Orchards Research Articles

UGV‐Based Precision Spraying System for Chemical Apple Blossom Thinning on Trellis Trained Canopies

ABSTRACTBlossom thinning is one of the key steps in apple crop load management that improves the quality of apples, reduces stress on the trees, and avoids the likelihood of biennial bearing. Conventional chemical blossom thinning such as air‐blast spraying can lead to excessive use of chemical thinner to ensure full coverage, which can also cause leaf damage fruit russeting. In addition, a well‐trained operator is required to use these chemical spraying systems. To address these challenges, a UGV‐based precision spraying system was developed for automated and targeted chemical blossom thinning for apples. The system is capable of automatically driving along the tree row in the orchard environment during blooming stage, locating apple flower clusters to be thinned using a real‐time machine vision system, and precisely spraying the chemical thinner to the targeted flower clusters. A set of field tests were conducted to evaluate the performance of the UGV‐based target spraying system by comparing it to a conventional air‐blast sprayer (ABS) and a previous prototype named the cartesian target sprayer (CTS). Tests showed that the flower cluster detection reached a precision of 93.8%. The UGV‐based spraying system used 2.2 L of chemical thinner to finish the chemical thinning for 30 apple trees, followed by the ABS and CTS with 4.2 and 2.4 L usage, respectively. The robotic system also obtained an average fruit set of 2.2 per cluster after thinning, which was comparable to that with the air blast sprayer. The findings indicated that the robotic thinning system demonstrated a 66.7% reduction in chemical usage compared to the ABS and exhibited a 43.0% faster operational pace than the CTS, while attaining a comparable fruit set per cluster. The outcomes of the study provided guidance for developing a full scale robotic chemical thinning system for modern apple orchards.

Leaf and soil nutritional composition of purple passion fruit (Passiflora edulis Sims) as influenced by organic nutrients amended with biofertilizer consortium

The use of organic nutrient regimes is regarded as holistic and ecological approach for agricultural sustainability. It offers improved soil health, increased crop growth and enhanced fruit quality, whilst minimizing costs and sustaining the use of natural resources. The present investigation was conducted over two consecutive seasons, viz. 2021–22 and 2022–23 at Mizoram University, Aizawl, India to study the effect of different combinations of organic manures along with consortium of biofertilizers on leaf and soil characteristics of purple passion fruit (Passiflora edulis Sims). The investigation comprises of fourteen treatments of various combinations of organic manures along with consortium of biofertilizers, viz. farmyard Manure (FYM) vermicompost (VC) pig manure (PIM), poultry manure (POM), Azospirillum, phosphate solubilizing bacteria (PSB), potash solubilizing bacteria (KSB), and vesicular arbascular mycorrhiza (VAM). The results demonstrated that the use of organic amendments in combination with biofertilizers enhanced leaf nutrient status as well soil properties in the passion fruit orchard. Among the different treatment combinations used in the investigation, POM + Azospirillum + PSB + KSB + VAM was the most efficient in improving the leaf and soil nutrient status of passion fruit. This consortium may achieve high microbial load, high nutrient availability for soil which results in better growth of the crop. This treatment may therefore be recommended as an alternative option to meet the demand for long term maintenance of soil fertility as well agro-ecosystem conservation and sustainability.

Efficient batch size detection of apple fruit in a plantation environment

There is growing interest in using deep learning for object recognition in robots to enhance the efficiency of apple farming. While deep learning-based object detection has shown promising results in various visual tasks, more research is needed to accurately recognize apples in orchard environments. During the training phase, it is important to determine the optimal values of hyperparameters. This research aims to develop a deep learning model, YOLOv7, to reliably identify apples in orchards, using four different batch size values for training. The MinneApple dataset, trained with these batch sizes, serves as our reference model. To assess the model’s ability to work in different situations, we evaluate it using test data with varying input scales. Our results show that the optimal batch size for detecting apples in orchards is 16, achieving a mean average precision (mAP) of 50%. Furthermore, our findings suggest that increasing the batch size does not improve the efficiency of apple detection in orchard environments.

Machine Learning Methods for Evaluation of Technical Factors of Spraying in Permanent Plantations

Considering the demand for the optimization of the technical factors of spraying for a greater area coverage and minimal drift, field tests were carried out to determine the interaction between the area coverage, number of droplets per cm2, droplet diameter, and drift. The studies were conducted with two different types of sprayers (axial and radial fan) in an apple orchard and a vineyard. The technical factors of the spraying interactions were nozzle type (ISO code 015, code 02, and code 03), working speed (6 and 8 km h−1), and spraying norm (250–400 L h−1). The airflow of both sprayers was adjusted to the plantation leaf mass and the working pressure was set for each repetition separately. A method using water-sensitive paper and a digital image analysis was used to collect data on coverage factors. The data from the field research were processed using four machine learning models: quantile random forest (QRF), support vector regression with radial basis function kernel (SVR), Bayesian Regularization for Feed-Forward Neural Networks (BRNN), and Ensemble Machine Learning (ENS). Nozzle type had the highest predictive value for the properties of number of droplets per cm2 (axial = 69.1%; radial = 66.0%), droplet diameter (axial = 30.6%; radial = 38.2%), and area coverage (axial = 24.6%; radial = 34.8%). Spraying norm had the greatest predictive value for area coverage (axial = 43.3%; radial = 26.9%) and drift (axial = 72.4%; radial = 62.3%). Greater coverage of the treated area and a greater number of droplets were achieved with the radial sprayer, as well as less drift. The accuracy of the machine learning model for the prediction of the treated surface showed a satisfactory accuracy for most properties (R2 = 0.694–0.984), except for the estimation of the droplet diameter for an axial sprayer (R2 = 0.437–0.503).

Modeling the impact of long-term land use changes on deep soil hydrological processes in the Loess Plateau, China

Land use change can significantly affect soil hydrology in arid and semi-arid regions, making it crucial to understand the relationship between vegetation roots and soil moisture. Current models often fail to predict root growth and its impacts on water dynamics accurately. Our work presents a novel model that seamlessly integrates the Community Land Model (CLM) with the Soil & Water Assessment Tool (SWAT). Furthermore, it enhances the root module within the CLM, enabling more accurate simulations of dynamic root depth and distribution across varying tree ages. This improvement particularly considers the crucial processes of dormancy and plant maturity. Soil moisture and root patterns under apple trees of varying ages and in wheat fields on the Loess Plateau was analyzed. Our findings indicate that our dynamic root depth model outperforms traditional static models, and can accurately reflect soil moisture levels with high precision (R2 = 0.80–0.81; Nash-Sutcliffe efficiency (NSE) = 0.65–0.75). In contrast to methods that utilize fixed root depths, dynamic root simulation can provide new insights. As apple orchards mature, the roots of 22-year-old apple trees have been found to reach a depth of 21 m in the soil. Conversely, the maximum root depth of wheat is limited to 1.9 m. This latter finding aligns more closely with the measured root depths, highlighting the accuracy of dynamic simulations. This model reveals that older apple orchards show decreased soil moisture at greater depths (>20 m), contrasting with wheat fields that affect moisture mostly within the top 2 m. Our results underscore the crucial role of dynamic modeling in comprehending root-soil water interactions. Furthermore, they imply that extended orchard cultivation practices can lead to a substantial depletion of deep soil moisture. Specifically, over a period of 1 to 22 years, a water deficit of up to 85 mm yr−1 has been observed. For a 22-year-old forest, the D-D (dynamic distributions of coarse and fine roots) method calculates a significant cumulative deep Soil Water Storage loss. Over the course of 22 years, this loss amounts to 1664 mm, which is almost three times compare to the annual rainfall recorded. Such a large loss has the potential to significant impact on groundwater recharge. This highlights the need for careful consideration in future afforestation efforts to prevent increased soil aridity.

Continuous N fertilization immobilized nuclear accident-derived 137Cs in apple orchard soil during 10 years after the accident

Continuous N fertilization immobilized nuclear accident-derived ¹³⁷Cs in apple orchard soil during 10 years after the accident

Effect of habitat management on arthropod community in crab apple orchards (Malus sylvestris (L.) Mill.) in Indonesia

ABSTRACT Crab apple (Malus sylvestris (L.) Mill.) is native to Central Europe and successfully commercially grown in Indonesia. Management practices that increase biodiversity in agroecosystems are essential for mediating the negative impacts of intensive agriculture. This study aims to compare arthropod abundance and diversity between grass ground cover and Hydrangea intercropping in crab apple orchards and assess their impact on arthropod functional groups providing ecosystem services. In this study, arthropods were sampled using three types of traps, i.e. pitfall trap, yellow sticky trap, and pan trap. Arthropod specimens were identified by order and family, then categorised by their ecological role: detritivore, omnivore, herbivore, the natural enemy (predator and parasitoid), and pollinator. Our study collected 164 families belonging to the five examined functional groups. We found that the family composition of arthropods was significantly different between Hydrangea intercropping and grass ground cover in crab apple orchards. Grass ground cover supports the increase of herbivore and natural enemy populations and it’s related to enhancing biological control. In addition, Hydrangea intercropping increases pollinator species richness and contributes to pollination services. Pest management in crab apple orchards may benefit from a combination of grass ground cover to encourage natural enemies and Hydrangea intercropping to increase pollinators.

Estimating Leaf Area Index in Apple Orchard by UAV Multispectral Images with Spectral and Texture Information

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.

Multi-Source Image Fusion Based Regional Classification Method for Apple Diseases and Pests

Efficient diagnosis of apple diseases and pests is crucial to the healthy development of the apple industry. However, the existing single-source image-based classification methods have limitations due to the constraints of single-source input image information, resulting in low classification accuracy and poor stability. Therefore, a classification method for apple disease and pest areas based on multi-source image fusion is proposed in this paper. Firstly, RGB images and multispectral images are obtained using drones to construct an apple diseases and pests canopy multi-source image dataset. Secondly, a vegetation index selection method based on saliency attention is proposed, which uses a multi-label ReliefF feature selection algorithm to obtain the importance scores of vegetation indices, enabling the automatic selection of vegetation indices. Finally, an apple disease and pest area multi-label classification model named AMMFNet is constructed, which effectively combines the advantages of RGB and multispectral multi-source images, performs data-level fusion of multi-source image data, and combines channel attention mechanisms to exploit the complementary aspects between multi-source data. The experimental results demonstrated that the proposed AMMFNet achieves a significant subset accuracy of 92.92%, a sample accuracy of 85.43%, and an F1 value of 86.21% on the apple disease and pest multi-source image dataset, representing improvements of 8.93% and 10.9% compared to prediction methods using only RGB or multispectral images. The experimental results also proved that the proposed method can provide technical support for the coarse-grained positioning of diseases and pests in apple orchards and has good application potential in the apple planting industry.

Identification of streptomycin-resistant Erwinia amylovora in Iowa.

Erwinia amylovora is a bacterial pathogen that causes fire blight, an important disease in apples and pears. Applying the antibiotic streptomycin during the phenological bloom stage is considered the most effective management tactic for fire blight. Though streptomycin-resistant (SmR) E. amylovora populations have emerged in major U.S. apple-producing regions, antibiotic resistance data for medium to small-sized apple-producing regions like the Midwest is still lacking. This short communication collected symptomatic fire blight samples from Iowa apple orchards during 2022 and 2023, where recent fire blight outbreaks persisted despite streptomycin use. Among E. amylovora isolates from seven counties in central and eastern Iowa, around 90% of them were SmR. All SmR isolates exhibited a single base pair mutation in codon 43 of the rpsL gene, conferring resistance to streptomycin levels exceeding 1,000 μg/mL. Through clustered regularly interspaced short palindromic repeat (CRISPR) analysis, we characterized two E. amylovora genotypes unique to our region. Whole genome sequencing of one representative SmR isolate, IA01, confirmed its CRISPR genotype and subsequent phylogenetic analysis suggested that IA01 is genetically similar to Michigan isolates and distinct from those in eastern and western regions of North America. Furthermore, the disease-causing ability of IA01 was comparable to that of the highly virulent Ea110 strain, a streptomycin sensitive strain isolated from Michigan, in immature pears. Overall, this study underscores the urgent need for regional strategies to address antibiotic resistance and provide insights into its genetic basis and geographic distribution which are crucial for sustainable orchard management.

Effects of Different Living Grass Mulching on Soil Carbon and Nitrogen in an Apple Orchard on Loess Plateau

Living grass mulching (LMG) is a modern, environmentally friendly, practical, and efficient production management technology that improves the ecological environment, quality, and efficiency of the orchard. However, in arid and semi-arid areas, the effects of different grass species mulching on soil carbon composition, carbon pool stability, and nitrogen content are still unclear. Therefore, in order to explore the impact of different LMG on soil carbon, nitrogen, and its component content, as well as the related soil carbon pool management index in an apple orchard located in the semi-arid region of the Loess Plateau, a control experiment was conducted. The experiment involved different grass species cover treatments on an 11-year-old semi-dwarf Qinguan apple orchard from 2019 to 2022. Soil carbon and nitrogen content were measured under each treatment. The results indicated that the application of LMG treatment and depth of the soil had a significant impact on the soil organic carbon (SOC), particulate organic carbon (POC), inactive organic carbon (NAOC), total nitrogen (TN), and carbon-to-nitrogen ratio (C/N). Planting Vulpia myuros mulches significantly enhanced 39.6% surface soil organic carbon, 61.7% surface particulate organic carbon, 20.3% surface dissolved organic carbon (DOC), 75.8% surface inactive organic carbon, and 20.6% surface soil total nitrogen compared to clean tillage. Mulching treatment with the planting of Vulpia myuros boosted surface soil organic carbon and decreased soil carbon pool activity (CPA) and carbon pool activity index (CPAI), ultimately improving the stability of the soil carbon pool. The findings will have a beneficial impact on improving soil quality, carbon sequestration, and emission reduction in arid and semi-arid regions.

Influence of deployment method and maintenance on efficacy of sticky card traps for Halyomorpha halys (Hemiptera: Pentatomidae).

Halyomorpha halys (Stål) (Hemiptera: Pentatomidae) is an invasive pest which feeds on numerous economically significant crops. Many integrated pest management strategies for this species rely on effective season-long monitoring for H. halys populations, including attract-and-kill and threshold-based insecticide sprays. Previous studies have shown that a black pyramid trap effectively captures all mobile life stages of H. halys, however, these bulky, ground-deployed traps can be impractical in active orchard rows. Clear sticky cards have been used as a more practical tool for monitoring when paired with the H. halys aggregation pheromone and synergist. Here, the efficacy of deploying single- or double-sided sticky cards hanging in trees or deployed on wooden stakes was compared to standard black pyramid traps over 2 years. The efficacy of single-sided sticky cards deployed on wooden stakes was also evaluated after occlusion of 25%, 50%, or 75% of the surface area by 2D inert materials and 3D organic matter. Single-sided sticky cards were also exposed to simulated sunlight and rainfall for 0, 4, 8, and 12 wk before deployment on wooden stakes. Captures of H. halys adults using sticky cards deployed on wooden stakes were comparable to pyramid traps. Occlusion of cards by 25% or more of any material type led to a decrease in H. halys captures, however, weathering did not influence capture. These data show that clear sticky cards deployed on wooden stakes are effective for season-long monitoring of H. halys in apple orchards, and card replacement should be driven by maintenance of cleanliness.

A Novel Fusion Perception Algorithm of Tree Branch/Trunk and Apple for Harvesting Robot Based on Improved YOLOv8s

Aiming to accurately identify apple targets and achieve segmentation and the extraction of branch and trunk areas of apple trees, providing visual guidance for a picking robot to actively adjust its posture to avoid branch trunks for obstacle avoidance fruit picking, the spindle-shaped fruit trees, which are widely planted in standard modern apple orchards, were focused on, and an algorithm for apple tree fruit detection and branch segmentation for picking robots was proposed based on an improved YOLOv8s model design. Firstly, image data of spindle-shaped fruit trees in modern apple orchards were collected, and annotations of object detection and pixel-level segmentation were conducted on the data. Training set data were then augmented to improve the generalization performance of the apple detection and branch segmentation algorithm. Secondly, the original YOLOv8s network architecture’s design was improved by embedding the SE module visual attention mechanism after the C2f module of the YOLOv8s Backbone network architecture. Finally, the dynamic snake convolution module was embedded into the Neck structure of the YOLOv8s network architecture to better extract feature information of different apple targets and tree branches. The experimental results showed that the proposed improved algorithm can effectively recognize apple targets in images and segment tree branches and trunks. For apple recognition, the precision was 99.6%, the recall was 96.8%, and the mAP value was 98.3%. The mAP value for branch and trunk segmentation was 81.6%. The proposed improved YOLOv8s algorithm design was compared with the original YOLOv8s, YOLOv8n, and YOLOv5s algorithms for the recognition of apple targets and segmentation of tree branches and trunks on test set images. The experimental results showed that compared with the other three algorithms, the proposed algorithm increased the mAP for apple recognition by 1.5%, 2.3%, and 6%, respectively. The mAP for tree branch and trunk segmentation was increased by 3.7%, 15.4%, and 24.4%, respectively. The proposed detection and segmentation algorithm for apple tree fruits, branches, and trunks is of great significance for ensuring the success rate of robot harvesting, which can provide technical support for the development of an intelligent apple harvesting robot.

Variation in Bacterial and Fungal Communities in Soils from Three Major Apple Pear (Pyrus bretschneideri Rehd.) Orchards.

Microbial communities are closely related to the overall health and quality of soil, but studies on microbial ecology in apple pear orchard soils are limited. In the current study, 28 soil samples were collected from three apple pear orchards, and the composition and structure of fungal and bacterial communities were investigated by high-throughput sequencing. The molecular ecological network showed that the keystone taxa of bacterial communities were Actinobacteria, Proteobacteria, Gemmatimonadetes, Acidobacteria, Nitrospirae, and Chloroflexi, and the keystone taxon of fungal communities was Ascomycota. Mantel tests showed that soil texture and pH were important factors shaping soil bacterial and fungal communities, and soil water soluble organic carbon (WSOC) and nitrate nitrogen (NO3--N) were also closely related to soil bacterial communities. Canonical correspondence analysis (CCA) and variation partition analysis (VPA) revealed that geographic distance, soil texture, pH, and other soil properties could explain 10.55%, 13.5%, and 19.03% of the overall variation in bacterial communities, and 11.61%, 13.03%, and 20.26% of the overall variation in fungal communities, respectively. The keystone taxa of bacterial and fungal communities in apple pear orchard soils and their strong correlation with soil properties could provide useful clues toward sustainable management of orchards.

Ecosystem Services’ Supply–Demand Assessment and Ecological Management Zoning in Northwest China: A Perspective of the Water–Food–Ecology Nexus

The coordinated development of the water–food–ecology (WFE) nexus is a practical issue that has to be addressed urgently for northwest China’s (WTL) sustainable development. Optimizing the linkage relationship and accomplishing the rational distribution of resources from the perspective of the supply and demand for ecosystem services (ESSD) are imperative. Thus, in this study, a numerical indicator system for ESSD from the perspective of the WFE nexus was constructed with the incorporation of the water and carbon footprint. Based on this premise, the ecological management zoning method was enhanced by integrating supply and demand risks, and optimization suggestions were proposed for various zones. The results showed that (1) carbon sequestration (CS), food production (FP), and water yield (WY) supply and demand significantly increased between 2000 and 2021. High ESSDs were concentrated in the west side of northwest China. Maize, wheat, cotton, vegetables, and garden fruits had a higher demand for ecosystem services (ESs). (2) The three ESSDs were bound in a synergistic relationship. The synergy between supply exhibited significant spatial heterogeneity, while the synergies between demand showed similar distribution patterns. (3) Regarding quantity matching, the supply for FP and CS surpassed demand, while the WY supply could not meet the demand. The three ESs’ supply and demand deficits rose. Ecological supply–demand ratio (ESDR) regional differentiation for the three ESs was apparent. Regarding spatial matching, FP and CS concentrated on low supply–low demand, while WY focused on high supply–high demand. FP risk was concentrated in Qaidam Basin, whereas WY risk was mostly in Hexi inland river basin (HX), the Yellow River Basin area (HH), and both sides of the “Qice line”. (4) The ecological management zones were formed by integrating WTL’s three dominant weak functional zones, four categorized strategy regions, and four governance models. This study can serve as a scientific benchmark for regional ecological management, which is significant in ensuring northwest China’s water, food, and ecological safety.

The Effect of Soil Properties on Weed Distribution in Turkish Apple Orchards

The Effect of Soil Properties on Weed Distribution in Turkish Apple Orchards

The influence of soil depth and tree age on soil enzyme activities and stoichiometry in apple orchards

The indispensable role of soil microorganisms in apple orchards, impacting soil quality, nutrient cycling, and crop productivity, cannot be overstated. Nonetheless, as apple trees mature, the potential for soil nutrient imbalances arises, affecting apple growth and yield. Research on how deep soil properties evolve with tree age is limited. Soil samples were gathered from apple orchards of various ages, utilizing the microplate fluorescence method to examine soil enzyme activity, microbial stoichiometry, and their correlation with soil nutrients. Results reveal that soil properties, microbial biomass, and enzymes are influenced by both tree age and depth. Microbial biomass decreases with depth, peaking in young orchards with the highest MBN:MBP ratio. Initially declining, microbial entropy nitrogen rises with tree age. Redundancy analysis identifies OP as pivotal in explaining extracellular enzyme activity. The enzyme C:N ratio exhibits a negative correlation with the MBN:MBP ratio. Additionally, ratios of ln(NAG) to ln(BG), ln(AKP) to ln(BG), and ln(AKP) to ln(NAG) demonstrate positive correlations. Enzyme activity ratios for C, N, and P transformation deviate from the global ecosystem ratio, indicating nuanced nutrient dynamics. Soil vector angles are influenced by tree age, suggesting limitations on microbial metabolism by carbon and phosphorus. Structural equation modeling unveils that soil depth negatively impacts pH, AKP, and Enzyme N:P, while tree age adversely affects Enzyme N:P. Furthermore, MBN:MBP is directly influenced by AKP and soil depth, indirectly by tree age and pH. This study underscores the significance of microbial stoichiometry in evaluating soil resource ecology relations, offering insights into soil microbe-orchard ecosystem dynamics.

An Evaluation of Multi-Channel Sensors and Density Estimation Learning for Detecting Fire Blight Disease in Pear Orchards.

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.

Optimizing Orchard Planting Efficiency with a GIS-Integrated Autonomous Soil-Drilling Robot

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.

The role of agroecological systems in cation interaction and aggregate formation in drylands of northeastern Brazil

Agroecological production systems are used in dryland agroecosystems. Therefore, it is necessary to verify their contributions, especially with regard to the functionality of soil attributes. From this perspective, this study aimed to evaluate the influence of soil use and management on the structural, hydraulic, and chemical attributes of a Haplic Cambisol in the Apodi Plateau, RN, Brazil. The experimental design adopted was completely randomized, with treatments distributed in a 5×3 factorial arrangement corresponding to five sampling locations (fruit farming, horticulture, agroecological, intercropping of Zea mays and Vigna unguiculata (L.) Walp), and three soil layers (0.00-0.10, 0.10-0.20, and 0.20-0.30 m), in addition to the correlation matrix and principal component analysis as multivariate statistical techniques. The parameters evaluated included soil pH, nutrient levels (available P, K+, Ca2+, and Mg2+), total organic carbon, soil density, total porosity, field capacity, permanent wilting point, available water, aggregation, aggregate stability, weighted mean diameter of aggregates, and the content of organic matter, Ca2+, and Mg2+ in different soil aggregate classes. The study concluded that continuous soil use practices such as intercropping Z. mays and V. unguiculata and horticulture significantly reduce nutrient availability, organic carbon content, moisture, and soil aggregation. However, areas under agroecological management and fruit cultivation, which involve minimal soil disturbance, promote the improvement of these properties.