AgriEngineering

Accurate and consistent measurements of geometric features such as fruit length and width are essential for the quality assessment of Namdokmai Sithong mangoes. Traditional manual methods are labor-intensive and prone to inconsistency. This study presented an automated system for geometric feature extraction of Namdokmai Sithong mangoes using a YOLOv8-based object detection model. The framework automated the process of measuring key morphological traits, specifically fruit length and width, to improve accuracy and consistency in quality assessment. The model identified an anatomically meaningful reference point for guiding axis-based measurements by detecting the mango and its peduncle. HSV-based image segmentation combined with morphological operations and edge detection effectively calculated the major (length) and minor (top and bottom width) axes of the fruit. Evaluation on 30 test images showed that the proposed method achieved error rates below 5% in over 90% of samples, with average deviations for fruit length typically under 1.5%. The system was implemented as a standalone Python (version 3.12.8) application and demonstrated high potential for use in real-time, automated fruit grading scenarios. Full article

The increasing generation of agricultural waste poses both environmental and economic challenges, particularly in rural areas with limited infrastructure. Anaerobic digestion has emerged as a sustainable alternative, enabling the valorization of waste and the production of biogas and biofertilizer. This study evaluated the economic and environmental gains of mono- and co-digestion of equine manure and vegetable waste using biodigesters of different capacities across four simulated projects—Project 1 (15 m² biodigester with monodigestion), Project 2 (15 m² biodigester with co-digestion), Project 3 (20 m² biodigester with monodigestion), and Project 4 (20 m² biodigester with co-digestion). Economic feasibility was assessed through indicators such as Net Present Value (NPV), Internal Rate of Return (IRR), Modified IRR (MIRR), Profitability Index (PI), Benefit-Cost Ratio (B/C), Discounted Payback Period, sensitivity analysis, and Monte Carlo simulation, adopting a Minimum Attractiveness Rate (MAR) of 6.43% per year. Environmental benefits were estimated based on the annual reduction of CO₂ equivalent emissions. The results showed that all projects were economically viable and had the potential to mitigate up to 36 tons of CO₂eq per year. Additionally, an eco-efficiency indicator (NPV per CO₂eq avoided) was calculated to enable an integrated assessment of economic performance and environmental impact. Projects using 20 m³ biodigesters achieved the best results, with Project 3 being the most eco-efficient (USD256.05/tCO₂eq), while Project 4 yielded the highest absolute return in all economic analysis tools: NPV (USD 9063.81), IRR (25.10%), MIRR (10.95%), PI (USD 1.65), B/C (USD 1.65) and DPP (4.56 years). The integrated analysis underscores the significance of co-digestion and economies of scale in encouraging the adoption of this technology by small rural producers. Full article

This study evaluates the positional accuracy of Global Navigation Satellite Systems (GNSS) and Unmanned Aerial vehicle (UAV)-based LiDAR systems in terrain modeling, using a total station as a reference. The research was conducted over 17 Ground Control Points (GCPs), with measurements obtained using a CHCNAV i50 GNSS receiver and a DJI Zenmuse L1 Light Detection and Ranging (LiDAR) sensor mounted on a UAV. Accuracy was assessed for horizontal (X, Y) and vertical (Z) components by comparing the results against total station data. Errors were quantified using statistical metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and RMS at 1σ. GNSS exhibited superior horizontal accuracy with an RMS 1σ of 1.1 cm, while LiDAR achieved 1.7 cm. In contrast, GNSS outperformed LiDAR in vertical precision, achieving a 1σ RMS of 6.4 cm compared to 6.6 cm for LiDAR. These findings align with manufacturer specifications and international standards such as those of the American Society for Photogrammetry and Remote Sensing (ASPRS). The results highlight that GNSS is preferable for applications requiring high horizontal precision, while LiDAR is better suited for vertical modeling and terrain analysis. The combination of both systems may offer enhanced results for comprehensive geospatial surveys. Overall, both technologies demonstrated sub-decimetric accuracy suitable for precision agriculture, civil engineering, and environmental monitoring. Full article

The mechanisation of sesame (Sesamum indicum L.) planting remains a significant challenge due to the crop’s small, fragile seeds and non-uniform shape, which hinder the effectiveness of standard seeding systems. Crop emergence and production are adversely affected by poor singulation and uneven seed distribution, which are frequently caused by conventional and general-purpose planting equipment. For sesame, consistency in seed distribution and emergence is very important, necessitating careful consideration of agronomic conditions as well as seed properties. This study was conducted as a systematic review following the PRISMA 2020 guidelines to critically evaluate the existing literature on advanced planting methods that prioritise precision, efficiency, and seed protection. A comprehensive search was conducted across Scopus, Web of Science, and Google Scholar for peer-reviewed studies published from 2000 to 2025. Studies focused on the agronomic parameters of sesame, planting technologies, and/or simulation integration, such as Discrete Element Modelling (DEM), were included in this review, and studies unrelated to sesame planting or not available in full text were excluded. The findings from these studies were analysed to examine the interaction between seed metering mechanisms and seed morphology, specifically seed thickness and shape variability. Agronomic parameters such as optimal seed spacing, sowing depth, and population density are analysed to guide the development of effective planting systems. The review also evaluates limitations in existing mechanised approaches while highlighting innovations in precision planting technology. These include optimised seed plate designs, vacuum-assisted metering systems, and simulation tools such as DEM for performance prediction and system refinement. A total of 22 studies were included and analysed using systematic narrative synthesis, grouped into agronomical, technological, and simulation-based themes. The studies were screened for methodological clarity, and reference list screening was performed to reduce reporting bias. In conclusion, the findings of this research support the development of crop-specific planting strategies tailored to meet the unique requirements of sesame production. Full article

The definition of management zones is an essential strategy for optimizing agricultural productivity, enabling the efficient use of inputs and the minimization of environmental impacts. This study aims to identify and classify management zones in a cultivated area, considering spatial variations in soil fertility and carbon content. The methodology employed includes the analysis of spatial data through geotechnologies, combined with fuzzy logic for categorizing areas into management classes. The results indicate that lower-quality regions present distinct edaphic characteristics, being mostly composed of dystrophic Red-Yellow Argisol, which negatively affects productivity due to lower water retention capacity and poor fertility. In addition, a correlation between soil carbon content and fertility was identified, showing that areas with lower carbon stocks tend to be less productive. The application of these techniques allowed for a more precise approach to agricultural management, promoting sustainable practices that enhance productive efficiency and reduce environmental degradation. Full article

Estimated population growth and increased demand for food production bring with them the evident need for more efficient and sustainable production systems. Because of this, computer vision plays a fundamental role in the development and application of solutions that help producers with the issues that limit livestock production in Brazil and the world. In addition to being stressful for the producer and the animal, the conventional pig weighing system causes productive losses and can compromise meat quality, being considered a practice that does not value animal welfare. The objective was to develop a computational procedure to predict the live weight of pigs in the growth and finishing phases, through the volume of the animals extracted through the processing of 3D images, as well as to analyze the real and estimated biometric measurements to define the relationships of these with live weight and volume obtained. The study was conducted at Roçadinho farm, in the municipality of Capoeiras, located in the Agreste region of the state of Pernambuco, Brazil. The variables weight and 3D images were obtained using a Kinect^®—V2 camera and biometric measurements of 20 animals in the growth phase and 24 animals in the finishing phase, males and females, from the crossing of Pietrain and Large White, totaling 44 animals. To analyze the images, a program developed in Python (PyCharm Community Edition 2020.1.4) was used, to relate the variables, principal component analyses and regression analyzes were performed. The coefficient of linear determination between weight and volume was 73.3, 74.1, and 97.3% for pigs in the growing, finishing, and global phases, showing that this relationship is positive and satisfactorily expressed the weight of the animals. The relationship between the real and estimated biometric variables had a more expressive coefficient of determination in the global phase, having presented values between 77 and 94%. Full article

Given the demand for sustainable and cost-effective manure management in livestock systems, this study evaluated the economic feasibility of cattle manure treatment via composting and anaerobic digestion (AD) under different configurations. Five scenarios were compared: composting without solid–liquid separation, AD without separation at 20- and 30-day hydraulic retention times (HRTs), and combined systems with separation, composting the solid fraction and digesting the liquid. The analysis was based on a 200-cow herd and experimental data, with 15-year projected cash flows. Economic indicators included net present value (NPV), internal rate of return (IRR), discounted payback period (DPP), benefit–cost ratio (B/C), modified internal rate of return (MIRR), uniform annual equivalent (UAE), and profitability index (PI), supported by sensitivity analysis and Monte Carlo simulation. All scenarios were viable and posed low risk. Energy and fertilizer value were key drivers. The scenario 30-day HRT without separation had the best financial performance (NPV = 53,407.15 USD; IRR = 15.54%; DPP = 7.33 years; B/C = 1.57; MIRR = 9.28%; UAE = 5654.48 USD; PI = 1.66) and is recommended for capitalized farms seeking higher returns. Composting had lower returns (NPV = 9832.06 USD) but required the lowest investment, remaining a cost-effective alternative for smallholders. Full article

A rice grain’s proximate composition determines its nutritional potential. Macronutrient quantification is essential to identify superior genotypes and direct breeding efforts to reach more people who are vulnerable. Conventional methods to determine proximate composition are highly accurate; however, they remain time-consuming, costly, and destructive. Near-infrared (NIR) spectroscopy enables proximate composition analysis in a non-destructive, rapid, inexpensive, and practical manner, providing results similar to well-established conventional methods. This study aimed to evaluate the feasibility of NIRs-based selection to identify more nutritious rice genotypes. A collection of 155 rice genotypes grown in Southern Brazil was used. After harvest, grains were hulled, polished, and milled. NIRs was used to determine moisture, starch, protein, fat, ash, and fiber contents in rice flour. It was possible to differentiate genotypes with higher and lower levels of the investigated components. Similar and distinct values were observed in comparison to other studies, indicating the accuracy of NIRs and the effect of genotype and environment, respectively. Starch is correlated negatively with protein and fat, preventing the identification of genotypes with high levels of these three components. PCA enabled the separation of the genotypes but highlighted the complexity of sample distribution. NIRs is an effective and accurate method to determine the proximate composition of rice, enabling the selection of more nutritious genotypes. Full article

Compacted and degraded soils pose increasing challenges to agricultural practices, necessitating innovative approaches to soil tillage. This paper evaluates the performance of a vibratory-enhanced moldboard plowing system, designed to improve energy efficiency and tillage quality under compacted and moisture-deficient conditions, typical of low-moisture soils. Field experiments were conducted across four distinct Romanian regions with varying soil types and climatic conditions, all characterized by significant compaction and limited soil moisture. The vibratory system, mounted directly on each plow body, employed sinusoidal oscillations generated by a DC moto-vibrator, to reduce soil adhesion and traction force requirements, thereby lowering fuel consumption. Key parameters including fuel consumption, working speed, soil fragmentation, weed incorporation, and traction force were measured and compared with the conventional plowing method. The results showed enhanced soil fragmentation and more effective residue incorporation, along with notable reductions in traction effort and fuel use at optimal oscillation settings. These findings highlight the potential of vibratory tillage to be used as a soil preparation method for compaction-prone areas, improving the soil structure while increasing operational energy efficiency. Full article

The rapid and accurate identification of pathogenic spores is essential for the early diagnosis of diseases in modern agriculture. Gray mold disease, caused by Botrytis cinerea, is a significant threat to several crops and is traditionally controlled using fungicides or, alternatively, by UV-C radiation. Classically, the determination of conidial germination percentage, a key indicator for assessing pathogen viability, has been a manual, time-consuming, and error-prone process. This study proposes an approach based on deep learning, using one-stage detectors to automate the detection and counting of germinated and non-germinated conidia in microscopy images. We trained and assessed the performance of three models under several metrics: YOLOv8, YOLOv11, and RetinaNET. The results show that these three architectures provide an efficient and accurate solution for the recognition of gray mold conidia viability. Selecting the best model, we performed the task of detecting and counting conidia for determining the germination percentage on samples treated with different UV-C radiation dosages. The results show that these deep-learning models achieved counting accuracies that closely matched those obtained with conventional manual methods, yet they delivered results far more rapidly. Because they operate continuously without fatigue or operator bias, these models begin to open possibilities, after widening field tests and datasets, for efficient and fully automated monitoring pipelines for disease management in the agro-industry. Full article

Fodder maize constitutes a key economic crop in Thailand, particularly in the northeastern region, where it significantly contributes to livestock feed production and local economic development. Nevertheless, the planning and management of cultivation areas remain a major challenge, especially in urban and peri-urban agricultural zones, due to the limited availability of spatial data and suitable analytical frameworks. These difficulties are exacerbated in urban settings, where the complexity of land use patterns and high spectral similarity among land cover types hinder accurate classification. The Google Earth Engine (GEE) platform provides an efficient and scalable solution for geospatial data processing, enabling rapid land use classification and spatiotemporal analysis. This study aims to enhance the classification accuracy of fodder maize cultivation areas in Mueang District, Nakhon Ratchasima Province, Thailand—an area characterized by a heterogeneous mix of urban development and agricultural land use. The research integrates GEE with four machine learning algorithms: Random Forest (RF), Support Vector Machine (SVM), Naïve Bayes (NB), and Classification and Regression Trees (CART). Eleven datasets were developed using Sentinel-2 imagery and a combination of biophysical variables, including elevation, slope, Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and Modified Normalized Difference Water Index (MNDWI), to classify land use into six categories: fodder maize cultivation, urban and built-up areas, forest, water bodies, paddy fields, and other field crops. Among the 44 classification scenarios evaluated, the highest performance was achieved using Dataset 11—which integrated all spectral and biophysical variables—with the SVM classifier. This model attained an overall accuracy of 97.41% and a Kappa coefficient of 96.97%. Specifically, fodder maize was classified with 100% accuracy in both Producer’s and User’s metrics, as well as a Conditional Kappa of 100%. These findings demonstrate the effectiveness of integrating GEE with machine learning techniques for precise agricultural land classification. This approach also facilitates timely monitoring of land use changes and supports sustainable land management through informed planning, optimized resource allocation, and mitigation of land degradation in urban and peri-urban agricultural landscapes. Full article

This study addresses the agricultural requirement for flexible adjustment of planting spacing in seed breeding corn, designing an active rotating in-film hole-forming mechanism driven by an independent motor. The mechanism allows flexible regulation of planting spacing by adjusting the motor speed. The study first optimized the structure of the hole-forming device, selecting a rhombic duckbill as its core component and analyzing its motion trajectory and hole-forming shape. Single-factor experiments were conducted to determine the structural parameter ranges affecting film hole length. Using discrete element and multibody dynamics co-simulation, experiments were carried out with duckbill number, duckbill bottom width, and duckbill bottom height as experimental factors, and film hole length as the response variable, employing a three-factor, three-level orthogonal experimental method. Simulation results indicated that the factors influencing film hole length, in descending order of impact, were duckbill number, duckbill bottom height, and duckbill bottom width. The optimized best structural parameters were: 9 duckbills, bottom height of 351 mm, and bottom width of 22 mm, ensuring film hole length control within the range of 25–40 mm, meeting planting requirements, preventing weed growth, and ensuring a seed growth environment. Furrow testing validated the adaptability and planting performance of the mechanism under different spacing conditions, providing a theoretical basis and practical reference for the promotion of small-scale breeding and the sowing technology on the film for field seed production. Full article

Soil treatment is one of the most energy-intensive agricultural processes. While power take-off (PTO)-powered rotary tillage tools are widely used due to their operational advantages, their energy efficiency requires enhancement. A new PTO-powered rotary tillage tool was designed, with cutting blades inclined at angle β to prevent soil mass accumulation due to soil sliding along the blades, thereby enhancing energy efficiency and tillage quality. A kinematic model was developed to analyze the tool’s motion trajectories. Theoretical analysis substantiated the optimal inclination angle β = 38–42° and elliptical-profile edge configuration of the cutting blades. During field experiments for performance evaluation, the angle of attack was in the range 20° < α < 40°, and the kinematic coefficient varied in the range 1.0 < η < 1.21 in 0.07 increments. Results demonstrated that draught force and torque reduced by 1.3–1.5 and 1.1–1.4 times, respectively, with an increasing kinematic coefficient. Minimal specific total power requirements of 4.5–4.7 kW/m were obtained at the optimal kinematic coefficient, η = 1.14–1.21, and angle of attack, α = 40°. Compared to base ring tillage discs, the new design reduces total power requirements by 14–16%. Furthermore, it provides required tillage quality: soil pulverization ≥ 80%, weed cutting ≥ 97%, crop residue retention ≥ 60%, and roughness of the field soil surface ≤ 3 cm. Full article

The paper presents AgriMicro, a modern Farm Management Information System (FMIS) designed to help farmers monitor and optimize corn crops from sowing to harvest, by leveraging cloud technologies and machine learning algorithms. The platform is built on a modular architecture composed of multiple components implemented through microservices such as the weather and soil service, recommendation and alert engine, field service, and crop service—which continuously communicate to centralize field data and provide real-time insights. Through the ongoing exchange of data between these services, different information pieces about soil conditions, crop health, and agricultural operations are processed and analyzed, resulting in predictions of crop evolution and practical recommendations for future interventions (e.g., fertilization or irrigation). This integrated FMIS transforms collected data into concrete actions, supporting farmers and agricultural consultants in making informed decisions, improving field productivity, and ensuring more efficient resource use. Its microservice-based architecture provides scalability, modularity, and straightforward integration with other information systems. The objectives of this study are threefold. First, to specify and design a modular FMIS architecture based on microservices and cloud computing, ensuring scalability, interoperability and adaptability to different farm contexts. Second, to prototype and integrate initial components and Internet of Things (IoT)-based data collection with machine learning models, specifically Random Forest and XGBoost, to provide maize yield forecasting as a proof of concept. Model performance was evaluated using standard predictive accuracy metrics, including the coefficient of determination (R²) and the root mean square error (RMSE), confirming the reliability of the forecasting pipeline and validated against official harvest data (average maize yield) from the Romanian National Institute of Statistics (INS) for 2024. These results confirm the reliability of the forecasting pipeline under controlled conditions; however, in real-world practice, broader regional and inter-annual variability typically results in considerably higher errors, often on the order of 10–20%. Third, to present a Romania based case study which illustrates the end-to-end workflow and outlines an implementation roadmap toward full deployment. As this is a design-oriented study currently under development, several services remain at the planning or early prototyping stage, and comprehensive system level benchmarks are deferred to future work. Full article

Fixed irrigation systems often experience uneven water pressure across different sections, making it challenging to irrigate all areas efficiently without wasting energy. This issue is particularly evident when using low-power, single-phase motors. To address this, we created a load controller that fine-tunes how these motors work in irrigation systems with changing needs. Our load controller uses a MOC-TRIAC circuit and an Arduino Nano to adjust motor power. Furthermore, we developed a smartphone app that connects via Bluetooth, allowing users to conveniently set the motor power as needed. We tested it on two 1 hp motor pumps, using valves, a flow meter, and pressure gauges to simulate different conditions. By adjusting the “firing angle” of the motor, we were able to change the water pressure by up to 80% while maintaining the flow rate the same. This resulted in energy savings up to 70% and reduced current consumption by 50%. The only limitation occurred at very high-power reductions (75%) and low flow rates (below 3 m³ h⁻¹), where motor overheating was observed. Overall, our load controller presents a promising solution to save energy in irrigation systems by precisely matching motor power to the system’s needs. Full article

Traditional methods of analyzing biofouling in emitters fail to capture the complexity and heterogeneity of their components. Therefore, the objective of this work was to develop and validate an innovative approach that integrates fractal metrics and scanning electron microscopy (SEM) to accurately characterize, quantify, and diagnose biofouling in drippers used with brackish water. For this purpose, tests were conducted on benches that applied brackish water and fresh water through drippers with a flow exponent (x) of 0.46 (NJ), 0.45 (SL), and 0.48 (ST) over 160 h. Biofouling was mapped using advanced diagnostics using SEM and factual metrics, and the results were analyzed using multivariate statistics. The results obtained present important findings for the study, detection, mapping, and proposal of mitigation measures for biofouling in drippers, presenting factual metrics that may be new indicators of clogging. Biofouling is a phenomenon resulting from the interaction between the spatial evolution of the obstructing material, emitter geometry, and irrigation water quality. The combination of SEM and fractal metrics has proven to be an advanced and innovative diagnostic tool for detecting the presence and distribution of biofouling, enabling clogging monitoring and creating more realistic scenarios in hydrodynamic studies to improve or develop emitter designs. Full article

This systematic review analyzed a total of 109 scientific articles with the aim of identifying, organizing, and synthesizing academic output related to digital literacy, technology adoption in agricultural sectors, digital skills, and socioeconomic and cultural factors that influence the implementation of emerging technologies. Peer-reviewed academic publications available in open access and written in English were reviewed, complying with the PRISMA protocol guidelines. They came predominantly from Europe, Asia, and Latin America, which allowed for a global perspective. Quantitative, qualitative, and mixed approaches were applied, highlighting the use of surveys, interviews, and bibliometric analysis. Factors affecting the adoption of precision agriculture by smallholder farmers, challenges for the implementation of technologies in rural contexts, and sociocultural barriers to technological innovation were evaluated. The trend focuses on the need for sound public policies, continuous training strategies, technological accessibility, and contextualized approaches to ensure effective technology adoption. In conclusion, a broad and critical overview of the advances, limitations, and challenges surrounding digital literacy and technology adoption is provided as a basis for an in-depth debate on the digital transformation of contemporary agriculture. Full article

Viticulture, a key economic activity in the Mediterranean area, is facing several challenges including soil degradation. Among the sustainable practices available, the management of cover crops in vineyard inter-rows using a roller crimper to create dead mulch is gaining pace as an effective strategy for soil conservation. Nevertheless, the effectiveness of roller crimpers in terminating groundcovers in vineyards may be reduced by pedoclimatic conditions, type of vegetation and roller crimper configuration and operational parameters. This study aimed to evaluate the effectiveness of a roller crimper with two different weight configurations, light (LR) and ballasted (HR), each tested with one (P1) or two passes (P2), in terminating a cover crop mixture in a vineyard. To evaluate the termination performance, plant green cover data were modeled using a one phase exponential decay nonlinear regression. The four systems were also assessed for their ability to conserve soil moisture and their impact on soil compaction. Although the HR + P2 showed the highest termination performance, the system using the HR + P1 obtained comparable results, with k values of 0.07 and 0.11 days⁻¹ and half-life values of 9.50 and 6.09 days in 2023 and 2024, respectively. Given the need to coordinate multiple vineyard operations within short and weather-dependent timeframes, a one-pass approach such as HR + P1 offers operational advantages, providing a practical compromise between efficacy and efficiency. Full article

In the context of soil management, the porous structure present in these systems plays a relevant role due to its capacity to store and transport water, nutrients, gases, and provide root fixation. A detailed and precise analysis of these structures can assist specialists in determining specific agricultural solutions and management practices for each soil, depending on the characteristics of its porous structure. In this regard, this study presents a hybrid method for segmenting porous structures in micro computed tomography (micro CT) images of carbonate rocks, with a focus on applications in agricultural soil analysis and management. Initially, preprocessing steps such as Contrast Limited Adaptive Histogram Equalization (CLAHE) and histogram specification are applied in order to improve image contrast and uniformity. Subsequently, a UNet convolutional neural network is employed to identify pore contours, followed by the application of two geostatistical approaches, ordinary kriging and Universal Kriging, with the purpose of completing segmentation through the interpolation of unclassified regions. The proposed approach was evaluated using the dataset “16 Brazilian Pre Salt Carbonates”, which includes high-resolution micro CT images. The results show that the integration of UNet with ordinary kriging achieved superior performance, with 79.2% IoU, 93.3% precision, 81.7% recall, and 87.1% F1 Score. This method enables detailed analyses of pore distribution and the porous structure of soils and rocks, supporting a better understanding of inherent characteristics such as permeability, porosity, and nutrient retention in soil, thus contributing to more assisted agricultural planning and more efficient soil use strategies. Full article