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Fluconazole-resistant clade 4 Candida tropicalis causing candidemia in humans has been detected in tropical/subtropical areas, including those in China, Singapore, and Australia. We analyzed 704 individual yeasts isolated from fruits, soil, water, and farmers at 80 orchards in Taiwan. The most common pathogenic yeast species among 251 isolates recovered from farmers were Candida albicans (14.7%) and C. parapsilosis (11.6%). In contrast, C. tropicalis (13.0%), C. palmioleophila (6.6%), and Pichia kudriavzevii (6.0%) were prevalent among 453 environmental isolates. Approximately 18.6% (11/59) of C. tropicalis from the environment were resistant to fluconazole, and 81.8% (9/11) of those belonged to the clade 4 genotype. C. tropicalis susceptibility to fluconazole correlated with susceptibilities to the agricultural azole fungicides, difenoconazole, tebuconazole, and triadimenol. Tandem gene duplications of mutated ERG11 contributed to azole resistance. Agriculture environments are a reservoir for azole-resistant C. tropicalis; discontinuing agricultural use of azoles might reduce emergence of azole-resistant Candida spp. strains in humans. [ABSTRACT FROM AUTHOR]

ABSTRACT Xylosandrus crassiusculus (Motschulsky) and Xylosandrus germanus (Blandford) (Coleoptera: Curculionidae: Scolytinae) are major ambrosia beetle pests in tree nut and fruit orchards and ornamental nurseries in the eastern United States (USA). Ethanol‐baited bottle traps and ethanol‐infused tree stem sections (i.e., bolts) have been used to monitor ambrosia beetles, but limited studies exist on the influence of ethanol‐lure release rate on ambrosia beetle trap captures and bolt attacks. We designed this study to compare low‐release (LR) and high‐release (HR) ethanol lures in bottle traps for capturing invasive ambrosia beetles. We also compared beetle attacks among bolts pre‐soaked in ethanol solutions of low (10%) and high (90%) concentrations and bolts cored and filled with the same low and high ethanol concentrations. In 2022, experiments were conducted in ornamental nurseries and apple, peach, or pecan orchards in five USA states. Higher numbers of X. crassiusculus and X. germanus were captured in bottle traps baited with the HR ethanol lure compared to the LR lure at most of the study sites. More attacks per bolt by X. crassiusculus and X. germanus were observed at most sites on pre‐soaked and filled bolts with 90% compared to 10% solutions of ethanol. Bolts soaked in low (10%) ethanol solutions sustained more attacks from both X. crassiusculus and X. germanus than cored bolts filled with low ethanol. These results will assist with monitoring the flight activity of invasive ambrosia beetles within nut, fruit, and ornamental tree crops. [ABSTRACT FROM AUTHOR]

Automated technologies in precision agriculture enable unmanned systems to precisely target areas with chemicals through controlled nozzle movements. Quantitative assessment of these sprayers can enhance spraying strategies, catering to different canopy sizes, row spacing and coverage objectives. This research assessed an unmanned sprayer equipped with pan-tilt nozzles for targeted area control and spray coverage adjustment. The spray cloud path on the canopy, as the nozzles moved vertically and the sprayer advanced, was simulated mathematically. A model was developed to determine the swing angle based on orchard/vineyard geometrical parameters. This model was then applied in field tests in a vineyard and an apple orchard. Various nozzle-heading angles, driving speeds, and flow rates were experimented with, using average coverage and droplet density as the evaluation criterion. The findings showed that the developed model offered an effective method for determining the swing angles. Lowering driving speeds and increasing flow rates were found to notably enhance coverage. A 45º nozzle-heading angle proved more effective in vineyards, whereas a 90º angle yielded better results in apple orchards, reflecting the variations in canopy size and row spacing. The unmanned sprayer demonstrated great potential for autonomous spraying in vineyards and orchards. [ABSTRACT FROM AUTHOR]

Plum curculio, Conotrachelus nenuphar (Herbst) (Coleoptera: Curculionidae), is a key pest in Southeastern peach production by infesting fruit and decreasing yield. In Northeastern apples, plum curculio was found to have an "edge effect," where more plum curculio are present next to a forested border than in the center of an orchard, and their propensity to fly or walk depended on air temperature. We conducted field studies over 3 seasons (2019–2021) to investigate whether plum curculio in small Southeastern peach plots exhibits the edge effect and to determine its primary mode of movement (flying or walking). Our results revealed that plum curculio did not exhibit the edge effect in Southeastern peaches. Thus, unlike Northeastern apples where plum curculio exhibits the edge effect, the reduced-input application program where insecticide sprays mainly target a few perimeter-row trees instead of the whole orchard for plum curculio management is not recommended for Southeastern peaches. Additionally, we observed that plum curculio in Southeastern peaches did not exhibit a primary mode of movement, and in most of the sampling weeks, the numbers of flying and walking plum curculio were not significantly correlated in the field. These results emphasize that using plum curculio sampling tools that only capture flying or walking plum curculio is not ideal for monitoring plum curculio activity in the Southeast. Overall, our findings indicate that plum curculio in Southeastern small peach plots and Northeastern apples does not exhibit the same behavior (i.e. edge effect and propensity to fly or walk). [ABSTRACT FROM AUTHOR]

A range of remote sensing platforms provide high spatial and temporal resolution insights which are useful for monitoring vegetation growth. Very few studies have focused on fruit orchards, largely due to the inherent complexity of their structure. Fruit trees are mixed with inter-rows that can be grassed or non-grassed, and there are no standard protocols for ground measurements suitable for the range of crops. The assessment of biophysical variables (BVs) for fruit orchards from optical satellites remains a significant challenge. The objectives of this study are as follows: (1) to address the challenges of extracting and better interpreting biophysical variables from optical data by proposing new ground measurements protocols tailored to various orchards with differing inter-row management practices, (2) to quantify the impact of the inter-row at the Sentinel pixel scale, and (3) to evaluate the potential of Sentinel 2 data on BVs for orchard development monitoring and the detection of key phenological stages, such as the flowering and fruit set stages. Several orchards in two pedo-climatic zones in southeast France were monitored for three years: four apricot and nectarine orchards under different management systems and nine cherry orchards with differing tree densities and inter-row surfaces. We provide the first comparison of three established ground-based methods of assessing BVs in orchards: (1) hemispherical photographs, (2) a ceptometer, and (3) the Viticanopy smartphone app. The major phenological stages, from budburst to fruit growth, were also determined by in situ annotations on the same fields monitored using Viticanopy. In parallel, Sentinel 2 images from the two study sites were processed using a Biophysical Variable Neural Network (BVNET) model to extract the main BVs, including the leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and fraction of green vegetation cover (FCOVER). The temporal dynamics of the normalised FAPAR were analysed, enabling the detection of the fruit set stage. A new aggregative model was applied to data from hemispherical photographs taken under trees and within inter-rows, enabling us to quantify the impact of the inter-row at the Sentinel 2 pixel scale. The resulting value compared to BVs computed from Sentinel 2 gave statistically significant correlations (0.57 for FCOVER and 0.45 for FAPAR, with respective RMSE values of 0.12 and 0.11). Viticanopy appears promising for assessing the PAI (plant area index) and FCOVER for orchards with grassed inter-rows, showing significant correlations with the Sentinel 2 LAI (R2 of 0.72, RMSE 0.41) and FCOVER (R2 0.66 and RMSE 0.08). Overall, our results suggest that Sentinel 2 imagery can support orchard monitoring via indicators of development and inter-row management, offering data that are useful to quantify production and enhance resource management. [ABSTRACT FROM AUTHOR]

The nitrogen content of apple leaves and jujube leaves is an important index to judge the growth and development of apple trees and jujube trees to a certain extent. The prediction performance of the two samples was compared between different models for leaf nitrogen content, respectively. The near-infrared absorption spectra of 287 apple leaf samples and 192 jujube leaf samples were collected. After eliminating the outliers by Mahalanobis distance method, the remaining spectral data were processed by six different preprocessing methods. BP neural network (BP), random forest regression (RF), least partial squares (PLS), K-Nearest Neighbor (KNN), and support vector regression (SVR) were compared to establish prediction models of nitrogen content in apple leaves and jujube leaves. The results showed that the determination coefficient (R2), root mean square error (RMSE) and residual prediction deviation (RPD) of the models established by different combined pretreatment methods were compared among the five methods. Compared with the performance of the other four models, the modeling method of SG + SD + CARS + RF was suitable for the prediction of nitrogen content in apple leaves, and its modeling set R2 was 0.85408, RMSE was 0.082188, and RPD was 2.5864. The validation set R2 is 0.75527, RMSE is 0.099028, RPD is 2.1956. The modeling method of FD + CARS + PLS was suitable for the prediction of nitrogen content in jujube leaves. The modeling set R2 was 0.7954, RMSE was 0.14558, and RPD was 2.4264; the validation set R2 is 0.81348, RMSE is 0.089217, and RPD is 2.4552.In the prediction modeling of apple leaf nitrogen content in the characteristic band, the model quality of RF was better than the other four prediction models. The model quality of PLS in predictive modeling of nitrogen content of jujube leaves in characteristic bands is superior to the other four predictive models, These results provide a reference for the use of near-infrared spectroscopy to determine whether apple trees and jujube trees are deficient in nutrients. [ABSTRACT FROM AUTHOR]

Monitoring of Phyllosticta citricarpa (causal agent of citrus black spot [CBS]) inoculum in orchards has previously been performed using volumetric spore traps. However, volumetric traps are nonspecific, and only capture ascospores (not pycnidiospores) of different Phyllosticta species. This research aimed to monitor and quantify the DNA of P. citricarpa using young citrus plants as a spore trap combined with quantitative PCR (qPCR), as well as quantify the number of ascospores and pycnidiospores, and to correlate results with weather variables. Citrus nursery plants were placed as traps under and outside tree canopies during fruit developmental stages (from October to July) in two seasons in commercial ‘Valencia’ sweet orange orchards. DNA was extracted from trap leaves to quantify P. citricarpa inoculum by qPCR of the ribosomal internal transcribed spacer 1 (ITS1 rRNA) region of P. citricarpa (Pc‐ITS). Correlations of Pc‐ITS to rainy days, leaf wetness and temperature were performed. Overall, the highest numbers of 400 Pc‐ITS/cm2 of leaf tissue, which represented up to 12 ascospores or pycnidiospores per cm2, were detected on leaves sampled from October to March, regardless of the trap position, season and orchard. Trap plants placed under canopies had up to 20‐fold more Pc‐ITS than those placed outside. Rainy days and leaf wetness were the variables most positively correlated with Pc‐ITS. Both results in the fluctuation of P. citricarpa inoculum in orchards and the most favourable weather variables associated with inoculum production contribute to better understanding of the critical periods for CBS management in citrus‐growing areas. [ABSTRACT FROM AUTHOR]

Introduction: The relationships among microelements and soil microbial communities are essential for understanding the maintenance of soil's ecological functions and their effects on fruit quality in orchards. However, these relationships have not been adequately studied, despite the importance of microelements for the growth of microorganisms and plants. Methods: To address this research gap, we investigated the relationships among microelements (K, Ca, Na, Mg, Fe, Mn, Zn, and Cu), the diversity and composition of soil microbiomes, and fruit quality in loquat orchards. Results: We found that microelements explained more variations in microbial community structures than geographic position, basic soil properties, and macroelements, with 19.6-42.6% of bacterial, 4.3-27.7% of fungal, and 5.9- 18.8% of protistan genera significantly correlated with microelements. Among the microelements, AMg and ACu were the most influential in determining the soil microbiome. The soil microbes exhibited varied threshold values for environmental breadth among the microelements, with the broadest range for AMg and the narrowest for AZn. Additionally, the microbes showed significant phylogenetic signals for all microelements, with an increasing divergence of soil microelements. The dominant community assembly shifted from homogeneous selection to stochastic, and then to heterogeneous selection. Moreover, microelements and the microbiome were the top two factors individually explaining 11.0 and 11.4% of fruit quality variation, respectively. Discussion: These results highlight the importance of microelement fertilization in orchard management and provide scientific guidance for improving fruit quality. [ABSTRACT FROM AUTHOR]

A typical orchard's mechanical operation consists of three or four stages: lining and digging for plantation, moving the seedling from nurseries to the farm, moving the seedling to the planting hole, and planting the seedling in the hole. However, the digging of the planting hole is the most time-consuming operation. In fruit orchards, the use of robots is increasingly becoming more prevalent to increase operational efficiency. They offer practical and effective services to both industry and people, whether they are assigned to plant trees, reduce the use of chemical fertilizers, or carry heavy loads to relieve staff. Robots can operate for extended periods of time and can be highly adept at repetitive tasks like planting many trees. The present study aims to identify the locations for planting trees in orchards using geographic information systems (GISs), to develop an autonomous drilling machine and use the developed robot to open planting holes. There is no comparable study on autonomous hole planting in the literature in this regard. The agricultural mobile robot is a four=wheeled nonholonomic robot with differential steering and forwarding capability to stable target positions. The designed mobile robot can be used in fully autonomous, partially autonomous, or fully manual modes. The drilling system, which is a y-axis shifter driven by a DC motor with a reducer includes an auger with a 2.1 HP gasoline engine. SOLIDWORKS 2020 software was used for designing and drawing the mobile robot and drilling system. The Microsoft Visual Basic.NET programming language was used to create the robot navigation system and drilling mechanism software. The cross-track error (XTE), which determines the distances between the actual and desired holes positions, was utilized to analyze the steering accuracy of the mobile robot to the drilling spots. Consequently, the average of the arithmetic means was determined to be 4.35 cm, and the standard deviation was 1.73 cm. This figure indicates that the suggested system is effective for drilling plant holes in orchards. [ABSTRACT FROM AUTHOR]

Simple Summary: Thrips constitute one of the main nectarine pests, with damage either in flowering or before harvesting (silvering). Nectarine orchards under organic and integrated management were sampled in Lleida (Baix Segre intensive production area) and Girona at four key moments of the season (bud burst, full flowering, fruit setting stage, and colorization of the fruit) during 2021 and 2022 to determine the species composition and damage during fruit maturation. Fifteen species were collected in Lleida, and 10 species were collected in Girona. Organic orchards in Lleida showed lower populations and silvering damage levels than integrated orchards. Thrips fuscipennis Haliday (Thysanoptera: Thripidae) 1836 was the main species in Lleida during harvest, and Frankliniella occidentalis (Pergande) 1895 was the main species in Girona. Due to their predominance, both species were associated with silvering damage during fruit maturation. Thrips constitute one of the main nectarine pests, with damage either in flowering or before harvesting (silvering). Several species are associated with damage to flowers, but Frankliniella occidentalis (Pergande) (Thysanoptera: Thripidae) is the main species associated with summer damage in Europe. Tree canopies of nectarine orchards under organic and integrated management were sampled in Lleida and Girona at four key moments of the season (bud burst, full flowering, fruit setting stage, and colorization of the fruit) during 2021 and 2022 to determine the species composition in the area and the damage caused during fruit maturation. Adult individuals in flowers, leaves, and fruit surfaces were collected and identified, and silvering damage to the fruit surface was assessed in the Lleida area. Fifteen species in Lleida and 10 species in Girona were collected from the tree canopy. Organic orchards in Lleida showed lower populations and silvering damage levels when compared with integrated orchards. Thrips fuscipennis Haliday (Thysanoptera: Thripidae) 1836 was the main species in Lleida during harvest, and Frankliniella occidentalis (Pergande) 1895 was the main species in Girona. Due to their predominance, both species were associated with silvering damage during fruit maturation. [ABSTRACT FROM AUTHOR]

The Leaf Area Index (LAI) strongly influences vegetation evapotranspiration and photosynthesis rates. Timely and accurately estimating the LAI is crucial for monitoring vegetation growth. The unmanned aerial vehicle (UAV) multispectral digital camera platform has been proven to be an effective tool for this purpose. Currently, most remote sensing estimations of LAIs focus on cereal crops, with limited research on economic crops such as apples. In this study, a method for estimating the LAI of an apple orchard by extracting spectral and texture information from UAV multispectral images was proposed. Specifically, field measurements were conducted to collect LAI data for 108 sample points during the final flowering (FF), fruit setting (FS), and fruit expansion (FE) stages of apple growth in 2023. Concurrently, UAV multispectral images were obtained to extract spectral and texture information (Gabor transform). The Support Vector Regression Recursive Feature Elimination (SVR-REF) was employed to select optimal features as inputs for constructing models to estimate the LAI. Finally, the optimal model was used for LAI mapping. The results indicate that integrating spectral and texture information effectively enhances the accuracy of LAI estimation, with the relative prediction deviation (RPD) for all models being greater than 2. The Categorical Boosting (CatBoost) model established for FF exhibits the highest accuracy, with a validation set R2, root mean square error (RMSE), and RPD of 0.867, 0.203, and 2.482, respectively. UAV multispectral imagery proves to be valuable in estimating apple orchard LAIs, offering real-time monitoring of apple growth and providing a scientific basis for orchard management. [ABSTRACT FROM AUTHOR]

Pyroxasulfone is a very long chain fatty acid (VLCFA) inhibitor (group 15) with potential for preemergence weed control in orchard crops and would be beneficial for reducing selection pressure for resistance to more commonly used herbicides, such as glyphosate. Pyroxasulfone is registered and widely used in corn and soybeans in the United States as a solo product and in several premix formulations. However, there is limited published information on the performance and safety of pyroxasulfone in tree nut orchard systems. To assess the potential fit for pyroxasulfone in this crop sector, a series of crop safety and weed control efficacy experiments were carried out in key California orchard crops and in fallow fields with common orchard weeds. The non-crop studies were conducted near Davis, CA, to evaluate winter annual weed control with pyroxasulfone in comparison with pendimethalin and indaziflam. In addition, an irrigation incorporation experiment was conducted in summer to evaluate the effects of incorporation timing on pyroxasulfone, pendimethalin, and indaziflam efficacy as a measure of relative stability. Orchard weed control experiments were conducted in spring in an almond orchard near Arbuckle, CA, and a walnut orchard near Davis, CA. The orchard experiments included pyroxasulfone, pendimethalin, and indaziflam in various rate combinations with glufosinate. To evaluate crop safety, 2-year experiments were conducted to evaluate repeated applications of above-label rates of pyroxasulfone at 1199 g⋅ha-1 and S-metolachlor at 14,010 g⋅ha-1 on 1- to 2-year-old almond, pistachio, and walnut trees near Davis. Pyroxasulfone performed similarly to commercial standards with up to 95% control of broadleaf and grass weeds with pyroxasulfone and indaziflam providing 96% to 100% control of annual bluegrass, and pyroxasulfone (293 g⋅ha-1) being the only herbicide to suppress (>70%) common lambsquarters at 60 days after treatment (DAT). Other weeds controlled by pyroxasulfone include swinecress, redroot pigweed, yellow nutsedge, and Italian ryegrass. No differences were found among treatments in the incorporation timing study indicating adequate stability on the soil surface under summer conditions. Crop injury was not observed in the established orchards or the young orchard studies and there were no treatment effects on tree trunk diameter of almond, pistachio, and walnut in the 2-year crop safety experiments. These results indicate a potential for pyroxasulfone in California tree nut orchard systems that would be an additional site of action and beneficial for management of herbicide-resistant weeds in these crops. [ABSTRACT FROM AUTHOR]

Tower sprayers play a vital role in agriculture, being designed to apply pesticides and fertilizers effectively. The dynamics of this equipment, especially the vibration of the tower, play a crucial role in the success of agricultural operations. Analyzing and understanding this vibration is critical to ensuring application accuracy while minimizing variations in chemical distribution. This understanding directly contributes to uniform plant coverage, improving spraying efficiency and reducing input losses. In addition, practical vibration analysis extends the life of tower components, optimizes operational safety, and promotes energy efficiency by reducing fuel consumption. Tower stability becomes crucial to prevent spills and ensure efficient use of available resources. In short, dynamics and vibration analysis in tower sprayers are critical elements for the precise, efficient, and safe application of agricultural inputs. This approach promotes sustainable practices and contributes significantly to the success of farming operations. As a result of this study, a 23.68% reduction in trailer vibration amplitude was achieved. [ABSTRACT FROM AUTHOR]

Super-high density olive orchards may increase profitability for table olive producers. However, water needs and fruit damage could limit their viability. Deficit irrigation scheduling would reduce the amount of water required, but rehydration before harvest is extremely important. The aim of this work was to compare a typical deficit farm management model with a regulated deficit irrigation one based on the plant water status. The experiment was carried out during three seasons in a three years-old (4 × 1.5 m) commercial table olive orchard. In both irrigation scheduling plots, seasonal amount of applied water was similar. Irrigation treatments were: common farm management (CFM), an almost constant rate of irrigation, and Regulated deficit (RDI) based on midday shaded water potential. Vegetative measurements suggested that RDI increased the external surface of the hedge, and this was related with greater yield in this treatment. Significant differences were found in of fruit size at harvest in some seasons, with larger fruits in RDI compared to CFM. However, fruit damage occurred during mechanical harvest in both irrigation managements, despite the higher skin firmness in RDI, and shows the need to carry out postharvest treatments to improve the quality of the final product. Accurate control of the tree water status would allow optimizing the amount of water available for table olive orchards. [ABSTRACT FROM AUTHOR]

Kiwifruit (Actinidia chinensis var. chinensis) firmness is routinely measured in a destructive manner for decision-making purposes. Thus, a population's quality is inferred by measuring a sample from that population. Consequently, studies have investigated non-destructive techniques for measuring fruit firmness. However, most of these studies have been restricted to a single season or focused on performance over long-term storage. This work compared non-destructive compression (1 mm deformation) and acoustic stiffness with flesh firmness measured with a penetrometer across three seasons. 'SunGold' kiwifruit were harvested from 11, 9 and 3 orchards on multiple occasions in 2020, 2021 and 2022, respectively. Kiwifruit was freighted to Palmerston North and assessed on arrival. Thirty fruit per orchard were measured on lab arrival, whilst 24 fruit per orchard were stored for two weeks at 0°C prior to assessment. The non-destructive methods had a strong (r2 > 0.89–0.92) segmented correlation with flesh firmness (0.52–10 kgf). Flesh firmness could be adequately estimated with the non-destructive methods within a season. However, segmented regression performance was reduced when predicting for a season outside of the training population. Nonetheless, these non-destructive methods may be useful for estimating flesh firmness at harvest and after short-term storage (2 weeks at 0 °C). [ABSTRACT FROM AUTHOR]

In the deciduous fruit industry, orchards are often excavated and trees chipped. The organic material is then used as mulch for soil conservation, a practice that form part of sustainable agricultural. The presence and possible transmission of plant pathogens are not considered when trees are removed, chipped and used for mulch. Young apple trees can develop cankers due to Diplodia seriata of which the inoculum source might come from fruiting structures present on apple wood mulch. Therefore, the presence of D. seriata, on chipped apple tree wood pieces used for mulch in younger orchards was investigated. To be able to detect D. seriata, qPCR primers were designed from a previously identified unique sequence characterised amplified region (SCAR). The qPCR primers were specific for D. seriata (Cq ≤ 35 and Tm = 85 0.17 °C) when compared with DNA from nineteen other fungal taxa associated with canker or wood rot of apple trees tested, excluding Botryosphaeria dothidea (Cq = 38 and Tm = 85.25 °C). The qPCR assay was sensitive and had a limit of quantification of 2859 fg/µl and limit of detection of 571 fg/µl. Wood chips were collected at two time periods (from heaps and 6 months after it was spread in tree rows) in three apple orchards in the Western Cape of South Africa. DNA was extracted from water-washes of 120 wood piece samples and D. seriata was detected from 101 of these samples. This study showed that the newly developed primers was able to successfully detect D. seriata from mulched apple wood. The presence of D. seriata on apple tree wood chips indicates that there is a risk involved in using wood chips made from old apple trees. [ABSTRACT FROM AUTHOR]

With the development of agriculture, the complexity and dynamism of orchard environments pose challenges to the perception and positioning of inter-row environments for agricultural vehicles. This paper proposes a method for extracting navigation lines and measuring pedestrian obstacles. The improved YOLOv5 algorithm is used to detect tree trunks between left and right rows in orchards. The experimental results show that the average angle deviation of the extracted navigation lines was less than 5 degrees, verifying its accuracy. Due to the variable posture of pedestrians and ineffective camera depth, a distance measurement algorithm based on a four-zone depth comparison is proposed for pedestrian obstacle distance measurement. Experimental results showed that within a range of 6 m, the average relative error of distance measurement did not exceed 1%, and within a range of 9 m, the maximum relative error was 2.03%. The average distance measurement time was 30 ms, which could accurately and quickly achieve pedestrian distance measurement in orchard environments. On the publicly available TUM RGB-D dynamic dataset, YOLOD-SLAM2 significantly reduced the RMSE index of absolute trajectory error compared to the ORB-SLAM2 algorithm, which was less than 0.05 m/s. In actual orchard environments, YOLOD-SLAM2 had a higher degree of agreement between the estimated trajectory and the true trajectory when the vehicle was traveling in straight and circular directions. The RMSE index of the absolute trajectory error was less than 0.03 m/s, and the average tracking time was 47 ms, indicating that the YOLOD-SLAM2 algorithm proposed in this paper could meet the accuracy and real-time requirements of agricultural vehicle positioning in orchard environments. [ABSTRACT FROM AUTHOR]

Soil microorganisms play a crucial role in maintaining soil fertility sensu lato. Sustainable soil management aims to make the soil environment more hospitable increasing microorganism diversity and complexity by means of the minimal disturbance of soil and inputs of organic material. This results in the effective functioning of agricultural systems, better crop productivity, and a reduction in environmental impacts. A study was carried out to evaluate the effects of the long-term application (more than 15 years) of sustainable practices versus conventional ones on soil microbial biomass activity and its functional diversity within different Mediterranean commercial fruit orchards located in Southern Italy. A preliminary survey—performed using the electromagnetic induction technique (EMI)—guided the collection of representative soil samples by reducing the spatial heterogeneity of soil microorganisms. Soil management practices, based on no tillage and the recycling of organic materials of different origins and quality produced within the orchard, increased soil organic carbon, telluric microorganisms activity and their functional diversity compared to 'non-conservative' management methods such as continuous tillage. In addition, the rational use of the orchard-inside organic matter (natural/seeded grass cover and pruning material) allowed it to virtuously transform from useless waste into precious resources, eliminating the logistical and economic constraints for their disposal. The simultaneous use of different types of soil management strategies aimed at soil conservation reinforced the positive effects on the microbiological indicators of soil quality rather than the application of a single strategy. This study provides the opportunity to represent what could be the possible evolution of tilled orchards towards more balanced soil conditions when subjected to conservative practices, offering a reference model for fruit growers and technicians who want to improve the stability and the resiliency of their agrosystems. [ABSTRACT FROM AUTHOR]

This study identified and monitored the population fluctuations of insect vectors of Cowpea aphid-borne mosaic virus (CABMV) in experimental orchards in the North and Northwest Regions of the State of Paraná. The experiments were conducted in the cities of Londrina and Paranavaí at the Experimental Research Stations of the Instituto de Desenvolvimento Rural do Paraná-IAPAR-EMATER (IDR-Paraná), Brazil. The study period was from September 2019 to March 2021. In the experiments conducted in Londrina and in Paranavaí, yellow passion fruit plants were grown in trellis systems. Moericke traps were used to capture insects, and collections were performed weekly. Aphids were identified using a taxonomic identification key. The numbers of aphids present in each region were compared, and the populations were correlated with the climate and local precipitation. A total of 1522 and 1340 winged aphids were observed in the experiments in Londrina and Paranavaí, respectively. In Londrina, moderate positive correlations were observed between the number of insects and the maximum and average temperatures, while negative correlations were observed between the number of insects and the minimum temperature and precipitation. In contrast, in Paranavaí, the correlation between aphids and abiotic factors was negative, where maximum temperature was strongly correlated. In both regions, the following seven species of aphid vectors of CABMV were detected: Aphis fabae, Aphis gossypii, Toxoptera citricida, Acyrthosiphon pisum, Brevicoryne brassicae, Macrosiphum euphorbiae and Uroleucon ambrosiae. The population fluctuations of these species were influenced by variations in temperature and precipitation. [ABSTRACT FROM AUTHOR]

Fire blight is an infectious disease found in apple and pear orchards. While managing the disease is critical to maintaining orchard health, identifying symptoms early is a challenging task which requires trained expert personnel. This paper presents an inspection technique that targets individual symptoms via deep learning and density estimation. We evaluate the effects of including multi-spectral sensors in the model's pipeline. Results show that adding near infrared (NIR) channels can help improve prediction performance and that density estimation can detect possible symptoms when severity is in the mid-high range. [ABSTRACT FROM AUTHOR]

Summer pruning is an important horticultural operation that is performed to manipulate tree response so as to make it better manageable without a reduction in yield and fruit quality. But the timing is also an important factor in performing this event. Keeping in view the above facts, the present investigation was carried out to elucidate the ‘differential response of summer pruning timing on cropping behavior in low-chill peach cvs. Florida Prince and Shan-i-Punjab’. The experiment was carried out at the experimental orchard of the Department of Fruit Science, College of Horticulture and Forestry, Neri, Hamirpur (HP) during the years 2018–2019 and 2019–2020. There are six treatments in the experiment, each with four replications. T1: no pruning; T2: 25% summer pruning on July 10th + winter pruning (WP); T3: 25% summer pruning on July 20th +WP; T4: 25% summer pruning on July 30th +WP; T5: 25% summer pruning on August 10th +WP); and T6: standard winter pruning (SWP). According to the study’s findings, combining 25% summer pruning on July 10th with winter pruning resulted in the greatest improvement in terms of vegetative, flowering or blooming, and qualitative parameters, namely fruit weight, fruit shape index, total soluble solid, and sugar content. However, Florida Prince showed positive enhancement of various characteristics and performed better than Shan-i-Punjab. [ABSTRACT FROM AUTHOR]

High-repetitive features in unstructured environments and frequent signal loss of the Global Navigation Satellite System (GNSS) severely limits the development of autonomous robot localization in orchard settings. To address this issue, we propose a LiDAR-based odometry pipeline GLIO, inspired by KISS-ICP and DLIO. GLIO is based on a nonlinear observer with strong global convergence, effectively fusing sensor data from GNSS, IMU, and LiDAR. This approach allows for many potentially interfering and inaccessible relative and absolute measurements, ensuring accurate and robust 6-degree-of-freedom motion estimation in orchard environments. In this framework, GNSS measurements are treated as absolute observation constraints. These measurements are tightly coupled in the prior optimization and scan-to-map stage. During the scan-to-map stage, a novel point-to-point ICP registration with no parameter adjustment is introduced to enhance the point cloud alignment accuracy and improve the robustness of the nonlinear observer. Furthermore, a GNSS health check mechanism, based on the robot's moving distance, is employed to filter reliable GNSS measurements to prevent odometry crashed by sensor failure. Extensive experiments using multiple public benchmarks and self-collected datasets demonstrate that our approach is comparable to state-of-the-art algorithms and exhibits superior localization capabilities in unstructured environments, achieving an absolute translation error of 0.068 m and an absolute rotation error of 0.856°. [ABSTRACT FROM AUTHOR]

Novel tactics for controlling insect pests in perennial fruit and nut crops are needed because target pests often display decreased susceptibility to chemical controls due to overreliance on a handful of active ingredients and regulatory issues. As an alternative to chemical controls, entomopathogenic fungi could be utilized as biological control agents to manage insect pest populations. However, development of field ready products is hampered by a lack of basic knowledge. Development of field ready products requires collecting, screening, and characterizing a greater variety of potential entomopathogenic fungal species and strains. Creation of a standardized research framework to study entomopathogenic fungi will aid in identifying the potential mechanisms of biological control activity that fungi could possess, including antibiotic metabolite production; strains and species best suited to survive in different climates and agroecosystems; and optimized combinations of entomopathogenic fungi and novel formulations. This mini review therefore discusses strategies to collect and characterize new entomopathogenic strains, test different potential mechanisms of biocontrol activity, examine ability of different species and strains to tolerate different climates, and lastly how to utilize this information to develop strains into products for growers. [ABSTRACT FROM AUTHOR]

Strawberries are a commonly used agricultural product in the food industry. In the traditional production model, labor costs are high, and extensive picking techniques can result in food safety issues, like poor taste and fruit rot. In response to the existing challenges of low detection accuracy and slow detection speed in the assessment of strawberry fruit maturity in orchards, a CR-YOLOv9 multi-stage method for strawberry fruit maturity detection was introduced. The composite thinning network, CRNet, is utilized for target fusion, employing multi-branch blocks to enhance images by restoring high-frequency details. To address the issue of low computational efficiency in the multi-head self-attention (MHSA) model due to redundant attention heads, the design concept of CGA is introduced. This concept aligns input feature grouping with the number of attention heads, offering the distinct segmentation of complete features for each attention head, thereby reducing computational redundancy. A hybrid operator, ACmix, is proposed to enhance the efficiency of image classification and target detection. Additionally, the Inner-IoU concept, in conjunction with Shape-IoU, is introduced to replace the original loss function, thereby enhancing the accuracy of detecting small targets in complex scenes. The experimental results demonstrate that CR-YOLOv9 achieves a precision rate of 97.52%, a recall rate of 95.34%, and an mAP@50 of 97.95%. These values are notably higher than those of YOLOv9 by 4.2%, 5.07%, and 3.34%. Furthermore, the detection speed of CR-YOLOv9 is 84, making it suitable for the real-time detection of strawberry ripeness in orchards. The results demonstrate that the CR-YOLOv9 algorithm discussed in this study exhibits high detection accuracy and rapid detection speed. This enables more efficient and automated strawberry picking, meeting the public's requirements for food safety. [ABSTRACT FROM AUTHOR]

Introduction: The almond tree is a major global nut crop, and its production has surged dramatically in recent years. Super high-density (SHD) planting systems, designed to optimize resource efficiency and enhance precocity, have gained prominence in almond cultivation. A shift in cropping systems toward sustainable intensification (SI) pathways is imperative, and so maximizing branching density within the canopies of SHD trees is crucial to establish and maintain productive potential, especially for hedge-pruned trees. This study investigates the influence of different almond cultivars grafted onto a novel growth-controlling rootstock on tree architectural and growth parameters in a SHD orchard. This open field research provided valuable insights for the development and application of new tools and methods to increase productivity and sustainability in almond growing. Methods: Three cultivars (Lauranne® Avijour, Guara Tuono, and Filippo Cea) were evaluated in Gravina in Puglia (BA) over a two-year period. Canopy growth parameters, such as canopy volume and trunk cross-sectional area, and architectural traits, like branching density, branching angle, number and length of subterminal shoots, and number of brachyblasts, were measured through qualitative and quantitative measurements. Results and discussion: Results revealed significant differences in tree height, canopy thickness, width, volume, and vigor among the cultivars. Architectural traits, including branch parameters, brachyblast parameters, and subterminal shoots, varied among the cultivars. Lauranne displayed a more compact welldistributed canopy and exhibited the lowest vigor. Filippo Cea showed the highest vigor and the greatest canopy volume. Tuono had a higher number of buds and bud density. The best ideotype for SHD orchards is a smaller tree, with high branching density and smaller trunk diameters, i.e. the vigor. Cv. Lauranne seemed to be the best cultivar, mostly with the lowest tree vigor of all the cultivars involved. These findings provide valuable insights for almond growers and breeders seeking to optimize orchard design and management for enhanced SHD orchards productivity and sustainability. Future research will explore the relationship between canopy architecture and yield parameters, considering different scion/rootstock combinations in different environmental conditions. [ABSTRACT FROM AUTHOR]

Summary. White root rot, caused by Dematophora necatrix (syn. Rosellinia necatrix), affects deciduous trees. A D. necatrix infection-distribution survey found widespread disease in apple and cherry orchards in northern Israel bordering a Mediterranean forest, although the forest trees were unaffected. Because cherry and apple orchards must be abandoned due to long fungal survival in infested soils, alternative deciduous fruit trees and grapevines were assessed for growth in these D. necatrix-infested orchards. In the field, grapevine rootstocks and the almond–peach rootstock GF-677 were most tolerant to D. necatrix-infested soil. Apple was the most sensitive crop, with the rootstock Hashabi being more tolerant than PI80 or MM104. The Mediterranean forest tree Pistacia atlantica, which can serve as a rootstock for pistachio, was as sensitive as kiwifruit and apple, and persimmon rootstock sensitivity was not different from grapevine. Those results show that beside the above mentioned crops, vineyards can also replace apple orchards in D. necatrix-infested soils and so broadens the list of possible crops for the local farmers in a Mediterranean climate at altitudes above 440 m above sea level. This has also been observed in a commercial 10-year-old vineyard of ‘Shiraz’ grapevines grafted on SO4 rootstock. The almond-peach rootstock GF-677 can also be grown in infested soil, but in commercial orchards requires additional treatment for adequate disease control. [ABSTRACT FROM AUTHOR]

This study was carried out in peach orchards of Akyamaç, Kemalpaşa and Kömeç villages of Tokat centre in 2023. As a result of the observations made in the orchards of the mentioned villages, damaged peach fruits were cultured in the laboratory. Zaprionus tuberculatus Malloch, 1932 and Zaprionus indianus (Gupta, 1970) (Diptera: Drosophilidae), which are invasive species, were detected as a result of the culture processes. Especially Z. indianus was observed to be intensively reared from the cultures. These species are the first records for the insect fauna of Tokat province. [ABSTRACT FROM AUTHOR]

Brazil is one of the world's largest apple (Malus domestica Borkh: Rosaceae) producers. Throughout the production cycle, the crop is susceptible to infestations of phytophagous mites. The aim of this work was to understand the response of the mite community to cultivars, plant stratum and the management of apple orchards. The study was performed in conventional orchards (Fuji and Gala) and organic (Gala, Fuji and Eva). Monthly collections were carried out over one year. Forty plants were sampled per orchard, being three leaves per plant (apical, median and basal). The material was sorted and identified in the laboratory. A total of 7.193 mites were collected, with organic orchards having a higher average abundance (10.1±3.37) than conventional ones (7.39±1.82). The relative abundance of Aculus schlechtendali Nalepa (Eriophyidae) and Panonychus ulmi (Koch) (Tetranychidae) was higher in the conventional system. Neoseiulus californicus (McGregor) (Phytoseiidae) had higher relative abundance in the conventional system, while the organic had a higher richness of predatory mites. The cultivars influenced the mite community, with Eva having the highest mean abundance (17.7±9.13), followed by Gala (8.96±2.52) and Fuji (5.12±1.25). Regarding the stratum, the apical region had the highest mite abundance (13.3±4.82), followed by the median (6.99±1.86) and basal (5.38±1.30). Organic orchards kept the mite community in balance, conserving richness and diversity, with low populations of phytophagous and a constant and diverse presence of natural enemies. [ABSTRACT FROM AUTHOR]

In order to shorten detection times and improve average precision in embedded devices, a lightweight and high-accuracy model is proposed to detect passion fruit in complex environments (e.g., with backlighting, occlusion, overlap, sun, cloud, or rain). First, replacing the backbone network of YOLOv5 with a lightweight GhostNet model reduces the number of parameters and computational complexity while improving the detection speed. Second, a new feature branch is added to the backbone network and the feature fusion layer in the neck network is reconstructed to effectively combine the lower- and higher-level features, which improves the accuracy of the model while maintaining its lightweight nature. Finally, a knowledge distillation method is used to transfer knowledge from the more capable teacher model to the less capable student model, significantly improving the detection accuracy. The improved model is denoted as G-YOLO-NK. The average accuracy of the G-YOLO-NK network is 96.00%, which is 1.00% higher than that of the original YOLOv5s model. Furthermore, the model size is 7.14 MB, half that of the original model, and its real-time detection frame rate is 11.25 FPS when implemented on the Jetson Nano. The proposed model is found to outperform state-of-the-art models in terms of average precision and detection performance. The present work provides an effective model for real-time detection of passion fruit in complex orchard scenes, offering valuable technical support for the development of orchard picking robots and greatly improving the intelligence level of orchards. [ABSTRACT FROM AUTHOR]

To address the issue of automated apple harvesting in orchards, we propose a YOLOv5-RACF algorithm for identifying apples and calculating apple diameters. This algorithm employs the robot operating dystem (ROS) to control the robot's locomotion system, Lidar mapping, and navigation, as well as the robotic arm's posture and grasping operations, achieving automated apple harvesting and placement. The tests were conducted in an actual orchard environment. The algorithm model achieved an average apple detection accuracy (mAP@0.5) of 98.748% and a (mAP@0.5:0.95) of 90.02%. The time to calculate the diameter of one apple was 0.13 s, with a measurement accuracy within an error range of 1–3 mm. The robot takes an average of 9 s to pick an apple and return to the initial pose. These results demonstrate the system's efficiency and reliability in real agricultural environments. [ABSTRACT FROM AUTHOR]

Apis mellifera Linnaeus (Hymenoptera: Apis), honey bees, are the most widely used managed crop pollinators. However, their high rental cost and uncertain availability for North American orchard crops have motivated growers to explore alternative pollination options. We examined whether adding solitary, spring-flying Osmia lignaria Say (Hymenoptera: Megachilidae), blue orchard bees, as co-pollinators with A. mellifera in Washington sweet cherry and pear orchards enhances fruit set and yield compared to the use of A. mellifera alone. We added managed O. lignaria to orchard sites where A. mellifera hives were already present. Fruit set, fruit yield, and O. lignaria reproduction at O. lignaria -supplemented sites were compared to nearby, paired sites pollinated only by A. mellifera (3 paired cherry and 3 paired pear sites). For both crops, the addition of O. lignaria significantly increased fruit set but did not yield at harvest. Microscopic inspection of pollen grains from O. lignaria nest cell provisions confirmed that O. lignaria primarily visited orchard flowers. Mean retention of O. lignaria in cherry orchards was slightly higher (65%) than O. lignaria retention reported in other orchard crops (30%–60%). However, retention in pear orchards was much lower (≤20%). These results show that supplementing hives with O. lignaria in Washington spring orchard crops can increase overall pollination, but that trees fail to bear developing fruit to maturity. The strategy of using co-pollinators, O. lignaria and A. mellifera , in US orchards may act as "pollination insurance" when A. mellifera hives are in low supply or when the weather is not amenable for A. mellifera flight during the bloom period. [ABSTRACT FROM AUTHOR]

Ambrosia beetles (Coleoptera: Curculionidae: Scolytinae) are among the most devastating pests of orchards, nurseries, and forests. Improving trap design and ethanol lures for capturing ambrosia beetles is necessary to develop effective monitoring and management strategies. In this 2-year study, we assessed 4 trap designs and 3 commercially formulated ethanol lures to refine trapping methods tailored for orchard environments in the eastern United States. Our investigation included orchards in 2 regions, Georgia (pecan orchards) and New York (apple orchards), targeting major ambrosia beetle (Coleoptera: Curculionidae) pest species such as Xylosandrus crassiusculus (Motschulsky), X. compactus (Eichhoff), X. germanus (Blandford), and Anisandrus maiche (Stark). Among the trap designs evaluated, clear sticky cards were most effective for capturing ambrosia beetles across orchard locations. Notably, in Georgia, sticky cards paired with specific low-release ethanol lures demonstrated enhanced capture of X. crassiusculus and X. compactus , 2 key ambrosia beetle pests found infesting young pecan trees. Similarly, in New York, sticky cards baited with low-release ethanol lures captured the highest rates of X. germanus and A. maiche , thus indicating its suitability for diverse ambrosia beetle populations. Overall, our study provides practical implications for tailoring trapping protocols to optimize ambrosia beetle management strategies in orchard settings. [ABSTRACT FROM AUTHOR]

Rio Grande do Sul State produces 70% of pecans in Brazil. There is a high density of phytopathogenic nematodes in orchards. This study characterises the populations of plant-parasitic nematodes and investigates their association with soil properties in pecan-cultivated areas. The genera Mesocriconema, Criconemoides, Discocriconemella, Xiphinema, Tylenchus, Pratylenchus, Helicotylenchus, and Aphelenchoides were all identified. Discocriconemella and Xiphinema were positively associated with soil organic matter, cation exchange capacity, clay content, copper, zinc, calcium, magnesium, and potassium. Conversely, Pratylenchus, Helicotylenchus, Tylenchus, Criconemoides, and Aphelenchoides were positively correlated with aluminium. Mesocriconema, the most commonly found nematode, negatively correlated with most soil chemical attributes. A significant correlation was observed between the genera Criconemoides, Mesocriconema, Aphelenchoides, and Tylenchus. Parasitic nematodes in pecan orchards have not previously been documented in this region of Brazil. [ABSTRACT FROM AUTHOR]

Aureobasidium pullulans, formulated commercially as Blossom Protect, has become a highly successful biological material for fire blight suppression in apple and pear. This material, which is composed of viable spores of two strains of A. pullulans, attained this status with minimal prior knowledge that yeasts could be used effectively for this purpose. In early orchard trials, it was observed that mixing A. pullulans in spray tanks with a low pH buffer enhanced disease suppression from very good to outstanding. Prevention of fire blight by Blossom Protect requires that A. pullulans colonizes most of the flowers on a tree with populations that exceed 1 × 104 CFU/flower. A. pullulans colonizes floral stigmas similar to bacteria used to suppress fire blight biologically; however, unlike these bacteria, the yeast also colonizes the hypanthial surface within the floral cup, which is where the fire blight pathogen Erwinia amylovora infects the host. In combination with the buffer, colonization activity by A. pullulans on the hypanthium reduces pH of the floral cup and strongly induces host defense genes in hypanthial tissue. A specific risk from use of A. pullulans for fire blight control is a potential to contribute to 'russeting' of developing fruitlets. Russeting risk from use of Blossom Protect has been lessened by reformulation of the companion buffer and by use of conventional or organically-approved fungicides in late and post-bloom periods. In large-scale apple production, particularly organic orchards, the use of Blossom Protect requires integration with other sprays that are required for managing crop load and suppression of fungal diseases such as apple scab. [ABSTRACT FROM AUTHOR]

Fire blight, caused by Erwinia amylovora, can be spread through orchards and nurseries because of poor sanitation practices such as contaminated secateurs. This research investigated the efficacy of 12 commercial sterilants at varying concentrations to kill E. amylovora on secateurs. Secateurs were dipped into an Erwinia amylovora inoculum solution (106 colony forming units/mL), and then misted with a test sterilant. After 10 s, secateurs were swabbed and plated onto Kings B agar plate, incubated at 26 °C for 48 h and bacterial colonies counted. Sterilants were also assessed for cotton bleaching and metal corrosion. Best performing sterilants were then tested as described above by cutting through infected plant material containing sticky bacterial ooze as inoculum. Each of the sterilants tested (methylated spirits (95% and 70%), sodium hypochlorite (NaOCl) (1%, 0.5%, 0.135%), Bac-Stop/benzalkonium chloride (2%, 1%), Virkon™ (label rate), Dettol (50%, 10%, 1%, 0.1%), and HarvestCide® gel (0.1%, 0.5%, 1%)) were found to be effective to kill E. amylovora on inoculum-coated secateurs. The best performing sterilants (methylated spirits, Dettol, HarvestCide gel, NaOCl) were also effective in killing E. amylovora on infected plant material when compared with the untreated control. Most sterilants, except methylated spirits or Dettol, caused corrosion of metal and bleaching of cotton. Each of the tested sterilants were found to be effective to kill Erwinia amylovora on inoculum-coated secateurs and inoculum in plant material and bacterial ooze. Many of the best performing sterilants were likely to damage tools over time and cause bleaching on clothing. However, Dettol or methylated spirits did not cause metal corrosion or bleaching. [ABSTRACT FROM AUTHOR]

The hedgerow growing system is prevalent in new olive orchards worldwide due to its fully mechanized harvesting. Several works have been published to compare cultivars planted in this system, focusing on productivity and oil composition. However, little research has been conducted on the long-term evaluation of cultivars' growth habits when trained in hedgerow systems and on how it affects their interannual productivity. In this work, we report the canopy growth habit, productivity, and their correlation for the 'Arbequina', 'Arbosana', 'Koroneiki', 'Lecciana', 'Oliana', and 'Sikitita' cultivars grown in a hedgerow system in Extremadura, central-western Spain, for 9 years. 'Koroneiki', 'Arbequina', and 'Lecciana' were the cultivars with the highest canopy growth, both in young and adult trees, and the ones with the highest pruning needs from 5 to 10 years after planting. The yield behavior in each of the years evaluated was stable in all cultivars except 'Lecciana'. This alternate bearing was associated with the distribution of total yearly produced biomass between fruits and vegetative growth. 'Oliana', 'Arbosana', and 'Sikitita' were the cultivars with the highest proportion of fruit of the total biomass, and 'Lecciana' showed the lowest. This study indicates that cultivars with higher fruit proportions of total biomass might have better suitability for long-term growing in hedgerow formation, fewer pruning needs, and more stable productivity across the years. In this sense, in the climatic conditions considered here, 'Arbosana', 'Sikitita', and 'Oliana' could be the most suitable cultivars for this growing system. [ABSTRACT FROM AUTHOR]

Carbon farming practices are pivotal for enhancing soil organic carbon (SOC) storage in agricultural systems. This study focuses on evaluating the effects of spontaneous cover crops as a conservation strategy compared to conventional management practices on total, non-protected, and protected SOC fractions, as well as carbon saturation, in olive groves across 13 paired sites (26 sites in total) in Andalucía, Spain. The research evaluates organic carbon concentrations in different soil fractions: non-protected (250–2000 µm), physically protected (53–250 µm), and chemically protected (<53 µm). The results reveal that olive groves managed with temporary spontaneous cover crops (CC) over the last 8–12 years generally exhibit higher SOC concentrations compared to those managed conventionally (BS), with significant differences observed across multiple sites. CC sites exhibited higher carbon stocks, with protected carbon averaging 42.6 Mg C ha−1 compared to 29.7 Mg C ha−1 in BS, and non-protected carbon at 10.3 Mg C ha−1 versus 4.8 Mg C ha−1. A direct relationship was identified between total SOC and both protected and non-protected carbon fractions, indicating that the soil of the studies olive orchards is far from being saturated in protected SOC. Moreover, the soil of the CC olive farms had a lower carbon saturation deficit (45.3%) compared to BS (67.2%). The findings show that maintaining the cover crops in olive orchards significantly contributed to carbon sequestration and reduced carbon saturation deficits by increasing the stocks of protected SOC. [ABSTRACT FROM AUTHOR]

This article presents a combined route and path planning strategy to guide Skid–Steer Mobile Robots (SSMRs) in scheduled harvest tasks within expansive crop rows with complex terrain conditions. The proposed strategy integrates: (i) a global planning algorithm based on the Traveling Salesman Problem under the Capacitated Vehicle Routing approach and Optimization Routing (OR-tools from Google) to prioritize harvesting positions by minimum path length, unexplored harvest points, and vehicle payload capacity; and (ii) a local planning strategy using Informed Rapidly-exploring Random Tree ( IRRT * ) to coordinate scheduled harvesting points while avoiding low-traction terrain obstacles. The global approach generates an ordered queue of harvesting locations, maximizing the crop yield in a workspace map. In the second stage, the IRRT * planner avoids potential obstacles, including farm layout and slippery terrain. The path planning scheme incorporates a traversability model and a motion model of SSMRs to meet kinematic constraints. Experimental results in a generic fruit orchard demonstrate the effectiveness of the proposed strategy. In particular, the IRRT * algorithm outperformed RRT and RRT * with 96.1% and 97.6% smoother paths, respectively. The IRRT * also showed improved navigation efficiency, avoiding obstacles and slippage zones, making it suitable for precision agriculture. [ABSTRACT FROM AUTHOR]

Apple-picking robot can promote the development of smart agriculture, and accurate object recognition in complex natural environments using deep learning algorithms is critical. However, research has shown that changes in illumination and object occlusion remain significant challenges for recognition. In order to improve the accuracy of apple apple-picking robot's identification and positioning of apples in natural environment, a method using YOLOv5 (You Only Look Once, YOLO) combined with fast-guided filter is proposed. By introducing a fast-guided filtering module, the ability to extract image features is improved, and the problem of inaccurate occlusion targets and edge detection is solved; K -means clustering algorithm is introduced in improving YOLOv5, which can realize automatic adjustment of image size and step size; BiFPN structure is introduced in Neck network to add weighted feature fusion to highlight the detailed features. The results show that the algorithm proposed in this paper can well remove noise information such as occlusion edge blurring in apple images in a natural light environment. In the real orchard environment, the apple recognition accuracy rate reached 97.8%, the recall rate was 97.3% and the recognition rate was about 26.84 fps. The results show that this research based on YOLOv5 and fast-guided filtering can realize fast and accurate identification of apple fruits in natural environment, and meet the practical application requirements of real-time target detection. [ABSTRACT FROM AUTHOR]

SUMMARY Water use efficiency (WUE) is crucial for apple tree fitness and survival, especially in response to climatic changes. The receptor‐like kinase FERONIA is reportedly an essential regulator of plant stress responses, but its role in regulating WUE under water deficit conditions is unclear. Here, we found that overexpressing the apple FERONIA receptor kinase gene, MdMRLK2, enhanced apple WUE under long‐term water deficit conditions. Under drought treatment, 35S::MdMRLK2 apple plants exhibited higher photosynthetic capacity and antioxidant enzyme activities than wild‐type (WT) plants. 35S::MdMRLK2 apple plants also showed increased biomass accumulation, root activity, and water potential compared to WT plants. Moreover, MdMRLK2 physically interacts with and phosphorylates cinnamoyl‐CoA reductase 1, MdCCR1, an enzyme essential for lignin synthesis, at position Ser260. This interaction likely contributed to increased vessel density, vascular cylinder area, and lignin content in 35S::MdMRLK2 apple plants under drought conditions. Therefore, our findings reveal a novel function of MdMRLK2 in regulating apple WUE under water deficit conditions. [ABSTRACT FROM AUTHOR]

This article reports the design and experiment of a novel air-assisted nozzle for pesticide application in orchard. A novel air-assisted nozzle was designed based on the transverse jet atomization pattern. This article conducted the performance and deposition experiments and established the mathematical model of volume median diameter (D50) and liquid flow rate with the nozzle design parameters. The D50 of this air-assisted nozzle ranged from 52.45 mmto 113.67 mm, and the liquid flow rate ranged from 142.6 ml/min to 1,607.8 ml/min within the designed conditions. These performances meet the low-volume and ultra-low-volume pesticide application in orchard. The droplet deposition experiment results demonstrated that the droplet coverage distribution in different layers and columns is relatively uniform, and the predicted value of spray penetration (SP) numbers SPiA, SPiB, and SPiC (i =1,2,and3)are approximately 70%, 60%, and 70%, respectively. The droplet deposits on the foliage of the canopy (inside and outside) uniformly bring benefit for plant protection and pesticide saving. Compared with the traditional air-assisted nozzle that adopts a coaxial flow atomization pattern, the atomization efficiency of this air-assisted nozzle is higher. Moreover, the nozzle air pressure and liquid flow rate are considerably lower and greater than the traditional air-assisted nozzle, and these results proved that this air-assisted nozzle has great potential in orchard pesticide application. The relationship between the D50 and nozzle liquid pressure of this air-assisted nozzle differs from that of traditional air-assisted nozzles due to the atomization pattern and process. While this article provides an explanation for this relationship, further study about the atomization process and mechanism is needed so as to improve the performance. [ABSTRACT FROM AUTHOR]

This article analyzes the current situation and existing problems of the construction and development of Guangxi's mango industry cluster, and proposes countermeasures and suggestions such as strengthening the construction of high standard orchards, extending and strengthening the chain, cultivating leading enterprises, and strengthening brand building. This provides reference for promoting the upgrading of the mango industry form from "small mangoes" to advantageous and characteristic "big industries". [ABSTRACT FROM AUTHOR]

Given the severe impact of Citrus Huanglongbing on orchard production, accurate detection of the disease is crucial in orchard management. In the natural environments, due to factors such as varying light intensities, mutual occlusion of citrus leaves, the extremely small size of Huanglongbing leaves, and the high similarity between Huanglongbing and other citrus diseases, there remains an issue of low detection accuracy when using existing mainstream object detection models for the detection of citrus Huanglongbing. To address this issue, we propose YOLO-EAF (You Only Look Once–Efficient Asymptotic Fusion), an improved model based on YOLOv8n. Firstly, the Efficient Multi-Scale Attention Module with cross-spatial learning (EMA) is integrated into the backbone feature extraction network to enhance the feature extraction and integration capabilities of the model. Secondly, the adaptive spatial feature fusion (ASFF) module is used to enhance the feature fusion ability of different levels of the model so as to improve the generalization ability of the model. Finally, the focal and efficient intersection over union (Focal–EIOU) is utilized as the loss function, which accelerates the convergence process of the model and improves the regression precision and robustness of the model. In order to verify the performance of the YOLO-EAF method, we tested it on the self-built citrus Huanglongbing image dataset. The experimental results showed that YOLO-EAF achieved an 8.4% higher precision than YOLOv8n on the self-built dataset, reaching 82.7%. The F1-score increased by 3.33% to 77.83%, and the mAP (0.5) increased by 3.3% to 84.7%. Through experimental comparisons, the YOLO-EAF model proposed in this paper offers a new technical route for the monitoring and management of Huanglongbing in smart orange orchards. [ABSTRACT FROM AUTHOR]

Apple (Malus domestica Borkh.) stands out as a globally significant fruit tree with considerable economic importance. Nonetheless, the orchard production of 'Fuji' apples faces significant challenges, including delayed flowering in young trees and inconsistent annual yields in mature trees, ultimately resulting in suboptimal fruit yield due to insufficient flower bud formation. Flower development represents a pivotal process influencing plant adaptation to environmental conditions and is a crucial determinant of successful plant reproduction. The three gene or transcription factor (TF) families, C2H2, DELLA, and FKF1, have emerged as key regulators in plant flowering regulation; however, understanding their roles during apple flowering remains limited. Consequently, this study identified 24 MdC2H2, 6 MdDELLA, and 6 MdFKF1 genes in the apple genome with high confidence. Through phylogenetic analyses, the genes within each family were categorized into three distinct subgroups, with all facets of protein physicochemical properties and conserved motifs contingent upon subgroup classification. Repetitive events between these three gene families within the apple genome were elucidated via collinearity analysis. qRT-PCR analysis was conducted and revealed significant expression differences among MdC2H2-18, MdDELLA1, and MdFKF1-4 during apple bud development. Furthermore, yeast two-hybrid analysis unveiled an interaction between MdC2H2-18 and MdDELLA1. The genome-wide identification of the C2H2, DELLA, and FKF1 gene families in apples has shed light on the molecular mechanisms underlying apple flower bud development. [ABSTRACT FROM AUTHOR]

This study addresses the challenges of low detection precision and limited generalization across various ripeness levels and varieties for large non-greenripe citrus fruits in complex scenarios. We present a high-precision and lightweight model, YOLOC-tiny, built upon YOLOv7, which utilizes EfficientNet-B0 as the feature extraction backbone network. To augment sensing capabilities and improve detection accuracy, we embed a spatial and channel composite attention mechanism, the convolutional block attention module (CBAM), into the head's efficient aggregation network. Additionally, we introduce an adaptive and complete intersection over union regression loss function, designed by integrating the phenotypic features of large non-green-ripe citrus, to mitigate the impact of data noise and efficiently calculate detection loss. Finally, a layerbased adaptive magnitude pruning strategy is employed to further eliminate redundant connections and parameters in the model. Targeting three types of citrus widely planted in Sichuan Province--navel orange, Ehime Jelly orange, and Harumi tangerine--YOLOC-tiny achieves an impressive mean average precision (mAP) of 83.0%, surpassing most other state-of-the-art (SOTA) detectors in the same class. Compared with YOLOv7 and YOLOv8x, its mAP improved by 1.7% and 1.9%, respectively, with a parameter count of only 4.2M. In picking robot deployment applications, YOLOC-tiny attains an accuracy of 92.8% at a rate of 59 frames per second. This study provides a theoretical foundation and technical reference for upgrading and optimizing low-computing-power ground-based robots, such as those used for fruit picking and orchard inspection. [ABSTRACT FROM AUTHOR]