AgriEngineering

One of the greatest challenges in crop science worldwide is balancing crop production and water management. In the context of sustainability and vertical production growth, understanding water relations is fundamental for improving crop management in irrigated and rainfed sugarcane fields. Adequate irrigation management can improve water use efficiency and agronomic performance. Nicaragua, due to its limited research and information on irrigation, has significant opportunities to increase crop yields and enhance water efficiency. Therefore, the aim of this study was to evaluate the response of sugarcane growth, yield, and water use efficiency under different irrigation management strategies. The study was performed in a field area from Casur Sugarcane mill in Nicaragua during the crop cycle 2021/2022. The experimental area was cultivated in high clay soil, with the variety CP72-2086 in the second cut with the furrow irrigation method. Two treatments were evaluated, irrigation based on soil moisture (ISw) and irrigation with fixed intervals (IFI), and their effect on growth variables and crop yield. On a temporal analysis, the plants showed compensatory growth in IFI, recovering from water-deficit stress in most measured variables. Sugarcane yield was statistically different between the treatments with 97.87 and 83.84 Mg ha⁻¹ for ISw and IFI, respectively. The water use efficiency was similar for both irrigation strategies. Based on the results found by the authors, it is recommendable to manage irrigation based on soil moisture content because of the best growth response and sugarcane yield. Full article

The global transition to a digital era is crucial for society, as most daily activities are driven by digital technologies aimed at enhancing productivity and efficiency in the production of food, fibers, and bioenergy. However, the segregation of digital techniques and equipment in both rural and urban areas poses significant obstacles to technological efforts aimed at combating hunger, ensuring sustainable agriculture, and fostering innovations aligned with the United Nations Sustainable Development Goals (SDGs 02 and 09). Rural regions, which are often less connected to technological advancements, require digital transformation to shift from subsistence farming to market-integrated production. Recent efforts to expand digitalization in these areas have shown promising results. Digital agriculture encompasses terms such as artificial intelligence (AI), the Internet of Things (IoT), big data, and precision agriculture integrating information and communication with geospatial and satellite technologies to manage and visualize natural resources and agricultural production. This digitalization involves both internal and external property management through data analysis related to location, climate, phytosanitary status, and consumption. By utilizing sensors integrated into unmanned aerial vehicles (UAVs) and connected to mobile devices and machinery, farmers can monitor animals, soil, water, and plants, facilitating informed decision-making. An important limitation in studies on nutritional diagnostics is the lack of accuracy validation based on plant responses, particularly in terms of yield. This issue is observed even in conventional leaf tissue analysis methods. The absence of such validation raises concerns about the reliability of digital tools under real field conditions. To ensure the effectiveness of spectral reflectance-based diagnostics, it is essential to conduct additional studies in commercial fields across different regions. These studies are crucial to confirm the accuracy of these methods and to strengthen the development of digital and precision agriculture. Full article

Yellow rust is one of the most destructive fungal diseases affecting wheat, significantly reducing yield and grain quality. Early detection is crucial for effective plant protection and disease management. This study aims to develop and validate a methodology for early diagnosis of yellow rust using the Normalized Difference Vegetation Index (NDVI) derived from UAV-acquired spectral data. This research was conducted in an experimental wheat field near General Toshevo, Bulgaria, which is owned by the Dobrudja Agricultural Institute (DAI). A widely cultivated winter wheat variety, Enola, was monitored using UAV-based imaging, and the NDVI values were analyzed to assess the correlation between spectral reflectance and infection severity. The NDVI showed a moderate correlation as an indicator of pathogen-induced stress, with moderate predictive capability (R² = 51.4%) for assessing yellow rust infection severity. The results demonstrated that UAV-based NDVI analysis could effectively detect early-stage infections and monitor the spatial spread of the disease. The proposed methodology enables large-scale, non-invasive monitoring of wheat health, facilitating early disease detection. This approach can help optimize disease management strategies, although ground-based validation remains essential to distinguish between different stress factors affecting vegetation. Full article

This study explores advanced methods for plant disease classification by integrating pre-trained deep learning models and leveraging ensemble learning. After a comprehensive review of deep learning methods in this area, the InceptionResNetV2, MobileNetV2, and EfficientNetB3 architectures were identified as promising candidates, as they have been shown to achieve high accuracy and efficiency in various applications. The proposed approach strategically combines these architectures to leverage their unique strengths: the advanced feature extraction capabilities of InceptionResNetV2, the lightweight and efficient design of MobileNetV2, and the scalable, performance-optimized structure of EfficientNetB3. By integrating these models, the approach aims to improve classification accuracy and robustness and overcome the multiple challenges of plant disease detection. Comprehensive experiments were conducted on three datasets—PlantVillage, PlantDoc, and FieldPlant—representing a mix of laboratory and real-world conditions. Advanced data augmentation techniques were employed to improve model generalization, while a systematic ablation study validated the efficacy of key architectural choices. The ensemble model achieved state-of-the-art performance, with classification accuracies of 99.69% on PlantVillage, 60% on PlantDoc, and 83% on FieldPlant. These findings highlight the potential of ensemble learning and transfer learning in advancing plant disease detection, offering a robust solution for real-world agricultural applications. Full article

The dairy sector plays a fundamental role in the economic development of numerous regions by creating jobs and sustaining the livelihoods of millions of people. However, concerns related to animal welfare and environmental sustainability—particularly greenhouse gas (GHG) emissions—persist in intensive dairy systems. This study aimed to measure and assess the presence of GHGs, such as methane (CH₄) and carbon dioxide (CO₂), in a compost barn facility, using spatial variability tools to analyze the distribution of these gasses at different heights (0.25 m and 1.5 m) relative to the animals’ bedding. Data were collected over five consecutive days using a prototype equipped with low-cost sensors. Geostatistical analysis was performed using R, and spatial distribution maps were generated with Surfer 13^®. Results showed elevated CH₄ concentrations at 0.25 m, exceeding values typically reported for similar systems values (60–117 ppm), while CO₂ concentrations remained within the expected range (970–1480 ppm), suggesting low risk to animals, workers, and the environment. The findings highlight the importance of continuous environmental monitoring to promote sustainability and productivity in confined dairy operations. Full article

Air assistance and electrical charge transfer to droplets can optimize pesticide applications and reduce losses in sweet pepper cultivation. The objective of this study was to evaluate the effects of spray rate and pneumatic spraying with and without an electrostatic charge on spray deposition, spray coverage, and ground losses in sweet pepper crops. Four application techniques were employed: standard farmer hydraulics (SFH), reduced volume hydraulics (RVH), pneumatic with air and electrostatic assistance (PAEA), and pneumatic with air assistance (PAA). The effects of the application techniques on spray deposition varied as a function of plant height, canopy depth, and leaf surface. The SFH resulted in the greatest amounts of spray deposition on the adaxial leaf surface. In contrast, PAEA resulted in the greatest amounts of deposition on the abaxial leaves. The PAEA treatment improved spray coverage on abaxial leaves of the external canopy but did not improve spray coverage on the internal canopy. Compared to the SFH treatment, the 50% reduction in the spray rate of the RVH treatment decreased deposition and spray coverage. The pneumatic treatments, regardless of electrostatic charges, resulted in lower spray loss to the ground. Full article

Climate change poses a significant threat to agricultural productivity, particularly in regions vulnerable to extreme temperatures and water scarcity, such as Irbid, Jordan. This study assesses the future impacts of projected shifts in precipitation and temperature on wheat yields, using the Decision Support System for Agrotechnology Transfer (DSSAT) model for calibrating and validating under local agro-environmental conditions. Two shared socioeconomic pathways (SSP3-7.0 and SSP5-8.5), representing high-emission and fossil-fuel-intensive futures, were evaluated across mid- and late-century periods (2030–2060 and 2070–2100). The DSSAT model was calibrated using local field data to simulate crop phenology, biomass accumulation, and nitrogen dynamics, showing strong agreement with observed grain yield and harvest index, thereby confirming its reliability for climate impact assessments. Yield projections under each scenario were further analyzed using machine learning algorithms—random forest and gradient boosting regression—to quantify the influence of individual climate variables. The results showed that under SSP5-8.5 (2030–2060), precipitation was the dominant factor influencing yield variability, underscoring the critical role of water availability. In contrast, under SSP3-7.0 (2070–2100), rising maximum temperatures became the primary constraint, highlighting the growing risk of heat stress. Predictive accuracy was higher in precipitation-dominated scenarios (R² = 0.81) than in temperature-dominated cases (R² = 0.65–0.73), reflecting greater complexity under extreme warming. These findings emphasize the value of integrating well-calibrated crop models with climate projections and machine learning tools to support climate-resilient agricultural planning. Moreover, practical adaptation strategies, such as adjusting planting dates, using heat-tolerant varieties, and optimizing irrigation, are recommended to enhance resilience. Emerging techniques such as seed priming show promise and merit integration into future crop models. The findings support SDG 2 and SDG 13 by informing climate-resilient food production strategies. Full article

The main objective of this study was to evaluate the possibilities of visible-near-infrared spectroscopy for investigating water and nitrogen stress in tomato plants. Two varieties of tomato plants (Red Bounty and Manusa) were grown in a greenhouse. Plants were divided into three groups: control, reduced nitrogen fertilization, and reduced watering. Spectral measurements of tomato leaves were made on-site. A USB4000 spectrometer for 450–1100 nm and a handheld AlbaNIR for the 900–1650 nm region were used for the spectra acquisition. Twenty-four vegetative indices were calculated using the reflectance characteristics of plants. Soft Independent Modeling of Class Analogy (SIMCA) models were developed for classification. Additionally, aquagrams were calculated. Results show differences between the spectra of leaves from control and stressed plants for both tomato varieties. Aquagrams clearly show the differences in water structures in the three groups of plants. The performance of developed SIMCA models for discriminating plants according to growing conditions was very high. The total accuracy was between 86.89% and 97.09%. Several vegetation indices successfully differentiate control and stressed plants for both tomato varieties. The results show successful differentiation of the control and stressed tomato plants based on spectral characteristics of the plants’ leaves in the visible and near-infrared region. Full article

Precision irrigation requires reliable estimates of crop evapotranspiration (ET) using site-specific crop and weather data inputs. Such estimates are needed at high resolutions which have been minimally explored for heterogeneous crops such as orchards. In addition, weather information for estimating ET is very often selected from sources that do not represent conditions like heterogeneous site-specific conditions. Therefore, a study was conducted to map geospatial ET and transpiration (T) of a high-density modern apple orchard using high-resolution aerial imagery, as well as to quantify the impact of site-specific weather conditions on the estimates. Five campaigns were conducted in the 2020 growing season to acquire small unmanned aerial system (UAS)-based thermal and multispectral imagery data. The imagery and open-field weather data (solar radiation, air temperature, wind speed, relative humidity, and precipitation) inputs were used in a modified energy balance (UASM-1 approach) extracted from the Mapping ET at High Resolution with Internalized Calibration (METRIC) model. Tree trunk water potential measurements were used as reference to evaluate T estimates mapped using the UASM-1 approach. UASM-1-derived T estimates had very strong correlations (Pearson correlation [r]: 0.85) with the ground-reference measurements. Ground reference measurements also had strong agreement with the reference ET calculated using the Penman–Monteith method and in situ weather data (r: 0.89). UASM-1-based ET and T estimates were also similar to conventional Landsat-METRIC (LM) and the standard crop coefficient approaches, respectively, showing correlation in the range of 0.82–0.95 and normalized root mean square differences [RMSD] of 13–16%. UASM-1 was then modified (termed as UASM-2) to ingest a locally calibrated leaf area index function. This modification deviated the components of the energy balance by ~13.5% but not the final T estimates (r: 1, RMSD: 5%). Next, impacts of representative and non-representative weather information were also evaluated on crop water uses estimates. For this, UASM-2 was used to evaluate the effects of weather data inputs acquired from sources near and within the orchard block on T estimates. Minimal variations in T estimates were observed for weather data inputs from open-field stations at 1 and 3 km where correlation coefficients (r) ranged within 0.85–0.97 and RMSD within 3–13% relative to the station at the orchard-center (5 m above ground level). Overall, the results suggest that weather data from within 5 km radius of orchard site, with similar topography and microclimate attributes, when used in conjunction with high-resolution aerial imagery could be useful for reliable apple canopy transpiration estimation for pertinent site-specific irrigation management. Full article

China is the world’s largest producer of chili peppers, which occupy particularly important economic and social values in various fields such as medicine, food, and industry. However, during its production process, chili peppers are affected by pests and diseases, resulting in significant yield reduction due to the temperature and environment. In this study, a lightweight pepper disease identification method, DD-YOLO, based on the YOLOv8n model, is proposed. First, the deformable convolutional module DCNv2 (Deformable ConvNetsv2) and the inverted residual mobile block iRMB (Inverted Residual Mobile Block) are introduced into the C2F module to improve the accuracy of the sampling range and reduce the computational amount. Secondly, the DySample sampling operator (Dynamic Sample) is integrated into the head network to reduce the amount of data and the complexity of computation. Finally, we use Large Separable Kernel Attention (LSKA) to improve the SPPF module (Spatial Pyramid Pooling Fast) to enhance the performance of multi-scale feature fusion. The experimental results show that the accuracy, recall, and average precision of the DD-YOLO model are 91.6%, 88.9%, and 94.4%, respectively. Compared with the base network YOLOv8n, it improves 6.2, 2.3, and 2.8 percentage points, respectively. The model weight is reduced by 22.6%, and the number of floating-point operations per second is improved by 11.1%. This method provides a technical basis for intensive cultivation and management of chili peppers, as well as efficiently and cost-effectively accomplishing the task of identifying chili pepper pests and diseases. Full article

This study presents the development of a system based on convolutional neural networks for the automated detection and counting of Gossypium barbadense fruits, specifically the IPA cotton variety, during its maturation stage, known as “mota”, in crops located in the Lambayeque region of northern Peru. To achieve this, a dataset was created using images captured with a mobile device. After applying data augmentation techniques, the dataset consisted of 2186 images with 70,348 labeled fruits. Five deep learning models were trained: two variants of YOLO version 8 (nano and extra-large), two of YOLO version 11, and one based on the Faster R-CNN architecture. The dataset was split into 70% for training, 15% for validation, and 15% for testing, and all models were trained over 100 epochs with a batch size of 8. The extra-large YOLO models achieved the highest performance, with precision scores of 99.81% and 99.78%, respectively, and strong recall and F1-score values. In contrast, the nano models and Faster R-CNN showed slightly lower effectiveness. Additionally, the best-performing model was integrated into a web application developed in Python, enabling automated fruit counting from field images. The YOLO architecture emerged as an efficient and robust alternative for the automated detection of cotton fruits and stood out for its capability to process images in real time with high precision. Furthermore, its implementation in crop monitoring facilitates production estimation and decision-making in precision agriculture. Full article

This study presents a remote sensing (RS) approach for monitoring invasive aquatic species and assessing their impact on water quality in the Lis Valley Irrigation District (LVID), Portugal. Using high-resolution PlanetScope imagery (3.7 m resolution), this method overcomes spatial limitations in narrow irrigation canals. Representative sub-zones were selected to analyze spatial and temporal trends, and vegetation indices (Normalized Difference Vegetation Index—NDVI, Enhanced Vegetation Index—EVI, Green Chlorophyll Index—GCI) were calculated to map the spread of Eichhornia crassipes (water hyacinth—WH) and Myriophyllum aquaticum (parrot’s feather—PF). All three vegetation indices exhibited significant linear regressions with pH, with the EVI showing the highest coefficient of determination (R² = 0.761), followed by the NDVI (R² = 0.726) and GCI (R² = 0.663), with p-values and ANOVA p-values below 0.05. Dissolved Oxygen (DO) also showed strong correlations, particularly with the GCI (R² = 0.886 for both DO concentration and saturation). The NDVI and EVI demonstrated significant regressions for these parameters, with R² values between 0.661 and 0.862. The results demonstrate the potential of RS to detect invasive species and assess their ecological impact, providing a cost-effective tool for management strategies in irrigation systems. Future research should integrate more field data and extend the study period to enhance classification accuracy. Full article

The characterization of soil attribute variability often requires dense sampling grids, which can be economically unfeasible. A possible solution is to perform targeted sampling based on previously collected data. The objective of this research was to develop a method for mapping soil attributes based on Management Zones (MZs) delineated from Sentinel-1 radar data. Sentinel-1 images were used to create time profiles of six indices based on VV (vertical–vertical) and VH (vertical–horizontal) backscatter in two agricultural fields. MZs were delineated by analyzing indices and VV/VH backscatter bands individually through two approaches: (1) fuzzy k-means clustering directly applied to the indices’ time series and (2) dimensionality reduction using deep-learning autoencoders followed by fuzzy k-means clustering. The best combination of index and MZ delineation approaches was compared with four soil attribute mapping methods: conventional (single composite sample), high-density uniform grid (one sample per hectare), rectangular cells (one composite sample per cell of 5 to 10 hectares), and random cells (one composite sample per cell of varying sizes). Leave-one-out cross-validation evaluated the performance of each sampling method. Results showed that combining the VV/VH index and autoencoders for MZ delineation provided more accurate soil attribute estimates, outperforming the conventional, random cells, and often the rectangular cell method. In conclusion, the proposed methodology presents scalability potential, as it does not require prior calibration and was validated on soil types commonly found across Brazil’s agricultural regions, making it suitable for integration into digital platforms for broader application in precision agriculture. Full article

This study presents a system framework by which cloud resources are utilized to analyze crop germination status in a 2U CubeSat. This research aims to address the onboard computing constraints in nanosatellite missions to boost space agricultural practices. Through the Espressif Simple Protocol for Network-on-Wireless (ESP-NOW) technology, communication between ESP-32 modules were established. The corresponding sensor readings and image data were securely streamed through Amazon Web Service Internet of Things (AWS IoT) to an ESP-NOW receiver and Roboflow. Real-time plant growth predictor monitoring was implemented through the web application provisioned at the receiver end. On the other hand, sprouts on germination bed were determined through the custom-trained Roboflow computer vision model. The feasibility of remote data computational analysis and monitoring for a 2U CubeSat, given its minute form factor, was successfully demonstrated through the proposed cloud framework. The germination detection model resulted in a mean average precision (mAP), precision, and recall of 99.5%, 99.9%, and 100.0%, respectively. The temperature, humidity, heat index, LED and Fogger states, and bed sprouts data were shown in real time through a web dashboard. With this use case, immediate actions can be performed accordingly when abnormalities occur. The scalability nature of the framework allows adaptation to various crops to support sustainable agricultural activities in extreme environments such as space farming. Full article

To address the inefficiency and high cost of manual potato pickup in segmented harvesting, a dual-disc potato pickup and harvesting device was designed. The device utilizes counter-rotating dual discs to gather and preliminarily lift the potato–soil mixture, and combines it with an elevator chain to achieve potato–soil separation and transportation. Based on Hertz’s collision theory, the impact of disc rotational speed on potato damage was analyzed, establishing a maximum speed limit (≤62.56 r/min). Through kinematic analysis, the disc inclination angle (12–24°) and operational parameters were optimized. Through coupled EDEM-RecurDyn simulations and Box–Behnken experimental design, the optimal parameter combination was determined with the potato loss rate and potato damage rate as evaluation indices: disc rotational speed of 50 r/min, disc inclination angle of 16°, and machine forward speed of 0.6 m/s. Field validation tests revealed that the potato loss rate and potato damage rate were 1.53% and 2.45%, respectively, meeting the requirements of the DB64/T 1795-2021 standard. The research findings demonstrate that this device can efficiently replace manual potato picking, providing a reliable solution for the mechanized harvesting of potatoes. Full article

This study employs RGB imagery and machine learning techniques to detect Tagosodes orizicolus infestations in “Tinajones” rice crops during the flowering stage, a critical challenge for agriculture in northern Peru. High-resolution images were acquired using an unmanned aerial vehicle (UAV) and preprocessed by extracting 256 × 256-pixel segments, focusing on three classes: infested zones, non-cultivated areas, and healthy rice crops. A dataset of 1500 images was constructed and utilized to train deep learning models based on VGG16 and ResNet50. Both models exhibited highly comparable performance, with VGG16 attaining a precision of 98.274% and ResNet50 achieving a precision of 98.245%, demonstrating their effectiveness in identifying infestation patterns with high reliability. To automate the analysis of complete UAV-acquired images, a web-based application was developed. This system receives an image, segments it into grids, and preprocesses each section using resizing, normalization, and dimensional adjustments. The pretrained VGG16 model subsequently classifies each segment into one of three categories: infested zone, non-cultivated area, or healthy crop, overlaying the classification results onto the original image to generate an annotated visualization of detected areas. This research contributes to precision agriculture by providing an efficient and scalable computational tool for early infestation detection, thereby supporting timely intervention strategies to mitigate potential crop losses. Full article

Sunflower press cake (SPC), a by-product of the edible oil industry, represents a promising source of plant-based protein. This study aimed to investigate and optimize protein recovery from SPC using conventional (CE) and advanced extraction techniques, including Ultrasound and Microwave-Assisted Extraction (UMAE), Pressurized Liquid Extraction (PLE) and Enzyme-Assisted Extraction (EAE). The protein content both in extracts and in the precipitated mass was measured through Lowry assay, while the amino acid profile of the extracted proteins under optimal conditions was analyzed via High-Performance Liquid Chromatography (HPLC). Extraction parameters were optimized using response surface methodology (RSM) for each method. Among the novel methods studied, UMAE and PLE demonstrated superior efficiency over CE, yielding higher protein recovery in significantly shorter extraction times. Optimal UMAE conditions (10 min, 0.03 g/mL, 450 W microwave power, and 500 W ultrasound power) yielded a precipitation yield (PY) of 21.2%, protein recovery in extract (PR_E) of 79.9%, and protein recovery in precipitated mass (PR_P) of 66.3%, with a protein content (PC_P) of 902.60 mg albumin eq./g. Similarly, optimal PLE conditions (6 min, 0.03 g/mL, and 50 °C) resulted in PY, PR_E, and PR_P of 17.7, 68.9, and 47.4%, respectively, with a PC_P of 932.45 mg albumin eq./g. EAE using Aspergillus saitoi protease was comparatively less effective. The amino acid profiling confirmed SPC as a valuable protein source, with glutamic acid, arginine, and aspartic acid being the most abundant. These results highlight the potential of UMAE and PLE as efficient strategies for valorizing edible oil industry by-products into high-quality protein ingredients for food and biotechnological applications. Full article

Precision agriculture and advanced sowing technologies, including variable sowing rates, can be used to optimise sunflower yields by ensuring a uniform plant distribution, efficient resource utilisation, and adaptation to soil variability. These agronomic and technological innovations help mitigate field heterogeneity effects, enhancing sunflower establishment, growth, and overall yield stability. The main goal of this research was to analyse the interactions among management, soil, and environmental variables and their effects on the sowing quality and yield in the case of precision sunflower production. A sowing field experiment was set up in the period between 2021 and 2023 to identify these effects and their complex interactions, which were evaluated with the aim of improving the sowing and yield parameters, while also understanding the importance of each different parameter. As a key outcome for precision sowing, this research demonstrates that the variability in sowing parameters—such as double and missing sowing rates, as well as sowing uniformity—was significantly influenced by the field conditions, productivity zones, and nominal crop density. These findings underscore the importance of implementing site-specific management strategies to optimise sunflower production and maximise yields. Overall, of the various factors influencing sunflower production, the crop year proved to be more significant than the soil parameters due to the strong influence of annual climatic variability. The field zone was also identified as a more critical determinant of sowing and yield variability than crop density, highlighting the importance of spatial management within fields, and also marking possible directions for future research. Full article

Armillaria root rot (ARR) is a fungal disease caused by Desarmillaria caespitosa and the leading cause of peach tree decline in the Southeastern U.S. It affects the roots and lower stems of trees, leading to the decay of the tree’s root system. Planting peach trees shallow on berms and excavating soil around the root collar after two years can extend the economic life of infected trees. However, berms pose operational challenges, including elevation changes, soil erosion from water flow, and herbicide and fertilizer runoff, thereby reducing orchard management efficiency. This study aimed to develop a tractor-mounted rotary tillage method to flatten the area between peach trees planted on berms, improving safety and reducing runoff. A custom paddle wheel attachment (20.3 cm height, 30.5 cm length) was retrofitted to an existing mechanical orchard weed management implement equipped with a hydraulic rotary head. A hydraulic flow meter, two pressure transducers, and an RTK-GPS receiver were integrated with a wireless data acquisition system to monitor the paddle wheel rotational speed and tractor ground speed during field trials. The effects of three paddle wheel speeds (132, 177, and 204 RPM) and three tractor ground speeds (1.65, 2.255, and 3.08 km/h) were evaluated in two orchards with Cecil sandy loam soil (bulk density: 1.93 g/cm³; slope: 2–6%). The paddle wheel speed had a greater influence on the torque and power requirements than the tractor ground speed. The combination of a 177 RPM paddle speed and 3 km/h tractor speed resulted in the smoothest soil surface with minimum torque demand, indicating this setting as optimal for flattening berms in similar soil conditions. Future research will include optimizing the paddle wheel structure and equipping the berm leveling machine with tree detection sensors to control the rotary head position. Full article