Search Results (3,254)

The A* algorithm is broadly identified for its application in diverse fields, such as agriculture, robotics and GPS technology, due to its effectiveness in route planning. Despite its broad utility, the algorithm faces inherent limitations regarding operational efficiency and the length of the paths it generates. Addressing these constraints, this paper proposes an enhancement to the traditional A* algorithm that significantly improves its performance. Our innovative approach integrates Euclidean and Chebyshev distances into a single heuristic function, thereby enhancing pathfinding accuracy and flexibility. This combined heuristic leverages the strengths of both distance measures: the Euclidean distance provides an accurate straight-line measure between points, while the Chebyshev distance effectively handles scenarios allowing diagonal movement. Furthermore, we incorporate Bezier curves into the algorithm to smooth the generated paths. This addition is particularly advantageous in agricultural environments, where machinery must navigate complex terrains without causing damage to crops. The smooth paths produced by Bezier curves ensure more efficient and safer navigation in such settings. Comprehensive experiments conducted in various agricultural scenarios demonstrate the superior performance of the enhanced algorithm. These results reveal that the improved algorithm not only reduces the computation time needed for route planning but also generates shorter and smoother paths compared to the standard A* algorithm. The proposed approach significantly enhances the operational efficiency and route optimization capabilities of the A* algorithm, making it more suitable for complex and dynamic applications in agriculture. This advancement also holds promise for improving navigation systems in various other domains. Full article

Background/Objectives: Dopamine (DA), a chemical commonly associated with neuroscience and human physiology, has been the subject of growing interest in the field of agriculture due to its potential applications. Methods: This comprehensive review examines the multifaceted role of dopamine in agricultural practices, elucidating its chemical characteristics, biological activities, and diverse applications. The review examines the chemical properties and physiological functions of dopamine in plants, highlighting the unique characteristics that make it suitable for agricultural applications. A significant portion of the review is dedicated to analyzing the biological activities of dopamine, particularly its antioxidant properties, and exploring the underlying mechanisms. The review also delves into the potential of dopamine to enhance crop growth, yield, and quality and investigates the influence of dopamine on plant physiology and metabolism. Results: Furthermore, the review provides a forward-looking perspective on the prospects of dopamine in agriculture, identifying emerging trends and areas of innovation that hold promise for sustainable and resilient farming systems. Conclusions: In summary, this review consolidates the current knowledge surrounding dopamine’s potential in agriculture, underscoring its versatility as a natural tool for growth enhancement and environmental sustainability, and offering valuable insights for researchers, practitioners, and policymakers seeking innovative approaches to address the challenges of modern agriculture. Full article

The stroke control of a hydrostatic transmission (HST) is crucial for improving the energy efficiency and power variability of heavy-duty vehicles, including agricultural, construction, mining, and forestry equipment. This study introduces a new control strategy: an Artificial Neural Network (ANN) controller that imitates a Multiple Model Predictive Controller (MPC). The goal is to compare their performance in controlling the HST’s stroke. The proposed controller is designed to track complex stroke reference trajectories for both primary and secondary regulations under realistic disturbances, such as engine and load torques, which are influenced by soil and road conditions for an HST system in line with a nonlinear and time-varying mathematical model. Processor-in-the-Loop simulations suggest that the ANN controller holds several advantages over the Multiple MPC and classical control strategies. These benefits include its suitability for multi-input–multi-output systems, its insensitivity to external stochastic disturbances (like white noise), and its robustness against modeling errors and uncertainties, making it a promising option for real-time HST implementation and better than the Multiple MPC scheme in terms of simplicity and computational cost-effectiveness. Full article

Inside the transmission group of an agricultural tractor, the efficiency of power transfer to moving parts, their lubrication, and protection from wear are guaranteed by UTTO (Universal Tractor Transmission Oil) fluids, which are also used to operate the hydraulic system. These fluids, with mineral or synthetic origin, are characterized by excellent lubricating properties, high toxicity, and low biodegradability, which makes it important to replace them with more eco-sustainable fluids, such as those based on vegetable oils that are highly biodegradable and have low toxicity. It is also important to consider EU policies on the use of such fluids in sensitive environmental applications. To this end, several experimental bio-UTTO formulations were tested at CREA to evaluate—compared to conventional fluids—their suitability for use as lubricants for transmissions and hydraulic systems through endurance tests carried out in a Fluid Test Rig (FTR) specifically developed by CREA to apply controlled and repeatable work cycles to small volumes of oil, which are characterized by high thermal and mechanical stresses. The technical performance and the main physical–chemical parameters of the fluids were continuously monitored during the work cycles. Based on these experiences, this study describes the first application of a methodological approach aimed at testing an experimental biobased UTTO on a tractor used in normal farm activity. The method was based on a former test at the FTR in which the performance of the bio-UTTO was compared to that of the conventional UTTO recommended by the tractor manufacturer. Given the good results of the FTR test, bio-UTTO was introduced in a 20-year-old medium-power tractor, replacing the mineral fluid originally supplied, for the first reliability tests during its normal use on the CREA farm. After almost 600 h of work, the technical performance and the trend of chemical–physical parameters of bio-UTTO did not undergo significant changes. No damage to the tractor materials or oil leaks was observed. The test is still ongoing, but according to the results, in line with the indications provided by the FTR test, the experimental bio-UTTO seems suitable for replacing the conventional fluid in the tractor used in this study. Full article

With the development of modern agricultural technology, plant factories have become an important way to achieve efficient and sustainable crop production. Accurate understanding of the light received by plants is the key to improving the light energy utilization efficiency of lamps and ensuring the benefits of plant factories. Ray tracing technology, as one of the key technologies in plant factories, is of great significance to analyze the growing light environment of vegetables. Spinach has high nutritional value and is loved by the public and is one of the main crops grown in plant factories. In this paper, LightTools, TracePro, and Ansys Lumerical FDTD Solution, which are currently mature light environment tracking software in the field of lighting, are selected as the research objects to investigate their performance in simulating the light environment of spinach leaf surfaces under different planting arrangements and different lamp source distances. The results show as follows: Under the rectangular planting arrangement, the leaves received more light, and the plants grew faster. Different planting arrangements of plants had little effect on the simulation effect of the same kind of software, but the simulation effect of the three kinds of software under the same planting arrangement was significantly different, and the difference between the simulated value and the measured value of TracePro was the least. Further, TracePro was used to trace and simulate the leaf surface light conditions of spinach under a rectangular planting arrangement at different lighting distances, and the simulation results showed that there was no significant difference between the software simulation value and the measured value, and the simulation accuracy was the highest when the distance from the light source was 30 cm. Therefore, TracePro software can accurately simulate the light intensity of spinach leaves during the growth process and is most suitable for monitoring the change of light environment of spinach growth in plant factories. Full article

The prolonged and intensive use of chemical inputs in agriculture, particularly synthetic fertilizers, has generated a variety of environmental and agronomic challenges. This has intensified the need for alternative, viable, and sustainable solutions. Plant-associated microbes have emerged as promising candidates in this regard. While research has largely focused on bacteria and fungi, comparatively less attention has been paid to other microbial groups such as microalgae and cyanobacteria. These photosynthetic microorganisms offer multiple agronomic benefits, including the ability to capture CO₂, assimilate essential micro- and macroelements, and synthesize a wide range of high-value metabolites. Their metabolic versatility enables the production of bioactive molecules with biostimulant and biocontrol properties, as well as biofertilizer potential through their intrinsic nutrient content. Additionally, several cyanobacterial species can fix atmospheric nitrogen, further enhancing their agricultural relevance. This review aims to summarize the potential of these microorganisms and their application in the agriculture sector, focusing primarily on their biofertilization, biostimulation, and biocontrol capabilities and presents a compilation of the products currently available on the market that are derived from these microorganisms. The present work also identifies the gaps in the use of these microorganisms and provides prospects for developing a suitable solution for today′s agriculture. Full article

Reliable and fast communication between unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) is essential for effective coordination in agricultural settings, particularly when human involvement is part of the system. This study systematically compares two communication architectures representing centralized and decentralized communication frameworks: (a) MAVLink (decentralized) and (b) Farm Management Information System (FMIS) (centralized). Field experiments were conducted in both empty field and orchard environments, using a rotary UAV for worker detection and a UGV responding to intent signaled through color-coded hats. Across 120 trials, the system performance was assessed in terms of communication reliability, latency, energy consumption, and responsiveness. FMIS consistently demonstrated higher message delivery success rates (97% in both environments) than MAVLink (83% in the empty field and 70% in the orchard). However, it resulted in higher UGV resource usage. Conversely, MAVLink achieved reduced UGV power draw and lower latency, but it was more affected by obstructed settings and also resulted in increased UAV battery consumption. In conclusion, MAVLink is suitable for time-sensitive operations that require rapid feedback, while FMIS is better suited for tasks that demand reliable communication in complex agricultural environments. Consequently, the selection between MAVLink and FMIS should be guided by the specific mission goals and environmental conditions. Full article

The utilization of renewable energy, particularly solar panels, has rapidly developed as a solution to reduce dependence on fossil fuels and carbon emissions. This study examines the application of solar panels across various sectors, including transportation, residential, commercial, industrial, and agricultural, using a systematic literature review (SLR) approach. The results indicate that solar panels provide significant benefits in supporting energy sustainability, such as high efficiency in electric vehicles, carbon emission reduction in the transportation sector, and energy cost savings in commercial buildings. In the agricultural sector, solar panels are used for irrigation and crop storage. Additionally, technological advancements such as bifacial panels and integration with energy storage systems enhance efficiency and application flexibility. However, challenges such as high initial costs, location limitations, and technological efficiency remain major barriers. Through an analysis of the advantages and disadvantages of three types of solar panels (monocrystalline, polycrystalline, and thin-film), this study provides strategic guidance for selecting the most suitable technology for specific needs. The study concludes that the adoption of solar panels can be accelerated through technological innovation, cost reduction, and government policy support. With optimal utilization, solar panels have significant potential to drive the transition toward sustainable energy in the future. Full article

Modern intelligent robotic systems offer farmers a promising solution to labor shortages caused by socio-economic instability and/or pandemics. Efficient harvesting of delicate fruits is one of the main needs in this area. In this context, this work presents a simple and low-cost improvement of the ability of a servo-electric gripper to adjust its force when picking delicate fruits without damaging them. Specifically, this module utilizes a microcontroller that intercepts the current consumed by the servomotor during the gripping action and properly adjusts its aperture, with respect to the force limits suitable for each type of fruit. Experiments were performed on various objects, from elastic balls to oranges, tomatoes and sweet bell peppers. These experiments revealed that the relationship between current consumption and applied force can be accurately approximated by nonlinear expression equations and verified the good performance of the proposed force limitation technique. Consequently, there is scope for adoption by a wide range of agricultural automation systems. Full article

Fusarium head blight (FHB), a globally significant agricultural disaster, causes annual losses of dozens of millions of tons of wheat toxins produced by FHB, such as deoxyroscyliaceol, further pose serious threats to human and livestock health. Consequently, rapid and non-destructive determination of FHB severity is crucial for implementing timely and precise scientific control measures, thereby ensuring wheat supply security. Therefore, this study adopts hyperspectral imaging (HSI) combined with a lightweight deep learning model. Firstly, the wheat ears were inoculated with Fusarium fungi at the spike’s midpoint, and HSI data were acquired, yielding 1660 samples representing varying disease severities. Through the integration of multiplicative scatter correction (MSC) and uninformative variable elimination (UVE) methods, features are extracted from spectral data in a manner that optimizes the reduction of feature dimensionality while preserving elevated classification accuracy. Finally, a lightweight FHB severity discrimination model based on MobileNetV2 was developed and deployed as an easy-to-use analysis system. Analysis revealed that UVE-selected characteristic bands for FHB severity predominantly fell within 590–680 nm (chlorophyll degradation related), 930–1043 nm (water stress related) and 738 nm (cell wall polysaccharide decomposition related). This distribution aligns with the synergistic effect of rapid chlorophyll degradation and structural damage accompanying disease progression. The resulting MobileNetV2 model achieved a mean average precision (mAP) of 99.93% on the training set and 98.26% on the independent test set. Crucially, it maintains an 8.50 MB parameter size, it processes data 2.36 times faster, significantly enhancing its suitability for field-deployed equipment by optimally balancing accuracy and operational efficiency. This advancement empowers agricultural workers to implement timely control measures, dramatically improving precision alongside optimized field deployment. Full article

Satellite monitoring of agricultural crops plays a crucial role in ensuring food security and in the sustainable management of agricultural resources, particularly in regions dominated by rainfed farming, such as the Turkestan region of Kazakhstan. Many satellite monitoring tasks rely on remote identification of different types of cultivated crops. In developing the proposed method, we accounted for the temporal characteristics of crop growth and development in various climatic zones of rainfed agriculture, analyzed the dynamics of the Normalized Difference Vegetation Index (NDVI) together with ground-based data, and identified effective time periods and patterns for successful crop recognition. This study aims to develop and comparatively assess two methods for the automatic identification of cultivated crops in rainfed zones using Sentinel-2 satellite data for the years 2018 and 2022. The first method is based on detailed classification of pre-digitized field boundaries, providing high accuracy in satellite-based mapping. The second method represents a fully automated approach applied to large rainfed areas, emphasizing operational efficiency and scalability. The results obtained from both methods were validated against official national statistics, ground-based field surveys, and farm-level data. The findings indicate that the field-boundary-based method delivers significantly higher accuracy (average accuracy of 91.1%). While the automated rainfed-zone approach demonstrates lower accuracy (78%), it still produces acceptable results for large-scale monitoring, confirming its suitability for rapid assessment of sown areas. This research highlights the trade-off between the accuracy achieved through detailed field boundary digitization and the efficiency provided by an automated, scalable approach, offering valuable tools for agricultural production management. Full article

The increasing use of reclaimed wastewater in agriculture raises growing concerns about the accumulation of priority organic micropollutants in edible crops. In this study, we developed and validated a novel QuEChERS–SPME–GC/MS method for the simultaneous determination of 15 polycyclic aromatic hydrocarbons (PAHs), 3 nitro-PAHs, and 14 polychlorinated biphenyls congeners in Eruca vesicaria (rocket) leaves. The method was optimized to address the matrix complexity of leafy vegetables and included a two-step dispersive solid-phase extraction (d-SPE) cleanup and aqueous dilution prior to SPME. Validation showed excellent performance, with MDLs between 0.1 and 6.7 µg/kg, recoveries generally between 70 and 120%, and precision (RSD%) below 20%. The greenness of the protocol was assessed using the AGREE metric, yielding a score of 0.60. Application to rocket samples irrigated with treated wastewater revealed no significant accumulation of target pollutants compared to commercial samples. All PCB and N-PAH congeners were below detection limits, and PAH concentrations were low and mostly limited to lighter compounds. Human health risk assessment based on toxic equivalent concentrations confirmed that estimated cancer risk (CR) values 10⁻⁹–10⁻⁸ were well below accepted safety thresholds. These findings support the safe use of reclaimed water for leafy crop irrigation under proper treatment conditions and highlight the suitability of the method for trace-level food safety monitoring. Full article

The anaerobic co-digestion of agricultural residues emerges as a promising strategy for energy recovery and nutrient recycling within circular agricultural systems. This study aimed to optimize co-digestion parameters for vinegar residue (VR) and cattle manure (CM) using an orthogonal experimental design. Three key variables were investigated which are the co-substrate ratio (VR to CM), feedstock-to-inoculum (F/I) ratio, and total solids (TS) content. Nine experimental combinations were tested to evaluate methane yield, feedstock degradation, and digestate characteristics. Results showed that the optimal condition for methane yield comprised a 2:3 co-substrate ratio, 1:2 F/I ratio, and 20% TS, achieving the highest methane yield of 267.84 mL/g volatile solids (VS) and a vs. degradation rate of 58.65%. Digestate analysis indicated this condition generated the most nutrient-rich liquid digestate and solid digestate, featuring elevated N, P, and K concentrations, acceptable seed germination indices (GI), and moderate humification levels. While total nutrient content did not meet commercial organic fertilizer standards, the digestate is suitable for direct land application in rural settings. This study underscores the need to balance energy recovery and fertilizer quality in anaerobic co-digestion systems, providing practical guidance for decentralized biogas plants seeking to integrate waste treatment with agricultural productivity. Full article

Dairy wastewater is highly polluting and requires treatment before being discharged into receiving surface waters or destined for reuse. This study aimed to evaluate the performance of a wastewater treatment plant (WWTP) at a dairy facility, which includes the following treatment stages: screening, grease trap, and an upflow anaerobic filter (UAF). Monitoring data from a WWTP at a dairy situated in the southern region of Minas Gerais, Brazil, were assessed based on pollutant removal efficiency in accordance with Brazilian environmental regulations. The results showed that the WWTP achieved average removal efficiencies of 96.2% for COD and 97.1% for BOD₅. The BOD₅/COD ratio of raw and treated wastewater averaged 0.46 and 0.30, respectively, indicating preferential removal of the biodegradable organic fraction. The treated wastewater complied with legal standards for pH, settleable solids, and total suspended solids. However, at least one sample did not meet regulatory limits for discharge into water bodies regarding surfactants and oils & greases. Strong linear correlations (R²~0.8) between COD and BOD₅ data were observed for both raw and treated wastewater. While the treated wastewater was not suitable for use in the facility’s wood-fired boiler, it may be reused for agricultural irrigation. Full article

With the rapid development of agricultural intelligence, nondestructive testing (NDT) has shown considerable promise for agricultural product inspection. Compared with traditional methods—which often suffer from subjectivity, low efficiency, and sample damage—NDT offers rapid, accurate, and non-invasive solutions that enable precise inspection without harming the products. These inherent advantages have promoted the increasing adoption of NDT technologies in agriculture. Meanwhile, rising quality standards for agricultural products have intensified the demand for more efficient and reliable detection methods, accelerating the replacement of conventional techniques by advanced NDT approaches. Nevertheless, selecting the most appropriate NDT method for a given agricultural inspection task remains challenging, due to the wide diversity in product structures, compositions, and inspection requirements. To address this challenge, this paper presents a review of recent advancements and applications of several widely adopted NDT techniques, including computer vision, near-infrared spectroscopy, hyperspectral imaging, computed tomography, and electronic noses, focusing specifically on their application in agricultural product evaluation. Furthermore, the strengths and limitations of each technology are discussed comprehensively, quantitative performance indicators and adoption trends are summarized, and practical recommendations are provided for selecting suitable NDT techniques according to various agricultural inspection tasks. By highlighting both technical progress and persisting challenges, this review provides actionable theoretical and technical guidance, aiming to support researchers and practitioners in advancing the effective and sustainable application of cutting-edge NDT methods in agriculture. Full article