN) and ammonium nitrogen (NH₄⁺-N) in PY and MF decreased. The content of nitrate nitrogen (NO₃⁻-N) in the three forests increased significantly (p < 0.05). Total phosphorus (TP) content decreased in PM and MF. The content of available phosphorus (AP) in PM and PY increased. In general, with the increase of vegetation restoration time, plant diversity and soil physical and chemical properties have also been significantly improved. The results can provide important data support for vegetation restoration in karst areas. Full article

Soil salinization affects agricultural productivity and ecosystem health in Xinjiang, especially in arid areas. The region’s complex topography and limited agricultural data emphasize the pressing need for effective, large-scale monitoring technologies. Therefore, 1044 soil samples were collected from arid farmland in northern Xinjiang, and the potential effectiveness of soil salinity monitoring was explored by combining environmental variables with Landsat 8 and Sentinel-2. The study applied four types of feature selection algorithms: Random Forest (RF), Competitive Adaptive Reweighted Sampling (CARS), Uninformative Variable Elimination (UVE), and Successive Projections Algorithm (SPA). These variables are then integrated into various machine learning models—such as Ensemble Tree (ETree), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and LightBoost—as well as deep learning models, including Convolutional Neural Networks (CNN), Residual Networks (ResNet), Multilayer Perceptrons (MLP), and Kolmogorov–Arnold Networks (KAN), for modeling. The results suggest that fertilizer use plays a critical role in soil salinization processes. Notably, the interpretable model KAN achieved an accuracy of 0.75 in correctly classifying the degree of soil salinity. This study highlights the potential of integrating multi-source remote sensing data with deep learning technologies, offering a pathway to large-scale soil salinity monitoring, and thereby providing valuable support for soil management. Full article

Microplastics (MPs) are plastic particles ranging from 1000 to 5000 µm in diameter, posing a growing environmental and health risk. Composting is an excellent way to add nutrient-rich humus to the soil to boost plant development, but it also pollutes agricultural soil with MPs. Previous research has shown that MPs can threaten plant development, production, and quality, hence they must be studied. This study examined how a mixture of three MP types—polyethene (PE), polystyrene (PS), and polypropene (PP)—affected greenhouse tomato plant development. MP types were spiked at 1% w/w (MPs/soil) in tomato pots, whereas non-spiked growth medium was the control. Statistical analysis was conducted using an analysis of variance (ANOVA) and Tukey’s test (95% confidence) to compare treatments and controls. Soil spiked with MPs increased chlorophyll content (SPAD), transpiration rate, photosynthetic rate, and stomata conductance by 5.16%, 16.71%, 25.81%, and 20.75%, respectively, compared to the control but decreased sub-stomata CO₂ concentration by 3.23%. However, MPs did not significantly affect tomato plant morpho-physiological features (p > 0.05). Biochemical analysis of tomato fruits showed significant (p < 0.05) reduction effects of MPs on carotenoid, total flavonoid, and sugar but increased protein, ascorbate, and peroxidase activity. However, there was no significant difference (p > 0.05) in the effects of the combined MPs on total phenolic content. These data imply that whereas MPs did not influence tomato plant physiological and morphological properties, tomato fruit biochemistry was reduced. This raise concerns that an increase in MPs in soils may reduce antioxidant content and negatively affect human health contributing to a decrease in food security. Full article

There is still controversy over the influence of no-tillage (NT) on CO₂ emissions in farmland soil. Few studies focus on the impact of monitoring hours on the response of soil CO₂ emissions to NT. Therefore, an in situ experiment was conducted in maize cropland in the Shandong Yucheng Agro-ecosystem National Observation and Research Station in the North China Plain. The soil CO₂ emissions, soil water content (SWC), and soil temperature (ST) were automatically monitored using the morning sampling (MonS) and continuous sampling (multi-hour sampling in one day, DayS) methods during the whole maize growth stages. The results showed that the MonS method decreased the sum of soil CO₂ emissions by 146.39 g CO₂ m⁻² in the wet year 2018 and increased that by 93.69 g CO₂ m⁻² in the dry year 2019 when compared to the DayS method. The influence intensity of NT on soil CO₂ effluxes was decreased with the MonS method. In contrast, the MonS method had no significant effect on the differences in SWC between NT and conventional tillage. However, the MonS method increased the variance in ST between NT and conventional tillage by 0.45 °C, which was higher than that with the DayS method (0.20 °C) across years. Compared to the DayS method, the MonS method increased the regression coefficient of soil CO₂ emissions with SWC but decreased that with ST. This study is beneficial for reducing the artificial impact of monitoring hours on the data accuracy of soil CO₂ effluxes and deepening the understanding of the influence of NT on soil CO₂ emissions. Full article

Background/Objectives: Cowpea is an important legume crop in sub-Saharan Africa (SSA) and beyond. However, access to phosphorus (P), a critical element for plant growth and development, is a significant constraint in SSA. Thus, it is essential to have high P-use efficiency varieties to achieve increased yields in environments where little-to- no phosphate fertilizers are applied. Methods: In this study, crop phenology, yield, and grain P efficiency traits were assessed in two recombinant inbred line (RIL) populations across ten environments under high- and low-P soil conditions to identify traits’ response to different soil P levels and associated quantitative trait loci (QTLs). Single-environment (SEA) and multi-environment (MEA) QTL analyses were conducted for days to flowering (DTF), days to maturity (DTM), biomass yield (BYLD), grain yield (GYLD), grain P-use efficiency (gPUE) and grain P-uptake efficiency (gPUpE). Results: Phenotypic data indicated significant variation among the RILs, and inadequate soil P had a negative impact on flowering, maturity, and yield traits. A total of 40 QTLs were identified by SEA, with most explaining greater than 10% of the phenotypic variance, indicating that many major-effect QTLs contributed to the genetic component of these traits. Similarly, MEA identified 23 QTLs associated with DTF, DTM, GYLD, and gPUpE under high- and low-P environments. Thirty percent (12/40) of the QTLs identified by SEA were also found by MEA, and some of those were identified in more than one P environment, highlighting their potential in breeding programs targeting PUE. QTLs on chromosomes Vu03 and Vu08 exhibited consistent effects under both high- and low-P conditions. In addition, candidate genes underlying the QTL regions were identified. Conclusions: This study lays the foundation for molecular breeding for PUE and contributes to understanding the genetic basis of cowpea response in different soil P conditions. Some of the identified genomic loci, many being novel QTLs, could be deployed in marker-aided selection and fine mapping of candidate genes. Full article

It is well known that individual pea (Pisum sativum L.) cultivars differ in their symbiotic responsivity. This trait is typically manifested with an increase in seed weights, due to inoculation with rhizobial bacteria and arbuscular mycorrhizal fungi. The aim of this study was to characterize alterations in the root proteome of highly responsive pea genotype k-8274 plants and low responsive genotype k-3358 ones grown in non-sterile soil, which were associated with root colonization with rhizobial bacteria and arbuscular mycorrhizal fungi (in comparison to proteome shifts caused by soil supplementation with mineral nitrogen salts). Our results clearly indicate that supplementation of the soil with mineral nitrogen-containing salts switched the root proteome of both genotypes to assimilation of the available nitrogen, whereas the processes associated with nitrogen fixation were suppressed. Surprisingly, inoculation with rhizobial bacteria had only a minor effect on the root proteomes of both genotypes. The most pronounced response was observed for the highly responsive k-8274 genotype inoculated simultaneously with rhizobial bacteria and arbuscular mycorrhizal fungi. This response involved activation of the proteins related to redox metabolism and suppression of excessive nodule formation. In turn, the low responsive genotype k-3358 demonstrated a pronounced inoculation-induced suppression of protein metabolism and enhanced diverse defense reactions in pea roots under the same soil conditions. The results of the study shed light on the molecular basis of differential symbiotic responsivity in different pea cultivars. The raw data are available in the PRIDE repository under the project accession number PXD058701 and project DOI 10.6019/PXD058701. Full article

Global population growth generates problems relating to increasing demand for sustainable energy and waste treatment. Proper solid waste management promotes material reuse, maximizes recovery and reduces anthropological pressure on natural resources. Anaerobic digestion (AD) is an alternative method of stabilizing organic substrates and generating biogas as a source of environmentally friendly energy. In addition, digestate is not only a waste product of that process but also a renewable resource with many potential applications. The circular economy concept encourages the use of digestate as a source of nutrients that promotes plant growth and improves soil properties. However, the stabilized substrates often contain various contaminants, including heavy metals (HMs) and antibiotics that are also detected in digestate. Therefore, the agricultural use of digestate obtained by AD could increase the pool of these pollutants in soil and water environments and contribute to their circulation in these ecosystems. Moreover, digestate may also increase the co-selection of genes determining resistance to HMs and antibiotics in environmental microorganisms. This article comprehensively reviews published data on the residues of various HMs and antimicrobial substances in different digestates around the world and maps the scope of the problem. Moreover, the potential risk of residual levels of these contaminants in digestate has also been evaluated. The review highlights the lack of legal standards regulating the concentrations of drugs introduced into the soil with digestate. The results of the ecological risk assessment indicate that the presence of medically important antimicrobials in digestate products, especially those used in agriculture, should be limited. Full article

In vegetated areas, soil pH impacts plant growth, soil properties, and spectral characteristics. Remote sensing enables soil pH mapping by delivering detailed surface data, and while high-resolution satellite images show great potential in complex terrains, research in this area is still limited. This study evaluated PlanetScope (high-resolution) and Sentinel-2 (medium-resolution) images in estimating soil pH across diverse land use types in southwestern China’s hilly areas. It examined how spectral variables from four seasonal images affect prediction accuracy. We integrated topographic and spectral variables at seven spatial resolutions (3 m, 10 m, 20 m, 30 m, 40 m, 50 m, and 60 m), using extreme gradient boosting (XGboost) for orchards, dry land, and paddy fields. We found that the models developed with PlanetScope images tended to achieve better prediction accuracy compared to those utilizing Sentinel-2 images. For each satellite, single-temporal images showed greater predictive power under each land use type. In particular, the spring spectral data showed desirable predictive performance for the orchards and the paddy fields, while the autumn spectral data contributed more effectively to the models for the dry land. Specifically, PlanetScope provided the best prediction accuracy for soil pH at 3 m resolution (orchard: R² = 0.72, MAE = 0.24, RMSE = 0.30, RPD = 1.91; dry land: R² = 0.77, MAE = 0.37, RMSE = 0.40, RPD = 2.09; paddy field: R² = 0.66, MAE = 0.35, RMSE = 0.41, RPD = 1.71), while Sentinel-2 performed better at 10 m resolution (orchard: R² = 0.67, MAE = 0.29, RMSE = 0.33, RPD = 1.75; dry land: R² = 0.70, MAE = 0.39, RMSE = 0.47, RPD = 1.83; paddy field: R² = 0.64, MAE = 0.34, RMSE = 0.42, RPD = 1.66). Our findings demonstrate that sensor selection, land use, temporal phases, and modeling resolution significantly impact outputs. High-resolution PlanetScope images prove effective for predicting soil pH in complex terrains. Full article

Soil Organic Matter (SOM) is crucial for soil fertility, and effective detection methods are of great significance for the development of agriculture and forestry. This study uses 206 hyperspectral soil samples from the state-owned Yachang and Huangmian Forest Farms in Guangxi, using the SPXY algorithm to partition the dataset in a 4:1 ratio, to provide an effective spectral data preprocessing method and a novel SOM content prediction model for the study area and similar regions. Three denoising methods (no denoising, Savitzky–Golay filter denoising, and discrete wavelet transform denoising) were combined with nine mathematical transformations (original spectral reflectance (R), first-order differential (1DR), second-order differential (2DR), MSC, SNV, logR, (logR)′, 1/R, ((1/R)′) to form 27 combinations. Through Pearson heatmap analysis and modeling accuracy comparison, the SG-1DR preprocessing combination was found to effectively highlight spectral data features. A CNN-SVM model based on the Black Kite Algorithm (BKA) is proposed. This model leverages the powerful parameter tuning capabilities of BKA, uses CNN for feature extraction, and uses SVM for classification and regression, further improving the accuracy of SOM prediction. The model results are RMSE = 3.042, R² = 0.93, MAE = 4.601, MARE = 0.1, MBE = 0.89, and PRIQ = 1.436. Full article

The aim of this study is to construct and validate an expert system to predict the adaptation of irrigation technologies, water-saving strategies, and monitoring tools by small-scale farmers in Egypt. The research investigates the impact of economic, educational, environmental, and social factors on adaptation rates. To build the expert system, extensive knowledge was collected from experts, key concepts were identified, and production rules were created to generate tailored scenarios. These scenarios utilize the empirical cumulative distribution function (ECDF), selecting the scenario with the highest ECDF as the optimal irrigation technology. This approach ensures well-informed, data-driven decisions that are tailored to specific conditions. The expert system was evaluated under the conditions of ten small farms in Egypt. The results indicate that water cost and availability are significant drivers of technology adaptation. Specifically, subsurface drip irrigation (SDI) demonstrated an adaptation percentage of 75% at high water costs, with probabilities of 0.67 and 0.33, while soil mulching (SM) showed a 75% adaptation rate with a probability of 0.33 in high-cost scenarios. Conversely, when water availability was high, the adaptation percentage for all techniques was zero, but it reached 100% adaptation with a probability of 0.76 for SM and SDI and a probability of 1 for variable number of drippers (VND) and the use of sensors as monitoring tools during water shortages. Educational attainment and professional networks enhance the adaptation of advanced technologies and monitoring tools, emphasizing the role of knowledge and community engagement. Environmental conditions, including soil texture and salinity levels, directly affect the choice of irrigation methods and water-saving practices, highlighting the need for localized solutions. The source of irrigation water, whether groundwater or surface water, influences the preference for water-saving technologies. The study underscores the importance of tailored approaches to address the challenges and opportunities faced by small farmers in Egypt, promoting sustainable agriculture and efficient water management. The evaluation findings reveal that SDI is the most favored irrigation technology, with a probability of 0.55, followed by variable number of drippers (VND) at 0.38 and ultralow drip irrigation (ULDI) at 0.07 across various scenarios for small farmers. Regulated deficit irrigation (RDI) and SM are equally preferred water-saving strategies, each with a probability of 0.50. Sensors emerged as the preferred monitoring tool, boasting a high probability of 0.94. The analysis reveals the critical roles of economic pressures, educational levels, environmental conditions, and social networks in shaping the adaptation of sustainable agricultural practices. Full article

Machine traffic during timber harvesting operations induces soil compaction, which is particularly evident in the formation of ruts. Visual inspection of rut formation is labor-intensive and limits the volume of data that can be collected. This study aims to contribute to the limited knowledge base regarding the extent of soil physical disturbance caused by machine traffic on steep slopes and to evaluate the utility of LiDAR and UAV photogrammetry techniques. The selected traffic trails included single-pass uphill, single-pass downhill, three-pass round trip, and five-pass round trip trails, with an average slope of 70.7%. Traditional methods were employed to measure rut depth using a pin board and to assess soil bulk density (BD) and soil porosity (SP) from soil samples. The results revealed that the average rut depth was 19.3 cm, while the deepest ruts were observed after a single pass (uphill: 20.0 cm; downhill: 22.7 cm), where BD and SP showed the most significant changes. This study provides a rare quantitative evaluation of the applicability of remote sensing methods in forestry by comparing surface height data collected via a pin board with that derived from a Mobile LiDAR System (MLS) and UAV photogrammetry using structure-from-motion (SfM). When compared to pin board measurements, the MLS data showed an R² value of 0.74 and an RMSE of 4.25 cm, whereas the SfM data had an R² value of 0.62 and an RMSE of 5.27 cm. For rut depth estimation, SfM (16.0 cm) significantly underestimated values compared to the pin board (19.3 cm) and MLS (19.9 cm). These findings not only highlight the potential and limitations of remote sensing methods for assessing soil disturbance in steep forest environments but also contribute to addressing the knowledge gaps surrounding the effects of soil compaction in steep terrain. Full article