AgriEngineering

Efficient sugarcane ratooning management requires maintaining soil organic carbon (SOC) balance and improving soil physical properties. Retaining agricultural residues and applying organic fertilizers are essential for sustaining SOC levels. However, excessive soil compaction caused by heavy machinery remains a challenge, and no existing implements are specifically designed to alleviate soil compaction and apply organic fertilizers in sugarcane ratoon fields. This study aimed to design, develop, and evaluate an organic fertilizer applicator capable of performing a single-step operation that integrates subsoiling, fertilizer application, and soil mixing. The developed implement consists of four main components: (1) a pyramid-shaped hopper, (2) a two-way horizontal screw conveyor, (3) a subsoiler, and (4) a disk harrow set. The results indicated that the specific mass flow rate is directly proportional to screw size and inversely proportional to PTO shaft speed. The optimal configuration for the organic fertilizer applicator included an 18-inch harrow set, a 10-degree harrow angle, an inclined-leg subsoiler, and the Low3 gear at 1900 rpm, which required a draft force of 12.75 kN. Field performance tests demonstrated an actual field capacity of 0.89 ha·h⁻¹ and a field efficiency of 66.17%, confirming the implement’s effectiveness in improving soil conditions and integrating tillage with fertilizer application. Full article

Due to the increasing demand for sustainable energy, non-edible oilseed crops are being explored as alternatives to traditional edible oils. Annona squamosa seeds are rich in oil content (24%/100 g) and often discarded as agricultural waste. Determination of mechanical properties of the seeds under compression loading is significant for designing machinery for its handling and processing. Thus, the present study assessed the effect of loading speeds, LS, (5.0–25 mm/min) and moisture contents, ms, (8.0–32.5%, db) on rupture force and energy, bioyield force and energy, deformation, and hardness at the seed’s horizontal and vertical orientations using a Testometric Universal Testing Machine. The results indicate that both LS and mc significantly (p<0.05) affect the mechanical properties of the seeds. Particularly, horizontal loading orientations consistently exhibited higher values for the selected compressive properties than vertical orientations, except for deformation at varying LS. The correlations between LS, mc, and the compressive parameters of the seed were mostly linear, at both orientations, with increasing mc from 8.0 to 32.5% (db). High correlation coefficients (R2) were obtained for the relationship between the studied parameters, LS, and mc. The data obtained would provide crucial insights into optimizing oil extraction processes by enabling the design of efficient machinery that accommodates the unique characteristics of the seeds. Thus, the findings contribute to the growing interest in alternative biodiesel feedstock, demonstrating that A. squamosa seeds can be repurposed for economic and environmental benefits. Full article

Accurate weed segmentation in Unmanned Aerial Vehicle (UAV) imagery remains a significant challenge in precision agriculture due to environmental variability, weak contextual representation, and inaccurate boundary detection. To address these limitations, we propose the Multi-Scale and Edge-Aware Network (MSEA-Net), a lightweight and efficient deep learning framework designed to enhance segmentation accuracy while maintaining computational efficiency. Specifically, we introduce the Multi-Scale Spatial-Channel Attention (MSCA) module to recalibrate spatial and channel dependencies, improving local–global feature fusion while reducing redundant computations. Additionally, the Edge-Enhanced Bottleneck Attention (EEBA) module integrates Sobel-based edge detection to refine boundary delineation, ensuring sharper object separation in dense vegetation environments. Extensive evaluations on publicly available datasets demonstrate the effectiveness of MSEA-Net, achieving a mean Intersection over Union (IoU) of 87.42% on the Motion-Blurred UAV Images of Sorghum Fields dataset and 71.35% on the CoFly-WeedDB dataset, outperforming benchmark models. MSEA-Net also maintains a compact architecture with only 6.74 M parameters and a model size of 25.74 MB, making it suitable for UAV-based real-time weed segmentation. These results highlight the potential of MSEA-Net for improving automated weed detection in precision agriculture while ensuring computational efficiency for edge deployment. Full article

Understanding the impact of robotization on the workforce dynamics has become increasingly urgent. While expert assessments provide valuable insights, they are often time-consuming and resource-intensive. Large language models (LLMs) offer a scalable alternative; however, their accuracy and reliability in evaluating workforce robotization potential remain uncertain. This study systematically compares general-purpose LLM-generated assessments with expert evaluations to assess their effectiveness in the agricultural sector by considering human judgments as the ground truth. Using ChatGPT, Copilot, and Gemini, the LLMs followed a three-step evaluation process focusing on (a) task importance, (b) potential for task robotization, and (c) task attribute indexing of 15 agricultural occupations, mirroring the methodology used by human assessors. The findings indicate a significant tendency for LLMs to overestimate robotization potential, with most of the errors falling within the range of 0.229 ± 0.174. This can be attributed primarily to LLM reliance on grey literature and idealized technological scenarios, as well as their limited capacity, to account for the complexities of agricultural work. Future research should focus on integrating expert knowledge into LLM training and improving bias detection and mitigation in agricultural datasets, as well as expanding the range of LLMs studied to enhance assessment reliability. Full article

The integration of cloud computing, IoT (Internet of Things), and artificial intelligence (AI) is transforming precision agriculture by enabling real-time monitoring, data analytics, and dynamic control of environmental factors. This study develops a cloud-driven data analytics pipeline for indoor agriculture, using lettuce as a test crop due to its suitability for controlled environments. Built with Apache NiFi (Niagara Files), the pipeline facilitates real-time ingestion, processing, and storage of IoT sensor data measuring light, moisture, and nutrient levels. Machine learning models, including SVM (Support Vector Machine), Gradient Boosting, and DNN (Deep Neural Networks), analyzed 12 weeks of sensor data to predict growth trends and optimize thresholds. Random Forest analysis identified light intensity as the most influential factor (importance: 0.7), while multivariate regression highlighted phosphorus (0.54) and temperature (0.23) as key contributors to plant growth. Nitrogen exhibited a strong positive correlation (0.85) with growth, whereas excessive moisture (–0.78) and slightly elevated temperatures (–0.24) negatively impacted plant development. To enhance resource efficiency, this study introduces the Integrated Agricultural Efficiency Metric (IAEM), a novel framework that synthesizes key factors, including resource usage, alert accuracy, data latency, and cloud availability, leading to a 32% improvement in resource efficiency. Unlike traditional productivity metrics, IAEM incorporates real-time data processing and cloud infrastructure to address the specific demands of modern indoor farming. The combined approach of scalable ETL (Extract, Transform, Load) pipelines with predictive analytics reduced light use by 25%, water by 30%, and nutrients by 40% while simultaneously improving crop productivity and sustainability. These findings underscore the transformative potential of integrating IoT, AI, and cloud-based analytics in precision agriculture, paving the way for more resource-efficient and sustainable farming practices. Full article

Picking fruits from tall fruit trees manually is laborious and inefficient. Rotary-wing drones, a low-altitude carrier platform, can enhance the picking efficiency for tall fruit trees when combined with picking robotic arms. However, during the operation of rotary-wing drones, the wind field changes dramatically, and the center of gravity of the drone shifts at the moment of picking, leading to poor aerodynamic stability and making it difficult to achieve optimized attitude control. To address the aforementioned issues, this paper constructs a drone and wind field testing platform and employs the Lattice Boltzmann Method and Large Eddy Simulation (LBM-LES) algorithm to solve the high-dynamic, rapidly changing airflow field during the transient picking process of the drone. The aerodynamic structure of the drone is optimized by altering the rotor spacing and duct intake ratio of the harvesting drone. The simulation results indicate that the interaction of airflow between the drone’s rotors significantly affects the stability of the aerodynamic structure. When the rotor spacing is 2.8R and the duct ratio is 1.20, the lift coefficient is increased by 11% compared to the original structure. The test results from the drone and wind field experimental platform show that the rise time () of the drone is shortened by 0.3 s, the maximum peak time () is reduced by 0.35 s, and the adjustment time () is accelerated by 0.4 s. This paper, by studying the transient wind field of the harvesting drone, clarifies the randomness of the transient wind field and its complex vortex structures, optimizes the aerodynamic structure of the harvesting drone, and enhances its aerodynamic stability. The research findings can provide a reference for the aerodynamic optimization of other types of drones. Full article

Durum wheat (Triticum durum Desf.), among the herbaceous crops, is one of the most extensively grown in the Mediterranean area due to its fundamental role in supporting typical food productions like bread, pasta, and couscous. Among the environmental and technical aspects, nitrogen (N) fertilization is crucial to shaping plant development and that of kernels by also affecting their protein concentration. Today, new techniques for monitoring fields using uncrewed aerial vehicles (UAVs) can detect crop multispectral (MS) responses, while advanced machine learning (ML) models can enable accurate predictions. However, to date, there is still little research related to the prediction of the N nutritional status and its effects on the productivity of durum wheat grown in the Mediterranean environment through the application of these techniques. The present research aimed to monitor the MS responses of two different wheat varieties, one ancient (Timilia) and one modern (Ciclope), grown under three different N fertilization regimens (0, 60, and 120 kg N ha⁻¹), and to estimate their quantitative and qualitative production (i.e., grain yield and protein concentration) through the Pearson’s correlations and five different ML approaches. The results showed the difficulty of obtaining good predictive results with Pearson’s correlation for both varieties of data merged together and for the Timilia variety. In contrast, for Ciclope, several vegetation indices (VIs) (i.e., CVI, GNDRE, and SR_RE) performed well (r-value > 0.7) in estimating both productive parameters. The implementation of ML approaches, particularly random forest (RF) regression, neural network (NN), and support vector machine (SVM), overcame the limitations of correlation in estimating the grain yield (R² > 0.6, RMSE = 0.56 t ha⁻¹, MAE = 0.43 t ha⁻¹) and protein (R² > 0.7, RMSE = 1.2%, MAE 0.47%) in Timilia, whereas for Ciclope, the RF approach outperformed the other predictive methods (R² = 0.79, RMSE = 0.56 t ha⁻¹, MAE = 0.44 t ha⁻¹). Full article

Wineries require a significant energy demand for cooling interior spaces. As a result, designing energy-efficient winery buildings has become a crucial concern for winemaking countries. The objective of this study was to evaluate six winery building models with bioclimatic designs, located in the Guadalupe Valley, Baja California, using data on thermal performances (indoor temperature and relative humidity) and energy consumption obtained through dynamic thermal simulation. A baseline winery building model was developed and then enhanced with bioclimatic strategies: a semi-buried building; an underground cellar; an underground cellar with the variants of a green roof, double roof, shaded walls, and polyurethane insulation. The last solution entailed the requirement of a reduction in cooling in the warm season by 98 MWh, followed by the one with a green roof, corresponding to 94 MWh. This study provides valuable insights into the effectiveness of different architectural approaches, offering guidelines for the design of functional buildings for wine production, besides presenting energy-efficient solutions for wineries tailored to the climatic conditions of the study region. These findings highlight the importance of a function-based and energy-efficient architectural design in the winemaking industry, which leads to the definition of buildings with a compact arrangement of the functional spaces and a fruitful integration of the landscape through a wise adoption of underground solutions. Full article

One important element influencing the efficiency of automated timber harvesting is harvester maintenance. However, the understanding of this effect is limited, which can lead to more frequent harvest interruptions and consequently higher production costs. Data modeling can be used to evaluate how mechanical aspects affect harvester maintenance in plantation forests, which can help with forest planning. This study aimed to ascertain if mechanical harvester characteristics may be utilized to develop a high-performance model capable of properly forecasting harvester maintenance using machine learning. A free web application to help forest managers implement the approach was also developed as part of the study. For the modeling, we considered eight mechanical features and the mechanical status as the target feature. In default mode, we ran 25 popular algorithms through the database and compared them based on accuracy and error metrics. Although the combination models performed well, the Random Forest model performed better in the default mode with an accuracy of 0.933. In addition, the generated model makes it possible to create a harvester maintenance prediction tool that provides a quick visualization of the mechanical status feature and can help forest managers make informed decisions. Along with the data from the experimental research, we will make available the complete file containing the predictive model, as well as the software, both developed in the Python language. Full article

This research presents a hybrid approach of Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) model for greenhouse environmental monitoring, integrating machine learning and Internet of Things (IoT)-based sensing to enhance climate prediction and classification. Unlike traditional single-method approaches, this dual-model system provides a comprehensive framework for real-time climate control, optimizing temperature and humidity forecasting while enabling accurate weather classification. The LSTM model excels in capturing sequential patterns, achieving superior temperature prediction performance with a Root-Mean-Square Error (RMSE) of 0.0766, Mean Absolute Error (MAE) of 0.0454, and coefficient of determination (R²) of 0.8825. For humidity forecasting, our comparative analysis revealed that the Simple Recurrent Neural Network (RNN) demonstrates the best accuracy (RMSE: 5.3034, MAE: 3.8041, R²: 0.8187), an unexpected finding that highlights the importance of parameter-specific model selection. Simultaneously, the SVM model classifies environmental states with an accuracy of 0.63, surpassing traditional classifiers such as Logistic Regression and K Nearest Neighbors (KNN). To enhance real-time data collection and transmission, the ESP NOW wireless protocol is integrated, ensuring low latency and reliable communication between greenhouse sensors. The proposed hybrid LSTM-SVM system, combined with IoT technology, represents a significant advancement in proactive greenhouse management, offering a scalable and sustainable solution for optimizing plant growth, resource allocation, and climate adaptation. Full article

This study aims to develop a system combining fluorescence spectroscopy and machine learning through a convolutional neural network (CNN) to identify the origins of various Japanese green teas (Sayama tea, Kakegawa tea, Yame tea, and Chiran tea). Although food origin labeling is important for ensuring consumer quality and safety, ac-curate identification remains a priority for the food industry due to the emergence of problems with false origin labeling. In this study, image data of the fluorescent fingerprints of green teas were collected using fluorescence spectroscopy and analyzed using a CNN model implemented in Python (ver. 3.13.2), TensorFlow (ver. 2.18.0), and Keras (ver. 3.9). The fluorescence of each sample was measured in the range of 250 to 550 nm, highlighting the differences in chemical composition that reflect each region. Using these data, a CNN suitable for image recognition successfully identified the origins of the teas with an average accuracy of 92.83% in 10 trials. For Chiran tea and Yame tea, precision and recall rates of over 95% were achieved, showing clear differences from other regions. In contrast, the classification of Kakegawa and Sayama teas proved challenging due to their similar fluorescence patterns in the 300–350 nm spectral range, corresponding to catechins and polyphenolic compounds. These similarities are presumed to reflect the comparable growing conditions and processing methods characteristic of the two regions. This study shows the potential of this system in food origin identification, suggesting applications in preventing origin fraud and quality control. Future research will aim to extend the system to other regions and foods, enhance data preprocessing to improve accuracy, and develop a versatile identification system. Full article

At a time when global food security is challenged, the importance of phenomics research on rice, as a major food crop, has become more and more prominent. In-depth analysis of rice phenotypic characteristics is of key importance to promote the genetic improvement of rice and sustainable agricultural development. However, it is a challenging task to accurately identify and classify entities from the huge amount of rice phenotypic data. In this study, a deep learning model based on Roberta-two-layer BiLSTM-MHA was innovatively constructed for rice phenomics entity classification. Firstly, with the powerful language comprehension capability of the pre-trained Roberta model, deep feature extraction was performed on the rice phenotype text data to capture the underlying semantic information in the text. Next, the contextual information is comprehensively modelled using a two-layer bidirectional long- and short-term memory network (BiLSTM) to fully explore the long-term dependencies in the text sequences. Finally, a multi-head attention mechanism is introduced to enable the model to adaptively focus on key features at different levels, which significantly improves the classification accuracy of complex phenotypic information. The experimental results show that the model performs excellently in several evaluation metrics, with accuracy, recall, and F1-scores of 89.56%, 86.40%, and 87.90%, respectively. This research result not only provides an efficient and precise entity classification tool for rice phenomics research but also provides a comparable method for other crop phenomics analyses, which is expected to promote the technological innovation in the field of crop genetic breeding and agricultural production. Full article

In agricultural research, effective and efficient disease detection in crops is crucial for enhancing yield and sustainability. This study presents a novel approach to improving YOLOv8, a state-of-the-art object detection model, by integrating the Ghost model with three advanced attention mechanisms: Convolutional Block Attention Module (CBAM), Triplet Attention, and Efficiency Multi-Scale Attention (EMA). The Ghost model optimizes feature extraction by reducing computational complexity, while the attention modules enable the model to focus on relevant regions, improving detection performance. The resulting Ghost-Attention-YOLOv8 model was evaluated on the Rice Leaf Disease dataset to assess its efficacy in identifying and classifying various diseases. The experimental results demonstrate significant improvements in accuracy, precision, and recall compared to the baseline YOLOv8 model. The proposed Ghost YOLOv8s with Efficiency Multi-Scale Attention model achieves a parameter count of 5.5 M, a reduction of 4.3 million compared to the original YOLOv8s model, while the accuracy is improved: the mAP@50 metric reaches 95.4%, a 2.3% increase; and mAP@50–95 improves to 62.4%, an increase of 3.7% over the original YOLOv8s. This research offers a practical solution to the challenges of computational efficiency and accuracy in agricultural monitoring, contributing to the development of robust AI tools for disease detection in crops. Full article

Over the past quarter-century, the enhanced availability of satellite imagery, characterized by improved temporal, spectral, radiometric, and spatial resolutions, has enabled valuable insights into the spatial soil variability of annual croplands and orchards. This study investigates the impact of spatial resolution on classifying three-year, multi-temporal vegetation indices derived from satellites with coarse (30 m, Landsat 8), medium (10 m, Sentinel-2), and fine spatial resolutions (3.7 m, PlanetScope). The classification was performed using the fuzzy c-means algorithm, with the fuzziness performance index (FPI) and normalized classification entropy (NCE), which were used to determine the optimal number of management zones (MZs). Our results revealed that the Landsat 8-based NDVI images produced the highest number of clusters (nine for annual cropland and six for orchards), while the finer resolutions from PlanetScope reduced this to three clusters for both cultivation types, more accurately capturing the intra-parcel variability. Except for Landsat 8, the NDVI means of MZs generated based on Sentinel-2 and PlanetScope using the fuzzy c-means algorithm showed statistically significant differences from each other, as determined by a one-way and Welch’s ANOVA (p < 0.05). The use of PlanetScope imagery demonstrated its superiority in generating zones that reflect inherent variability, offering farmers actionable insights at a reconnaissance scale. Multi-temporal satellite imagery has proved effective in monitoring plant growth responses to edaphological soil properties. In our study, the PlanetScope satellites, which offer the highest spatial resolution, consistently produced effective zones for orchard areas. These zones have the potential to enhance farmers’ discovery of knowledge at a reconnaissance scale. With the increasing spatial resolution and enhanced spectral resolution of newer satellite sensors, using cluster analysis with insights from soil scientists promise to help farmers better understand and manage the fertility of their fields in a cost-effective manner. Full article

Bleeding constitutes an essential stage in pre-processing operations for fish products. In China, this process remains entirely manual, characterized by low efficiency, high labor intensity, and operational hazards, creating a pressing demand for versatile automated equipment in the market. To address the mechanization requirements for efficient freshwater fish bloodletting, we developed an automated fish bleeding machine based on gill-based blood extraction principles, incorporating an impact-triggered positional bleeding methodology. The positional triggering mechanism was engineered through kinematic and dynamic analyses of fish sliding trajectories onto the trigger plate, informed by morphological parameters of fish specimens. This design achieved automated positional awareness and bleeding activation. A reciprocating bleeding mechanism was developed by mimicking manual bleeding motions, leveraging the quick-return motion characteristics of an offset crank-slider mechanism. The transmission system combined chain-drive and clutch mechanisms to enable sequential and intermittent power delivery. Experimental validation employed live specimens of snakehead (Channa argus), grass carp (Ctenopharyngodon idellus), and tilapia (Oreochromis mossambicus), with systematic evaluations including sensory assessments, comparative testing, and performance metrics. Results demonstrated a comprehensive sensory evaluation score of 3.8 for bleeding efficacy; significant influence of baffle geometry on performance, identifying V-shaped baffles as optimal; a bleeding success rate averaging 94% with throughput reaching 1417 fish/hour. The integrated workflow—directional feeding, postural constraint, positional triggering, reciprocating bleeding, and automated ejection—established a cyclic mechanized bleeding process with industrial applicability. Full article

Hyperspectral plant phenotyping is a method that has a wide range of applications in various fields, including agriculture, forestry, food processing, medicine and plant breeding. It can be used to obtain a large amount of spectral and spatial information about an object. However, it is important to acknowledge the inherent limitations of this approach, which include the presence of noise and the redundancy of information. The present study aims to assess a novel approach to hyperspectral data preprocessing, namely Random Reflectance (RR), for the classification of plant species. This study employs machine learning (ML) algorithms, specifically Random Forest (RF) and Gradient Boosting (GB), to analyse the performance of RR in comparison to Min–Max Normalisation (MMN) and Principal Component Analysis (PCA). The testing process was conducted on data derived from the proximal hyperspectral imaging (HSI) of leaves from three different maple species, which were sampled from trees at 7–10-day intervals between 2021 and 2024. The RF algorithm demonstrated a relative increase of 8.8% in the F1-score in 2021, 9.7% in 2022, 11.3% in 2023 and 11.8% in 2024. The GB algorithm exhibited a similar trend: 6.5% in 2021, 13.2% in 2022, 16.5% in 2023 and 17.4% in 2024. It has been demonstrated that hyperspectral data preprocessing with the MMN and PCA methods does not result in enhanced accuracy when classifying species using ML algorithms. The impact of preprocessing spectral profiles using the RR method may be associated with the observation that the synthesised set of spectral profiles exhibits a stronger reflection of the general parameters of spectral reflectance compared to the set of actual profiles. Subsequent research endeavours are anticipated to elucidate a mechanistic rationale for the RR method in conjunction with the RF and GB algorithms. Furthermore, the efficacy of this method will be evaluated through its application in deep machine learning algorithms. Full article

Agriculture, an essential driver of economic expansion, is faced by the issue of sustaining an increasing global population in the context of climatic uncertainty and limited resources. As a result, “Smart Farming”, which uses cutting-edge artificial intelligence (AI) to support autonomous decision-making, has become more popular. This article explores how the Internet of Things (IoT), AI, machine learning (ML), remote sensing, and variable-rate technology (VRT) work together to transform agriculture. Using sophisticated algorithms to predict soil conditions, improving agricultural yield projections, diagnosing water stress from sensor data, and identifying plant diseases and weeds through image recognition, crop mapping, and AI-guided crop selection are some of the main applications investigated. Furthermore, the precision with which VRT applies water, pesticides, and fertilizers optimizes resource utilization, enhancing sustainability and efficiency. To effectively meet the world’s food demands, this study forecasts a sustainable agricultural future that combines AI-driven approaches with conventional methods. Full article

In spaced crop systems, understanding the interactions between different types of vegetation in the agroecosystem and solar radiation is essential for understanding surface radiation dynamics. This study aimed to both seasonally and spatially quantify and characterize the components of the solar radiation balance in the cultivation of green dwarf coconut. The experiment was conducted in Santa Izabel do Pará, Brazil, and monitored the following meteorological parameters: rainfall, incident global radiation (R_g), and net radiation (R_n). Landsat 8 satellite images were obtained between 2021 and 2023, and the estimates for global and net radiation were subsequently calculated. The resulting data were subjected to mean tests and performance index analysis. The dry season showed higher values of R_g and R_n due to reduced cloud cover. In contrast, the rainy season exhibited lower R_g and R_n totals, with reductions of 21% and 23%, respectively. In the irrigated area, a higher R_n/R_g fraction was observed compared to the non-irrigated area, with no significant differences between the row and inter-row zones. In the non-irrigated system, there were no seasonal differences, but a spatial difference between row and inter-row was noted, with the row having higher net radiation (9.95 MJ m⁻² day⁻¹) than the inter-row (8.36 MJ m⁻² day⁻¹), which could result in distinct energy balances at a micrometeorological scale. Spatially, the eastern portion of the study area showed higher global radiation totals, with the radiation balance predominantly ranging between 400 and 700 W m⁻². Based on the performance indices obtained, satellite-based estimates proved to be a viable alternative for characterizing the components of the radiation balance in the region, provided that the images have low cloud cover. Full article

Accidents and deaths at work are a persistent problem, with numbers still worrying. The agricultural and forestry sector is among the most exposed to work risks, with particular attention to noise risk from the use of agricultural machinery and operators. This study aims to analyze the exposure to noise risk during use of wheeled and tracked tractors, with or without a cab, as well as other operating machines. The analysis takes into account the parameters Lpeak (peak sound pressure values), LAeq.T (time-weighted equivalent noise exposure levels) and LAS (maximum and minimum values weighted according to the Slow time constant) in order to assess the noise impact and define strategies for improving the safety and health of workers. This study demonstrates that in multiple cases, the regulatory thresholds for the examined variables are exceeded, regardless of the presence of a cabin. Specifically, Lpeak values approach 140 dB, dangerous to human health, while LAeq.T levels are close to or, in some instances, exceed 87 dB. It is also verified that agricultural and forestry operators who mainly use crawler tractors have greater and constant exposure to noise compared to those who use tractors with a cabin. Full article