AgriEngineering, Volume 7, Issue 4 (April 2025) – 36 articles

Background: Intensive poultry production requires highly efficient drinking systems to ensure both animal welfare and production performance; however, conventional drinkers for meat ducks often suffer from design deficiencies that compromise drinking efficiency and result in significant water wastage. Objectives: To address the drinking water demands in intensive waterfowl farming systems, a specialized drinking device tailored for meat ducks was developed. Methods: The drinking habits of meat ducks were analyzed and the performance of the existing drinkers was evaluated. The deficiencies of the current drinkers were observed and identified by high-speed video, and the parameters of the head of the meat duck were obtained by reverse-engineering technology. Based on this analysis, a specialized drinker for meat ducks was designed, and its performance was confirmed through farming trials. Results: The static and dynamic flow rate tests showed that the output of the new drinker was consistent with the nipple drinker. When the valve rod was pushed upward, the new drinker did not output, which met the design requirements. The results indicated that, under a water pressure of 2.5 kPa, the water loss rate for the designed drinker was 27.4%, which was 15.3% lower than the loss rate of 42.7% observed with the traditional nipple drinker. Conclusion: This study develops a specialized drinker for meat ducks in intensive farming, by utilizing the biting drinking method and incorporating the three-dimensional characteristics of the heads of meat ducks, significantly increasing the effective drinking rate and reducing leakage during the drinking process. Full article

This paper presents an innovative design of a photovoltaic panel system for agricultural applications, particularly in regions prone to drought and extreme temperatures, known as Agri-PV. The proposed solution utilizes optical elements of divergent lens types to illuminate the ground beneath photovoltaic panels in greenhouse or indoor controlled cultivation areas. The Agri-PV solution improves the ratio between the area occupied by the photovoltaic panels and the total cultivated area therefore the land under the photovoltaic panels is fully cultivable, produces clean electricity that can be used in the agricultural process, reduces solar energy at the ground level up to 16 times, reducing water evaporation from the ground diminishing the summer-extreme temperatures effect on crops. With an optimal vertical layout of the optical lens PV system, areas of minimum illumination can be overlapped to provide a more uniform and consistent light intensity at ground level. The overall illumination uniformity is important for maximizing energy efficiency and maintaining optimal growing conditions in agrivoltaics applications. Full article

In the current era of agricultural management practices, known as agricultural 5.0, optimal indoor environments are associated with comfortable temperatures, regulated humidity, and good air quality—essential variables to improve yields. Given this scenario, there is a need for innovative ecosystems that automate indoor environmental monitoring in an affordable and scalable way. This paper presents the scope of the development and validation of an IoT-based ecosystem designed to monitor and control environmental conditions in agricultural barns. The objective is to present a cost-effective and easily accessible environmental monitoring system for barn buildings and agricultural storage areas, promoting the welfare of animals, humans, and crops, and contributing to the sustainable development of the agricultural industry. The system integrates wireless sensors, predictive algorithms, a web interface and cloud infrastructure to optimize temperature and humidity. A proof-of-concept assessment was performed to determine whether the modular architecture offers scalability, while the responsive web interface ensures cross-device accessibility. The results show data accuracy above 95%, prediction efficiency of 96%, and increases in production yields. This solution demonstrates economic and operational advantages over existing technologies, promoting sustainability and automation in agricultural management practices in hangars and barns, in alignment with the United Nations’ Sustainable Development Goals (SDGs). Full article

The advancement of technology in agriculture has driven the use of drones for spraying, with their increasing adoption presenting challenges in calibration and volumetric distribution efficiency. This study aimed to evaluate the volumetric distribution of drone spraying by combining different operational parameters to determine spray swath and application uniformity. Experiments were conducted using a DJI T10 drone and a volumetric distribution table to assess the impact of different flight heights (2, 3, and 4 m), application volumes (8, 12, 16, and 20 L ha⁻¹), and nozzle types (FV 110 015, FL 110 010, and CO 080 010). Environmental conditions were monitored, and data were analyzed using histograms, analysis of variance (ANOVA) by F-test (p ≤ 0.05), and the Scott–Knott test (p ≤ 0.05) to group means. Results indicated that a lower application volume (8 L ha⁻¹) led to greater application uniformity and a narrower spray swath. Higher flight altitude (4 m) resulted in a wider spray swath and a normal distribution of spray deposition. Fine droplet nozzles (CO 080 010) enhanced uniformity, while very coarse droplets (FV 110 015) concentrated more volume in the center of the swath. Thus, using fine droplet nozzles (CO 080 010), lower application volume (8 and 12 L ha⁻¹), and higher flight altitude (4 m) as operational parameters maximizes drone spraying efficiency; however, this also increases drift potential. Full article

High temperature-induced rice sterility has become a major issue in Japan; thus, the conditions influencing this sterility must be better understood to identify effective countermeasures. In this study, a random forest-based sterility estimation model was developed using the sterility rate measured via a field survey and satellite images. Applying this model to Nankoku, Japan, we attempted to map fields based on their sterility rates and visualize the spatial distribution of sterility. The results showed that the rate of change in reflectance from the heading stage until an effective accumulated temperature of 350 °C was reached was an effective model variable. Applying this model to map fields where rice sterility occurred from 2022 to 2024 revealed that more than 41% of the fields in Nankoku may have been damaged, suggesting that many fields might be at risk of adverse effects from high temperatures. The 3-year average sterility rate revealed areas with a high concentration of paddies with a low sterility rate, suggesting that investigating the environment and cultivation management techniques in these areas could provide insights to reduce the sterility rate. Moreover, the growth process up to the heading stage may contribute to the increase in the sterility rate. In the future, we plan to conduct a longitudinal survey based on the generated map to further investigate the relationships between cropping type, cultivar, and weather conditions to develop countermeasures. Full article

This study presents an experimental and computational analysis of the specific draft (SD) and specific torque (ST) requirements of an energy-efficient tillage implement, the active–passive disk harrow (APDH). Soil bin trials were conducted to develop multiple regression models predicting SD and ST based on operational parameters such as gang angle (α), speed ratio (u/v), soil cone index, and working depth. Model’s accuracy was assessed through statistical indices such as R², RMSE, MIE, and MAE. The high R² and low RMSE confirmed the reliability of the developed models in capturing the relationships between input and output variables. A genetic algorithm-based multi-objective optimization was implemented in MATLAB R2016a to determine optimal operational settings that minimize total power consumption while maximizing soil pulverization. The optimized values of α and u/v were determined to be in the ranges of 35.91° to 36.98° and 3.27 to 3.87, respectively. Model validation with laboratory and field data demonstrated acceptable prediction accuracy despite minor deviations attributed to soil variability and measurement errors. The developed models provide a predictive framework for optimizing tillage performance, aiding in tractor-implement selection, and enhancing energy efficiency in agricultural operations. Full article

Agricultural production is a critical sector that directly impacts the economy and social life of any society. The identification of plant disease in a real-time environment is a significant challenge for agriculture production. For accurate plant disease detection, precise detection of plant leaves is a meaningful and challenging task for developing smart agricultural systems. Most researchers train and test models on synthetic images. So, when using that model in a real-time scenario, it does not give a satisfactory result because when a model trained on images of leaves is fed with the image of the plant, then its accuracy is affected. In this research work, we have integrated two models, the Segment Anything Model (SAM) with YOLOv8, to detect the tomato leaf of a tomato plant, mask the leaf, and extract the leaf in a real-time environment. To improve the performance of leaf disease detection in plant leaves in a real-time environment, we need to detect leaves accurately. We developed a system that will detect the leaf, mask the leaf, extract the leaf, and then detect the disease in that specific leaf. For leaf detection, the modified YOLOv8 is used, and for masking and extraction of the leaf images from the tomato plant, the Segment Anything Model (SAM) is used. Then, for that specific leaf, an image is provided to the deep neural network to detect the disease. Full article

A laser-based selective wax ablation method using a 532 nm Nd:YAG laser was developed to improve the foliar uptake efficiency of agrochemicals in citrus leaves. In contrast to conventional applications that suffer major losses, our approach exposes up to 80% of the underlying epidermis (within the irradiated footprint) with no visible tissue damage, thereby substantially enhancing substance penetration. Efficacy was confirmed using two indicators: (1) A fluorescent glucose analog (2-NBDG) exhibited a radial expansion velocity reaching 0.0105 mm/min in treated areas, enabling rapid phloem transport across an 8 cm distance within just three minutes—an 11,280% improvement over untreated controls. (2) Laser-induced breakdown spectroscopy (LIBS) demonstrated a threefold increase in zinc (Zn) uptake (and over fivefold compared to untreated leaves) when using a Zn-based foliar fertilizer. To assess processing efficiency, we quantified the ablation footprint by combining single-pulse laser shots in a 1 cm-diameter region and found that 23.4% of the total area was fully exposed. This selective, non-invasive approach enables precise targeting, potentially reducing fertilizer and pesticide usage while improving crop health. Beyond citrus, it is readily adaptable to other crops, with integration into orchard or greenhouse spraying systems as a promising path for scale-up. Such versatility highlights the technique’s potential to optimize efficacy, cut input costs, and diminish environmental impact in modern precision agriculture. Full article

Smart greenhouses rely on precise environmental control to optimize crop yields and resource efficiency. In this study, we propose a novel hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) architecture to predict fan actuator states based on environmental data. The hybrid model integrates CNNs for spatial feature extraction and LSTMs for temporal dependency modeling, enhanced by a custom activation function and loss function tailored for the problem’s characteristics. The model was trained and evaluated on a comprehensive dataset containing 37,923 samples with 13 environmental features, collected from a smart greenhouse. Experimental results demonstrate the superior performance of the hybrid CNN-LSTM model, achieving an accuracy of 0.9992, precision of 0.9989, recall of 0.9996, and an F1 score of 0.9992, significantly outperforming traditional machine learning methods such as Random Forest and Gradient Boosting, as well as standalone CNN and LSTM architectures. The high recall underscores the model’s reliability in identifying positive actuator states, critical for greenhouse management. This study highlights the importance of hybrid architectures in handling complex spatiotemporal data, offering potential applications beyond greenhouses, such as healthcare monitoring and predictive maintenance. Despite the model’s strengths, limitations include computational complexity and limited interpretability, necessitating future work on optimization and explainability. These findings establish a foundation for integrating deep learning into smart agricultural systems, advancing the automation and efficiency of environmental control mechanisms. Full article

Flowering intensity is an important parameter to predict and control fruit yield. However, its estimation is often based on subjective evaluations of fruit growers. This study explores the application of the YOLO framework for flowering intensity estimation. YOLO is a popular computer vision solution for object-detecting tasks. It was applied to detect flowers in different studies. Still, it requires manual annotation of photographs of flowering trees, which is a complex and time-consuming process. It is hard to distinguish individual flowers in photos due to their overlapping and indistinct outlines, false positive flowers in the background, and the density of flowers in panicles. Our experiment shows that the small dataset of images (320 × 320 px) is sufficient to achieve an accuracy of 0.995 and 0.994 mAP@50 for YOLOv9m and YOLOv11m using aggregated mosaic augmentation. The AI-based method was compared with the manual method (flowering intensity estimation, 0–9 scale). The comparison was completed using data analysis and the MobileNetV2 classifier as an evaluation model. The analysis shows that the AI-based method is more effective than the manual method. Full article

The process of droplet formation during spraying is influenced by several factors, including the nozzle type and the use of adjuvants. This study aimed to investigate the effect of adding adjuvants to spray solutions using different nozzles, with a focus on droplet spectra, and to examine the impact of the contact angle and the surface tension on this process. The surface tension and contact angle were evaluated using a droplet shape analyzer. The experiment was conducted in a completely randomized design (CRD) using four treatment solutions: water alone and water mixed with three different types of adjuvants, including fatty acid esters (vegetable oil-based), polyether–polymethyl, and polydimethyl-siloxane. The droplet spectra (volume median diameter, relative amplitude, and droplets smaller than 100 µm) were assessed using a particle size analyzer. A CRD with a 4 × 2 factorial scheme was used to assess the effects of the four treatment solutions and two flat-fan nozzles (ULD 120-02 with air induction and LD 110-02 without air induction technology). The polyether–polymethyl considerably reduced the contact angle and surface tension (226% and 180%, respectively, in relation to water). However, it did not homogenize the droplet spectra or reduce the drift risk. The vegetable oil-based adjuvant increased the droplet size when the standard flat-fan nozzle was used. No significant correlation was found between the surface tension and contact angle regarding the droplet spectra parameters. The effect of adjuvants on the droplet spectra was found to be dependent on the nozzle type, which prevents generalizations about the implications of their use. Full article

In this study, alfalfa (Medicago sativa L.) is evaluated for suitability of variable rate irrigation (VRI) by analyzing within-field variation in crop water productivity (CWP) under uniform irrigation. The objectives were to (1) measure within-field variation in crop evapotranspiration (ET), (2) quantify spatial variability of alfalfa biomass yield, and (3) assess whether a bivariate analysis of CWP and yield could inform VRI management zones. Research was conducted on a 22.6 ha center-pivot irrigated alfalfa field near Rexburg, Idaho, USA, over three harvest intervals (HIs) in 2021 and 2022. Using a water balance method at 66 field points, ET exhibited significant spatial clustering for each HI (p < 0.001 for all HIs), though spatial patterns varied among HIs. Biomass yield, measured via the quadrat method, ranged from 2.1 to 9.7 Mg ha⁻¹, with significant spatial clustering (p < 0.001 for all HIs). The CWP ranged from 0.07 to 0.54 Mg ha⁻¹ cm⁻¹, also showing significant spatial clustering (p < 0.001 for all HIs). Bivariate cluster analysis indicated 12–18% more area of the field was over-watered than under-watered, suggesting potential for optimizing irrigation with VRI. Reducing irrigation in these over-watered zones could improve CWP, supporting alfalfa as a viable candidate for VRI. Full article

Sugarcane (Saccharum spp.) is a crop of significant industrial and nutritional value, essential for producing various products. Due to its importance, genetic improvement programs involve a rigorous selection process. In this study, growth curve models were used to analyze the maturity curves of 33 hybrids (currently in the adaptability testing phase) and 6 control varieties (MEX 69-290, ITV 92-1424, CP 72-2086, COLMEX 94-8, COLMEX 95-27, RB 85-5113) during both plant and ratoon periods at the Melchor Ocampo Sugar Mill fields in Jalisco, México. With the use of clustering techniques, the materials were classified into four maturity groups: early, early–intermediate, intermediate–late, and late. Hybrids with a larger intercept and smaller slope were classified as having early and early–intermediate maturity. Conversely, hybrids with a smaller intercept and larger slope were classified as having intermediate–late and late maturity. According to the Connectivity and Dunn indexes, the DBSCAN algorithm provides the best clustering structure for materials in the plant cycle, while for the ratoon cycle, the k-means algorithm offers the best clustering structure. This highlights the versatility of each algorithm in the context of hybrid and varietal maturity analysis. These results are crucial for optimizing the productivity and sustainability of the crop, with significant implications for the sugar industry. Full article

This study presents the development and evaluation of a microwave-assisted prototype for scalable red palm oil production. The prototype, equipped with industrial magnetrons delivering a combined power of 2 kW, is designed to process up to 6 kg of oil palm fruit per batch. The design, optimized using COMSOL Multiphysics simulations, focused on waveguide configurations and cavity dimensions to ensure uniform energy distribution and minimize hotspots. Performance testing validated the system’s capability to deliver consistent heating across six trays and produce high-quality red palm oil. Results demonstrated a significant reduction in free fatty acid (FFA) content from 20.4% to 2.1% while retaining carotene content within the industrial standard range (558.2 ppm). The Deterioration of Bleachability Index (DOBI) showed a slight reduction but remained within acceptable limits, underscoring the prototype’s ability to maintain oil clarity and processability. Microwave heating effectively inactivated lipase enzymes, reducing FFA and enhancing oil stability, as confirmed by previous studies. The chemical-free process preserved essential nutrients, aligning with sustainability goals. This innovative system provides a scalable, energy-efficient solution for community and industrial applications, offering improved product quality with minimal environmental impact. Future work will focus on optimizing the system further and exploring its applications in broader agricultural processing contexts. Full article

Excess water in agricultural fields can significantly limit crop productivity. Drone technology offers solutions for identifying and predicting drainage degradation. This study utilized drone-based photogrammetry to create high-resolution elevation models, multispectral imagery for vegetation indices, and flood simulations models to identify zones at risk of poor surface drainage. These models, collected from 2021 to 2023, were used to assess the relationship between poor drainage and corn productivity. The findings revealed a substantial decline in productivity in poorly maintained surface drainage areas, notably a decrease in mean plant height from 1.43 m in 2022 to 0.26 m in flood-prone areas in 2023. Flood-prone zones expanded significantly, from 37% to 61% of the field area between 2022 and 2023, emphasizing the negative cumulative impacts of pre-existing drainage issues. Conversely, fields receiving regular annual maintenance showed an increase in mean plant heights (from 2.23 m to 2.54 m) and NDVI values, reflecting improved drainage conditions. This research demonstrates the effectiveness of drone-derived elevation models for proactively identifying problematic drainage areas, allowing farmers to make informed decisions regarding field maintenance. Implementing these technologies can optimize drainage management practices, enhance agricultural productivity, and increase economic viability in regions that rely on surface drainage. Full article

Controlling forage quality and grazing are crucial for sustainable livestock production, health, productivity, and animal performance. However, the limited availability of reliable handheld sensors for timely pasture quality prediction hinders farmers’ ability to make informed decisions. This study investigates the in-field dynamics of Mombasa grass (Megathyrsus maximus) forage biomass production and quality using optical techniques such as visible imaging and near-infrared (VIS-NIR) hyperspectral proximal sensing combined with machine learning models enhanced by covariance-based error reduction strategies. Data collection was conducted using a cellphone camera and a handheld VIS-NIR spectrometer. Feature extraction to build the dataset involved image segmentation, performed using the Mahalanobis distance algorithm, as well as spectral processing to calculate multiple vegetation indices. Machine learning models, including linear regression, LASSO, Ridge, ElasticNet, k-nearest neighbors, and decision tree algorithms, were employed for predictive analysis, achieving high accuracy with R² values ranging from 0.938 to 0.998 in predicting biomass and quality traits. A strategy to achieve high performance was implemented by using four spectral captures and computing the reflectance covariance at NIR wavelengths, accounting for the three-dimensional characteristics of the forage. These findings are expected to advance the development of AI-based tools and handheld sensors particularly suited for silvopastoral systems. Full article

Remotely piloted aircraft (RPA) are essential in precision coffee farming due to their capability for continuous monitoring, rapid data acquisition, operational flexibility at various altitudes and resolutions, and adaptability to diverse terrain conditions. This study evaluated the soil water conditions in a coffee plantation using remotely piloted aircraft to obtain multispectral images and vegetation indices. Fifteen vegetation indices were chosen to evaluate the vigor, water stress, and health of the crop. Soil samples were collected to measure gravimetric and volumetric moisture at depths of 0–10 cm and 10–20 cm. Data were collected at thirty georeferenced sampling points within a 1.2 ha area using GNSS RTK during the dry season (August 2020) and the rainy season (January 2021). The highest correlation (51.57%) was observed between the green spectral band and the 0–10 cm volumetric moisture in the dry season. Geostatistical analysis was applied to map the spatial variability of soil moisture, and the correlation between vegetation indices and soil moisture was evaluated. The results revealed a strong spatial dependence of soil moisture and significant correlations between vegetation indices and soil moisture, highlighting the effectiveness of RPA and geostatistics in assessing water conditions in coffee plantations. In addition to soil moisture, vegetation indices provided information about plant vigor, water stress, and general crop health. Full article

As agricultural tractors function under various soil and environmental conditions, optimising their design and paraameter settings for enhanced traction performance is essential for maximising their operational efficiency. This study aimed to assess the traction capabilities of standard tractors, ensuring effective operations even under highly demanding conditions. A traction load measurement system was refined to collect performance data, and standardised tests were conducted on a concrete track to evaluate key traction metrics. The analysis considered drawbar pull, traction force, travel reduction (slip), and net traction ratio. Two tractors from the same model series, featuring similar design characteristics but differing in engine power, were compared. Three primary parameters—tractor mass, tyre pressure, and engine power—were evaluated across six distinct operating conditions. Results recorded at forward speeds below 6 km/h indicated that lower tyre pressure led to an increased net traction ratio due to the enhanced drawbar pull. Additionally, an increase in tractor mass contributed to a higher drawbar pull, which, in turn, improved traction force across all speed ranges. The maximum traction force was not significantly affected between 66 kW and 86 kW tractors at speeds up to 4 km/h. Meanwhile, the traction force remained high up to velocities of 6 km/h in the 86 kW tractor. The efficiency (i.e., the ratio between measured and declared power) varied between 64% and 70% for the 66 kW tractor and between 70% and 74% for the 86 kW tractor. The travel reduction was mainly affected by the power of the tractor. The slip caused a reduction close to 4 and 6 km/h for the 66 kW and 86 kW agricultural tractors, respectively. Overall, the proper adjustment of tractor parameters significantly impacted their traction performance, and the findings provide valuable insights for improving tractor designs and field applications. Full article

This study is focused on assessing the impacts of different regional climate model targeted simulations performed at convection-permitting resolution (CPRCM) in the AgS crop model yield simulations, evaluating to what extent climate model uncertainty impacts the modeled yield—considering the spatial and temporal variability of crop yield simulations over central-south Brazil. The ensemble of CPRCMs has been produced as part of a Flagship Pilot Study (FPS-SESA) framework, endorsed by the Coordinated Regional Climate Downscaling Experiment (CORDEX). The AgS simulated crop yield exhibited significant differences, in both space and time, among the simulations driven by the different CPRCMs as well as when compared with the simulations driven by observations. Rainfall showed the highest uncertainty in CPRCM simulations, particularly in its spatial variability, whereas modeled temperature and solar radiation were generally more accurate and exhibited smaller spatial and temporal differences. The results evidenced the need for multi-model simulations to account for different uncertainty, from different climate models and climate models parameterizations, in crop yield estimations. Inter-institutional collaboration and coordinated science are key aspects to address these end-to-end studies in South America, since there is no single institution able to produce such CPRCM-CropModels ensembles. Full article

This study investigates the application of hyperspectral imaging (HSI) combined with machine learning (ML) models for the dynamic mapping of total phenolic content (TPC) and total flavonoid content (TFC) in sunflower microgreens. Spectral data were collected across different cultivation durations (Days 5, 6, and 7) to assess the secondary metabolite distribution in leaves and stems. Overall, the results indicate that TFC in leaves peaked on Day 5, followed by a decline on Days 6 and 7, while stems exhibited an opposite trend. However, TPC did not show a consistent pattern. Spectral reflectance analysis revealed higher near-infrared reflectance in leaves compared to stems. The variation in trait and spectral data among the collected samples was sufficient to develop models predicting the TPC and TFC content. K-nearest neighbours provided the highest predictive accuracy for TPC ($R^2$ = 0.95 and 1.6 mg GAE/100 g) and ridge regression performed best for TFC ($R^2$ = 0.97 and 6.1 mg QE/100 g). Dimensionality reduction via principal component analysis (PCA) proved effective for TPC and TFC prediction, with PC1 alone achieving performance comparable to the full spectral dataset. This integrated HSI-ML approach offers a non-destructive, real-time method for monitoring bioactive compounds, supporting sustainable agricultural practices, optimising harvest timing, and enhancing crop management. The findings can be further developed for smart microgreen farming to enable real-time secondary metabolite quantification, with future research recommended to explore other microgreen varieties for broader applicability. Full article

Rapid population growth, rising food demand, and climate change have created significant challenges to meet the water demands for agriculture. Effective irrigation water management is essential to address the world’s water crisis. The transition from conventional, frequently ineffective gravity-driven irrigations to contemporary, pressure-driven precision irrigation methods are explored in this article, addressing the difficulties associated with water-intensive irrigation, the possibility of updating conventional techniques, and the developments in smart and precision irrigation technologies. This study comprehensively analyses published literature of 150 articles from the year 2005 to 2024, based on titles, abstract, and conclusions that contain keywords such as precision irrigation scheduling, water-saving technologies, and smart irrigation systems, in addition to providing potential solutions to achieve sustainable development goals and smart agricultural production systems. Moreover, it explores the fundamentals and processes of smart irrigation, such as open- and closed-loop control, precision monitoring and control systems, and smart monitoring methods based on soil data, plant water status, weather data, remote sensing, and participatory irrigation management. Likewise, to emphasize the potential of these technologies for a more sustainable agricultural future, several smart techniques, including IoT, wireless sensor networks, deep learning, and fuzzy logic, and their effects on crop performance and water conservation across various crops are discussed. The review concludes by summarizing the limitations and challenges of implementing precision irrigation systems and AI in agriculture along with highlighting the relationship of adopting precision irrigation and ultimately achieving various sustainable development goals (SDGs). Full article

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 ($R^2$) 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

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