Search Results (33)

Alfalfa (Medicago sativa) persists for several years but must be rotated to another crop before replanting. Kura clover (T. ambiguum M. Bieb) is a perennial legume that can persist indefinitely without replanting; however, establishment is slow, which limits economic returns during the process. Two studies, each with four randomized complete blocks, were planted in two consecutive years at New Mexico State University’s Rex E. Kirksey Agricultural Science Center at Tucumcari, NM, USA, as the first known assessment evaluating alfalfa as an economic nurse crop during kura clover establishment using various kura clover–alfalfa drilled and broadcast planting arrangements. Irrigation termination due to drought limited yield measurements to three years after seeding. In that time, kura clover–alfalfa mixtures generally yielded equally to monoculture alfalfa, except for alternate row planting. After 5 years, the alfalfa stand percentage remained >80%, except for the alternate row treatment (69% stand). Kura clover monocultures attained about 40% stand, and the mixtures had a <25% stand. Alfalfa may persist for more than 5 years before relinquishing dominance to kura clover in mixtures, but the alfalfa would continue to provide economic returns as kura clover continues stand development with minimal production, but develops its root system to maximize production when released from the alfalfa nurse crop. Full article

With the objective of identifying superior Vernicia montana trees grounded in phenotypic and agronomic traits, this study sought to develop and implement a comprehensive evaluation method which would provide a practical foundation for future clonal breeding initiatives. Using the Vernicia montana propagated from seedling forests grown in the Suxian District of Chenzhou City in southern Hunan Province, we conducted pre-selection, primary selection, and re-selection of Vernicia montana forest stands and took the nine trait indices of single-plant fruiting quantity, single-plant fruit yield, disease and pest resistance, fruit ripening consistency, fruit aggregation, fresh fruit single-fruit weight, fresh fruit seed rate, dry seed kernel rate, and seed kernel oil content rate as the optimal evaluation indexes and carried out cluster analysis and a comprehensive evaluation in order to establish a comprehensive evaluation system for superior Vernicia montana trees. The results demonstrated that a three-stage selection process—consisting of pre-selection, primary selection, and re-selection—was conducted using a comprehensive analytical approach. The pre-selection phase relied primarily on sensory evaluation criteria, including fruit count per plant, tree size, tree morphology, and fruit clustering characteristics. Through this rigorous screening process, 60 elite plants were selected. The primary selection was based on phenotypic traits, including single-plant fruit yield, pest and disease resistance, and uniformity of fruit ripening. From this stage, 36 plants were selected. Twenty plants were then selected for re-selection based on key performance indicators, such as fresh fruit weight, fresh fruit seed yield, dry seed kernel yield, and oil content of the seed kernel. Then the re-selected optimal trees were clustered and analyzed into three classes, with 10 plants in class I, 7 plants in class II, and 3 plants in class III. In class I, the top three superior plants exhibited outstanding performance across key traits: their fresh fruit weight per fruit, fresh fruit seed yield, dry seed yield, and seed kernel oil content reached 41.61 g, 42.80%, 62.42%, and 57.72%, respectively. Compared with other groups, these figures showed significant advantages: 1.17, 1.09, 1.12, and 1.02 times the average values of the 20 reselected superior trees; 1.22, 1.19, 1.20, and 1.08 times those of the 36 primary-selected superior trees; and 1.24, 1.25, 1.26, and 1.19 times those of the 60 pre-selected trees. Fruits counts per plant and the number of fruits produced per plant of the best three plants in class I were 885 and 23.38 kg, respectively, which were 1.13 and 1.18 times higher than the average of 20 re-selected superior trees, 1.25 and 1.30 times higher than the average of 36 first-selected superior trees, and 1.51 and 1.58 times higher than the average of 60 pre-selected superior trees. Class I superior trees, especially the top three genotypes, are suitable for use as mother trees for scion collection in grafting. The findings of this study provide a crucial foundation for developing superior clonal varieties of Vernicia montana through selective breeding. Full article

This study aimed to evaluate the effects of varying traffic intensities, the time since harvesting, and the interaction between these two factors on the restoration of herbaceous cover on skid trails in the Hyrcanian forests, Northern Iran. Three compartments were selected from two districts within the pure oriental beech (Fagus orientalis Lipsky) stands of Kheyrud Forest, where ground-based timber extraction had occurred 5, 10, and 15 years prior. In each compartment, three skid trails representing low, medium, and high traffic intensities were identified. Control plots were established 10 m away from the trails. A total of 54 systematically selected 1 m × 1 m sample plots were surveyed: 27 on skid trails (three traffic intensities × three time intervals × three replicates) and 27 control plots (matching the same variables). Within each quadrat, all herbaceous plants were counted, identified, and recorded. Our findings revealed that only traffic intensity had a clear significant impact on plant abundance. High traffic intensity led to a pronounced decline in herbaceous cover, with disturbed skid trails showing reduced species diversity or the complete disappearance of certain species in comparison to the control plots. Time since harvesting and its interaction with traffic intensity did not yield statistically significant effects. Disturbance led to a reduction in the quantities of certain species or even their disappearance on skid trails in comparison to the control plots. Given the pivotal role of machinery traffic intensity in determining mitigation strategies, there is a critical need for research on region-specific harvesting techniques and the development of adaptive management strategies that minimize ecological impacts by aligning practices with varying levels of traffic intensity. Full article

Coral restoration is essential for recovering depleted populations and reef ecological functions. However, its effect on enhancing fish assemblages remains understudied. This study investigated the integration of 3D-printed and natural Staghorn coral (Acropora cervicornis) out-planting to assess their role in enhancing benthic spatial complexity and attracting fish communities. Conducted between 2021 and 2023 at Culebra Island, Puerto Rico, we employed a before-after-control-impact (BACI) design to test four treatments: natural A. cervicornis, 3D-printed corals, mixed stands of 3D-printed and natural corals, and non-restored controls. Fish assemblages were monitored through stationary counts. Results showed that integrating 3D-printed and natural corals enhanced fish assemblages and their ecological functions. Significant temporal changes in fish community structure and biodiversity metrics were observed, influenced by treatment and location. Herbivore abundance and biomass increased over time, especially in live coral and 3D-printed plots. Reefs with higher rugosity exhibited greater Scarid abundance and biomass post-restoration. Piscivore abundance also rose significantly over time, notably at Tampico site. Fishery-targeted species density and biomass increased, particularly in areas with live and 3D-printed coral out-plants. Fish assemblages became more complex and diverse post-restoration, especially at Tampico, which supported greater habitat complexity. Before restoration, fish assemblages showed a disturbed status, with biomass k-dominance curves above abundance curves. Post-out-planting, this trend reversed. Control sites showed no significant changes. The study demonstrates that restoring fast-growing branching corals, alongside 3D-printed structures, leads to rapid increases in abundance and biomass of key fishery species, suggesting its potential role promoting faster ecosystem recovery and enhanced coral demographic performance. Full article

Crop field monitoring using unmanned aerial vehicles (UAVs) is one of the most important technologies for plant growth control in modern precision agriculture. One of the important and widely used tasks in field monitoring is plant stand counting. The accurate identification of plants in field images provides estimates of plant number per unit area, detects missing seedlings, and predicts crop yield. Current methods are based on the detection of plants in images obtained from UAVs by means of computer vision algorithms and deep learning neural networks. These approaches depend on image spatial resolution and the quality of plant markup. The performance of automatic plant detection may affect the efficiency of downstream analysis of a field cropping pattern. In the present work, a method is presented for detecting the plants of five species in images acquired via a UAV on the basis of image segmentation by deep learning algorithms (convolutional neural networks). Twelve orthomosaics were collected and marked at several sites in Russia to train and test the neural network algorithms. Additionally, 17 existing datasets of various spatial resolutions and markup quality levels from the Roboflow service were used to extend training image sets. Finally, we compared several texture features between manually evaluated and neural-network-estimated plant masks. It was demonstrated that adding images to the training sample (even those of lower resolution and markup quality) improves plant stand counting significantly. The work indicates how the accuracy of plant detection in field images may affect their cropping pattern evaluation by means of texture characteristics. For some of the characteristics (GLCM mean, GLRM long run, GLRM run ratio) the estimates between images marked manually and automatically are close. For others, the differences are large and may lead to erroneous conclusions about the properties of field cropping patterns. Nonetheless, overall, plant detection algorithms with a higher accuracy show better agreement with the estimates of texture parameters obtained from manually marked images. Full article

Selenium (Se) is a beneficial element for plants and is essential for human nutrition. In plants, it plays an important role in the formation of selenocysteine and selenomethionine and in the activation of hydrolytic enzymes, which can aid in seed germination and reduce abiotic stress during germination. The objective of this study was to evaluate the effects of the application of selenium sources and rates to the soil on the physiological quality of cowpea seeds. The experimental design was a randomized block with four replications and a factorial scheme (7 × 2). Two sources of Se (sodium selenate and sodium selenite) and seven rates (0, 2.5, 5, 10, 20, 40 and 60 g ha⁻¹) were used. Physiological characterization was carried out by first counting of germination, germination, emergence, accelerated aging, cold testing, electrical conductivity, length and dry biomass of shoots and roots. Germination after accelerated aging increased with selenate, even at higher rates, whereas selenite provided benefits at lower rates. Selenation linearly increased germination after the cold test and linearly reduced electrolyte leakage as the Se rate increased. The soil application of Se is beneficial for cowpea seed quality. Compared with those treated with sodium selenite, cowpea plants treated with sodium selenate through the soil produce more vigorous seeds. The application of 10 g ha⁻¹ Se in the form of sodium selenate provides seedlings with faster germination and root development and is an alternative for rapid stand establishment. Full article

Plant counting is a critical aspect of crop management, providing farmers with valuable insights into seed germination success and within-field variation in crop population density, both of which are key indicators of crop yield and quality. Recent advancements in Unmanned Aerial System (UAS) technology, coupled with deep learning techniques, have facilitated the development of automated plant counting methods. Various computer vision models based on UAS images are available for detecting and classifying crop plants. However, their accuracy relies largely on the availability of substantial manually labeled training datasets. The objective of this study was to develop a robust corn counting model by developing and integrating an automatic image annotation framework. This study used high-spatial-resolution images collected with a DJI Mavic Pro 2 at the V2–V4 growth stage of corn plants from a field in Wooster, Ohio. The automated image annotation process involved extracting corn rows and applying image enhancement techniques to automatically annotate images as either corn or non-corn, resulting in 80% accuracy in identifying corn plants. The accuracy of corn stand identification was further improved by training four deep learning (DL) models, including InceptionV3, VGG16, VGG19, and Vision Transformer (ViT), with annotated images across various datasets. Notably, VGG16 outperformed the other three models, achieving an F1 score of 0.955. When the corn counts were compared to ground truth data across five test regions, VGG achieved an R² of 0.94 and an RMSE of 9.95. The integration of an automated image annotation process into the training of the DL models provided notable benefits in terms of model scaling and consistency. The developed framework can efficiently manage large-scale data generation, streamlining the process for the rapid development and deployment of corn counting DL models. Full article

Canola (Brassica napus L.) is an important oilseed crop that provides essential vegetable oil but faces significant competition from weeds that are influenced by various agronomic practices and environmental conditions. This study examines the complex interactions between canola stand density and weed intensity over three growing seasons, identifying a total of 27 weed species. It is important to establish a connection between the density of winter canola stands, the intensity of weeding and the response of individual weed species in real conditions. The case study was executed on plots located in the Přerov district (Olomouc region, Czech Republic). The assessment was carried out during two periods—autumn in October and spring in April. Canola plants (plant density) were counted in each evaluated area, weed species were identified, and the number of plants for each weed species was determined. Half of the plots were covered with foil before herbicide application to prevent these areas from being treated with herbicides. We used redundancy analysis (RDA) to evaluate the relationships between canola density and weed dynamics, both with and without herbicide treatment. The results show the ability of canola to compete with weeds; however, that is factored by the density of the canola stand. In dense stands (over 60 plants/m²), canola is able to suppress Galium aparine L., Geranium pusillum L., Lamium purpureum L., Papaver rhoeas L. and Chamomilla suaveolens (Pursh) Rydb. Nevertheless, there are weed species that grow well even in dense canola stands (Echinochloa crus-galli (L.) P. Beauv., Phragmites australis (Cav.) Steud., Tripleurospermum inodorum (L.) Sch. Bip. and Triticum aestivum L.). These findings highlight the potential for using canola stand density as a strategic component of integrated weed management to reduce herbicide reliance and address the growing challenge of herbicide-resistant weed populations. This research contributes significantly to our understanding of the dynamics of weed competition in canola systems and informs sustainable agricultural practices for improved crop yield and environmental stewardship. Full article

Hyperboloid cooling towers are counted among the largest cast-in-place industrial structures. They are an essential element of cooling systems used in many power plants in service today. Their main structural component, a reinforced-concrete shell in the form of a one-sheet hyperboloid with bidirectional curvature continuity, makes them stand out against other towers and poses very high construction and service requirements. The safe service and adequate durability of the hyperboloid structure are guaranteed by the proper geometric parameters of the reinforced-concrete shell and monitoring of their condition over time. This article presents an original concept for employing terrestrial laser scanning to conduct an end-to-end assessment of the geometric condition of a hyperboloid cooling tower as required by industry standards. The novelty of the proposed solution lies in the use of measurements of the interior of the structure to determine the actual thickness of the hyperboloid shell, which is generally disregarded in geometric measurements of such objects. The proposal involves several strategies and procedures for a reliable verification of the structure’s verticality, the detection of signs of ovalisation of the shell, the estimation of the parameters of the structure’s theoretical model, and the analysis of the distribution of the thickness and geometric imperfections of the reinforced-concrete shell. The idea behind the method for determining the actual thickness of the shell (including its variation due to repairs and reinforcement operations), which is generally disregarded when measuring the geometry of such structures, is to estimate the distance between point clouds of the internal and external surfaces of the structure using the M3C2 algorithm principle. As a particularly dangerous geometric anomaly of hyperboloid cooling towers, shell ovalisation is detected with an innovative analysis of the bimodality of the frequency distribution of radial deviations in horizontal cross-sections. The concept of a complete assessment of the geometry of a hyperboloid cooling tower was devised and validated using three measurement series of a structure that has been continuously in service for fifty years. The results are consistent with data found in design and service documents. We identified a permanent tilt of the structure’s axis to the northeast and geometric imperfections of the hyperboloid shell from −0.125 m to +0.136 m. The results also demonstrated no advancing deformation of the hyperboloid shell over a two-year research period, which is vital for its further use. Full article

Soybean (Glycine max (L.) Merr.) ranks as the second-largest crop by total production in the United States, despite its production experiencing significant constraints from plant pathogens, including those causing seedling diseases. Pythium irregulare Buisman stands out as a predominant driver of yield loss associated with the seedling disease complex. There is currently a lack of public or commercial varieties available to growers with adequate genetic resistance to manage this pathogen. To address the pressing need for germplasm resources and molecular markers associated with P. irregulare resistance, we conducted a screening of 208 genetically diverse soybean accessions from the United States Department of Agriculture Soybean Germplasm Collection (USDA-SGC) against two geographically and temporally distinct isolates under controlled greenhouse conditions. Disease severity was assessed through comparisons of the root weight and stand count ratios of inoculated plants to mock-inoculated controls. Employing linear mixed modeling, we identified ten accessions (PI 548520, PI 548360, PI 548362, PI 490766, PI 547459, PI 591511, PI 547460, PI 84946-2, PI 578503, FC 29333) with resistance significantly above the population average to one or both of two isolates originating from Ohio or Indiana. Previously curated genotyping data, publicly accessible via the SoyBase database, was subsequently utilized for conducting a genome-wide association study. This analysis led to the discovery of two significant marker–trait associations (MTAs) located on chromosomes 10 and 15 and accounting for 9.3% and 17.2% of the phenotypic variance, respectively. The resistant germplasm and MTAs uncovered through this study provide additional resources and tools for the genetic improvement of soybean resistance to seedling disease caused by P. irregulare. Full article

Precise soil water content (SWC) measurement is crucial for effective water resource management. This study utilizes the Cosmic-Ray Neutron Sensor (CRNS) for area-averaged SWC measurements, emphasizing the need to consider all hydrogen sources, including time-variable plant biomass and water content. Near Mead, Nebraska, three field sites (CSP1, CSP2, and CSP3) growing a maize–soybean rotation were monitored for 5 (CSP1 and CSP2) and 13 (CSP3) years. Data collection included destructive biomass water equivalent (BWE) biweekly sampling, epithermal neutron counts, atmospheric meteorological variables, and point-scale SWC from a sparse time domain reflectometry (TDR) network (four locations and five depths). In 2023, dense gravimetric SWC surveys were collected eight (CSP1 and CSP2) and nine (CSP3) times over the growing season (April to October). The N₀ parameter exhibited a linear relationship with BWE, suggesting that a straightforward vegetation correction factor may be suitable (f_b). Results from the 2023 gravimetric surveys and long-term TDR data indicated a neutron count rate reduction of about 1% for every 1 kg m⁻² (or mm of water) increase in BWE. This reduction factor aligns with existing shorter-term row crop studies but nearly doubles the value previously reported for forests. This long-term study contributes insights into the vegetation correction factor for CRNS, helping resolve a long-standing issue within the CRNS community. Full article

Industrial hemp is a continuously expanding crop; however, there has been limited research on its herbicide selectivity and weed control. Pendimethalin, s-metolachlor and aclonifen at 1137.5, 960 and 1800 g a.i. ha⁻¹, respectively, were applied in field experiments in 2022 and 2023 in Greece to study the response of industrial hemp to pre-emergence (PRE) herbicides and record their efficacy on weeds. In 2023, each PRE herbicide was followed by the postemergence application of cycloxydim at 200 g a.i. ha⁻¹ due to infestation of Sorghum halepense. In 2022, retardation in hemp growth was recorded by all PRE herbicide treatments, with there being a slight reduction in stand counts by pendimethalin and s-metolachlor and leaf yellowing by aclonifen in one the experiments. In 2023, no reductions in crop establishment and plant height were recorded, whereas leaf discoloration caused by aclonifen was less evident; cycloxydim did not affect hemp and perfectly controlled S. halepense. Despite the herbicide injury, hemp recovered and succeeded in higher biomass in both experiments at Thessaloniki and in higher seed production in the 2023 Thessaloniki experiment. This study showed that pendimethalin, s-metolachlor and aclonifen can be regarded as potential pre-emergence options with precautions in wet and light soils. Full article

The notable absence of juvenile Pseudolarix amabilis trees in forest understories suggests their vulnerability to ecological niche competition, leading to limited survival prospects. This study examines the key factors limiting the growth of P. amabilis seedlings by investigating the effects of five ecological factors: light intensity, rainfall, groundwater level, soil type, and type of fertilization, on the growth of one-year-old P. amabilis seedlings. Our results demonstrate that increasing the light intensity promotes plant growth by augmenting the leaf count, leaf biomass, plant height, stem biomass, root biomass, and total biomass. Further analysis reveals that increased light intensity influences biomass allocation, reducing the specific leaf area and leaf–stem biomass ratio, and favoring root and stem growth over leaf investment. Rainfall, groundwater level, fertilization type, and rhizosphere soil type primarily influence root growth by impacting the soil’s physicochemical properties. Specifically, rising groundwater levels lower the soil temperature and increase the soil moisture, total potassium content, and soil pH, leading to reductions in root biomass, plant height, net height increment, leaf number, and total biomass. When groundwater levels reach 21 cm and 28 cm, submerging the surface soil layer, root biomass decreases by 1.6 g/plant (−51.6%) and 2.3 g/plant (−74.2%), respectively. Further analysis reveals a gradual decrease in the root–shoot ratio above the 14 cm groundwater level, while the specific leaf area and leaf–stem biomass ratio remains unaffected, indicating stronger belowground root stress compared to aboveground stem and leaf components. The results highlight light intensity as the key ecological factor determining the growth of P. amabilis seedlings. These findings underscore the importance of considering light intensity in the management of natural stands, the cultivation of artificial forests, and the nursery cultivation of endangered P. amabilis. Full article

Plant counting is an important part in precision agriculture (PA). The Unmanned Aerial Vehicle (UAV) becomes popular in agriculture because it can capture data with higher spatiotemporal resolution. When it is equipped with multispectral sensors, more meaningful multispectral data is obtained for plants’ analysis. After tobacco seedlings are raised, they are transplanted into the field. The counting of tobacco plant stands in the field is important for monitoring the transplant survival rate, growth situation, and yield estimation. In this work, we adopt the object detection (OD) method of deep learning to automatically count the plants with multispectral images. For utilizing the advanced YOLOv8 network, we modified the architecture of the network to adapt to the different band combinations and conducted extensive data pre-processing work. The Red + Green + NIR combination obtains the best detection results, which reveal that using a specific band or band combinations can obtain better results than using the traditional RGB images. For making our method more practical, we designed an algorithm that can handling the image of a whole plot, which is required to be watched. The counting accuracy is as high as 99.53%. The UAV, multispectral data combined with the powerful deep learning methods show promising prospective in PA. Full article

Phenotyping is one of the most important processes in modern breeding, especially for maize, which is an important crop for food, feeds, and industrial uses. Breeders invest considerable time in identifying genotypes with high productivity and stress tolerance. Plant spacing plays a critical role in determining the yield of crops in production settings to provide useful management information. In this study, we propose an automated solution using unmanned aerial vehicle (UAV) imagery and deep learning algorithms to provide accurate stand counting and plant-level spacing variabilities (PSV) in order to facilitate the breeders’ decision making. A high-resolution UAV was used to train three deep learning models, namely, YOLOv5, YOLOX, and YOLOR, for both maize stand counting and PSV detection. The results indicate that after optimizing the non-maximum suppression (NMS) intersection of union (IoU) threshold, YOLOv5 obtained the best stand counting accuracy, with a coefficient of determination (R²) of 0.936 and mean absolute error (MAE) of 1.958. Furthermore, the YOLOX model subsequently achieved an F1-score value of 0.896 for PSV detection. This study shows the promising accuracy and reliability of processed UAV imagery for automating stand counting and spacing evaluation and its potential to be implemented further into real-time breeding decision making. Full article