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Agricultural Machinery and Agricultural Engineering: Current Achievements and Future Directions

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Agricultural Technology".

Deadline for manuscript submissions: closed (15 July 2023) | Viewed by 35306

Special Issue Editors


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Guest Editor
CREA Research Centre for Engineering and Agro-Food Processing, Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria, Via Milano 43, 24047 Treviglio, Italy
Interests: mechanization; livestock automation; precision farming
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Guest Editor
Department of Land, Environment, Agriculture and Forestry, University of Padova, 35020 Legnaro, Padova, Italy
Interests: precision agriculture; agricultural mechanization; sensors; automation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Since the beginning of modern agriculture in the early 20th century, mechanization and engineering have contributed to increasing primary sector productivity. In the last several decades, agricultural engineering has significantly enhanced the global sustainability of crop production, harvesting and processing, satisfying the demands of consumers and a wide range of industries.

Currently, and in the near future, agriculture is called on to secure affordable, safe and healthy food, feed and fibers for a growing population in a unprecedentedly unstable world, without compromising the natural resources and assuring a safe and fair workplace for millions of people.

Agricultural mechanization and engineering are crucial in developing engineering-based technology to meet these urgent demands. In the last few years, we assisted the increasing application of leading-edge technologies to agriculture, targeting the development of solutions that significantly enhance productivity and competitiveness.

This SI welcomes contributions from scholars reporting current achievements and frontier experimentations identifying the future direction of applications of such cutting-edge engineering technologies in agriculture. This Special Issue will publish papers dealing with agriculture mechanization, automation and robotics, remote sensing, agricultural resources management, waste management and recycling, greenhouse gas emission, carbon sequestration, livestock production and management, bio-energies, energy efficiency in agriculture, circular economy, ICT, artificial intelligence, decision-support systems and technologies and solutions for precision, digital and smart agriculture, soil management and conservation, harvest and postharvest technology, water conservation storage and utilization, as well as farm buildings and facilities. We particularly encourage the submission of papers giving experimental evidence of the integration of disciplines such as engineering, mathematics/statistics/physics, automation, human–machine interface and ergonomics, and environmental and computer science. Contributions focusing on engineering technologies to achieve the United Nations Sustainable Development Goals, and to overcome and recover from the COVID-19 pandemic are welcome.

Dr. Eugenio Cavallo
Dr. Carlo Bisaglia
Dr. Francesco Marinello
Guest Editors

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agriculture is an international peer-reviewed open access monthly journal published by MDPI.

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Keywords

  • agriculture mechanization
  • automation and robotics
  • remote sensing
  • agricultural resources management
  • waste management and recycling
  • greenhouse gas emission
  • carbon sequestration
  • livestock production and management
  • bio-energies
  • energy efficiency in agriculture
  • circular economy
  • ICT
  • artificial intelligence
  • decision-support systems and technologies and solutions for precision
  • digital and smart agriculture
  • soil management and conservation
  • harvest and postharvest technology
  • water conservation storage and utilization
  • farm buildings and facilities

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Published Papers (14 papers)

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Research

18 pages, 3674 KiB  
Article
Research on Load Spectrum Reconstruction Method of Exhaust System Mounting Bracket of a Hybrid Tractor Based on MOPSO-Wavelet Decomposition Technique
by Liming Sun, Mengnan Liu, Zhipeng Wang, Chuqiao Wang and Fuqiang Luo
Agriculture 2023, 13(10), 1919; https://doi.org/10.3390/agriculture13101919 - 30 Sep 2023
Cited by 3 | Viewed by 890
Abstract
To overcome the limitations of the hybrid tractor bumping tests, which include extended cycle times, high costs, and impracticality for single-part reliability verification, this study focuses on the exhaust system mounting bracket of a hybrid tractor. A novel approach that combines multi-objective particle [...] Read more.
To overcome the limitations of the hybrid tractor bumping tests, which include extended cycle times, high costs, and impracticality for single-part reliability verification, this study focuses on the exhaust system mounting bracket of a hybrid tractor. A novel approach that combines multi-objective particle swarm optimization (MOPSO) and wavelet decomposition algorithms was employed to enhance the reconstruction of shock vibration signals. This approach aims to enable the efficient acquisition of input signals for subsequent shaker table testing. The methodology involves a systematic evaluation of the spectral correlation between the original signal and the reconstructed signal at the stent’s response position, along with signal compression time. These parameters collectively constitute the objective function. The multi-objective particle swarm optimization algorithm is then deployed to explore a range of crucial parameters, including wavelet basic functions, the number of wavelet decomposition layers, and the selection of wavelet components. This exhaustive exploration identifies an optimized signal reconstruction method that accurately represents shock vibration loads. Upon rigorous screening based on our defined objectives, the optimal solution vector was determined, which includes the utilization of the dB10 wavelet basic function, employing a 12-layer wavelet decomposition, and selecting wavelet components a12 and d3~d11. This specific configuration enables the retention of 95% of the damage coefficients while significantly compressing the test time to just 46% of the original signal duration. The implications of our findings are substantial as the reconstructed signal obtained through our optimized approach can be readily applied to shaker excitation. This innovation results in a notable reduction in test cycle time and associated costs, making it particularly valuable for engineering applications, especially in tractor design and testing. Full article
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<p>Bump laying map.</p>
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<p>Bump test scene arrangement of measuring points on the bracket: (<b>a</b>) bump test scene; and (<b>b</b>) measuring points on the bracket.</p>
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<p>Fourier analysis trigonometric functions.</p>
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<p>Wavelet analysis wavelets.</p>
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<p>Mallat wavelet decomposition algorithm.</p>
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<p>Wavelet reconstruction algorithm.</p>
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<p>Dominance relationship of solutions.</p>
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<p>Flowchart of multi-objective particle swarm optimization algorithm.</p>
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<p>Modal analysis of exhaust system and its installation bracket: (<b>a</b>) finite element model; and (<b>b</b>) main mode shapes.</p>
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<p>Time domain waveform of signal of point 1.</p>
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<p>Power spectral density curves of Z-direction vibration of three measuring points.</p>
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<p>Shock response spectrum for Point 1 and Point 2.</p>
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<p>Objective functions for all non-dominated solutions.</p>
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<p>Comparison of the shock response spectra of the original and reconstructed signals at 2 measurement points.</p>
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21 pages, 8335 KiB  
Article
A Co-Simulation Virtual Reality Machinery Simulator for Advanced Precision Agriculture Applications
by Maurizio Cutini, Carlo Bisaglia, Massimo Brambilla, Andrea Bragaglio, Federico Pallottino, Alberto Assirelli, Elio Romano, Alessandro Montaghi, Elisabetta Leo, Marco Pezzola, Claudio Maroni and Paolo Menesatti
Agriculture 2023, 13(8), 1603; https://doi.org/10.3390/agriculture13081603 - 13 Aug 2023
Cited by 6 | Viewed by 2719
Abstract
Simulation systems have become essential tools for both researchers and virtual laboratory experiments. In the Agri-food-chain, SimAgri, a driving simulator for tractors and operating machines, has been developed for precision agriculture (PA) research and to train professional farm drivers. Using the virtual environment [...] Read more.
Simulation systems have become essential tools for both researchers and virtual laboratory experiments. In the Agri-food-chain, SimAgri, a driving simulator for tractors and operating machines, has been developed for precision agriculture (PA) research and to train professional farm drivers. Using the virtual environment of the simulator, the influence and fine-tuning of PA operations logic may be evaluated by simulating existing systems, or designing new ones, in specially compared scenarios and setups. Current configurations include an agricultural tractor carrying or towing farm equipment such as sprayers, seeders and fertilizer, embedded sensors, human–machine interfaces that may be configured like a joystick, console and touchscreen, and four virtual environment monitors. The study describes the design choices that have made it possible to create a simulator aimed at precision agriculture, keeping auto guidance, geolocation, and operations with ISOBUS implements as pillars. This research aims to use a unique purpose-designed simulation platform, installed on a driver-in-the-loop simulator to provide data to objectify the benefits of PA criteria. Numerical and experimental data have been compared to ensure results reliability. Full article
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<p>Some of the CREA-IT facilities adopted for fitting the tractor model: measurement of the center of gravity; the engine room and the 1000 m test track (the tractors are only as an example).</p>
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<p>An example of prescription maps.</p>
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<p>The platform allows to generate realistic environments, each element can be visible by virtual sensors.</p>
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<p>Experimental layout of the sowing experimental test and of the adopted field.</p>
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<p>Experimental layout of the spraying experimental test and the adopted field.</p>
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<p>Experimental layout of the fertilizing experimental test and the adopted field.</p>
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<p>The global layout of the developed simulator.</p>
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<p>Detail of the armrest, of the console, of the virtual terminals and of the set of pedals.</p>
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<p>An operator during an experience with the virtual fertilizer.</p>
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<p>The aerial view of the adopted OBJ of the tractor and of the implements during working.</p>
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<p>An example of selection of the available tractor and seeder.</p>
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<p>Selection of the scenario (<b>left</b>) and the developed digital twin of the farm environment (<b>right</b>).</p>
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<p>Examples of screenshots taken from the AgriSI application. The red line shows the trajectory to be followed during the operation and the cyan line highlights the field boundaries. The image on the left is monitoring of the tractor behavior, showing some parameters regarding powertrain, wheel torques, and vertical forces. On the right is the considered prescription map with some information on the implement behavior: flow rate of the distributors, geometrical parameters, and type and remaining quantity of product.</p>
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<p>Traction on field maneuver numerical vs. experimental results: (<b>a</b>) velocity profile, (<b>b</b>) longitudinal acceleration profile, (<b>c</b>) front axle longitudinal loads, (<b>d</b>) rear axle longitudinal loads.</p>
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<p>Traction on field maneuver numerical vs. experimental results: (<b>a</b>) velocity profile, (<b>b</b>) longitudinal acceleration profile, (<b>c</b>) front axle longitudinal loads, (<b>d</b>) rear axle longitudinal loads.</p>
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<p>Steering pad maneuver numerical vs. experimental results: (<b>a</b>) velocity profile, (<b>b</b>) imposed trajectory, (<b>c</b>) lateral acceleration profile, (<b>d</b>) rear vertical loads.</p>
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<p>Results of the sowing reported as georeferenced distributed dose between the experimental test carried out in the field with a tractor and seeder (<b>left</b>) and the result with the simulator experience (<b>right</b>).</p>
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<p>Results of the fertilizing reported as georeferenced distributed dose between the experimental test carried out in the field with a tractor and fertilizer (<b>left</b>) and the result with the simulator experience (<b>right</b>).</p>
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<p>Results of the spraying reported as georeferenced distributed dose between the experimental test carried out in the field with a tractor and sprayer (<b>left</b>) and the result with the simulator experience (<b>right</b>).</p>
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16 pages, 5824 KiB  
Article
The Selection of an Energy-Saving Engine Mode Based on the Power Delivery and Fuel Consumption of a 95 kW Tractor during Rotary Tillage
by Md. Abu Ayub Siddique, Seung-Yun Baek, Seung-Min Baek, Hyeon-Ho Jeon, Jun-Ho Lee, Mo-A Son, Su-Young Yoon, Yong-Joo Kim and Ryu-Gap Lim
Agriculture 2023, 13(7), 1376; https://doi.org/10.3390/agriculture13071376 - 11 Jul 2023
Viewed by 1604
Abstract
The objective of this study was to estimate power delivery efficiency and fuel consumption based on engine modes. In this study, a 95 kW power-shift tractor was used to analyze power delivery and estimate fuel consumption during rotary tillage. Rotary tillage was conducted [...] Read more.
The objective of this study was to estimate power delivery efficiency and fuel consumption based on engine modes. In this study, a 95 kW power-shift tractor was used to analyze power delivery and estimate fuel consumption during rotary tillage. Rotary tillage was conducted in a field experiment with the conventional, APS (auto power shift) power, and APS ECO engine modes. To analyze the field conditions, the soil hardness and soil water content were measured, and soil samples were collected from the experimental site to analyze the soil texture by using the USDA soil texture triangle. Finally, an efficient and suitable engine mode was selected for rotary tillage based on the working load. It was observed that the power delivery and tractive efficiencies when using the APS power mode were the highest among other engine modes, accounting for around 89.23 and 73.45%, respectively. However, the fuel consumption when using the APS power mode was approximately 23.02 L/h, which was also comparatively higher than that of the other engine modes. Additionally, the tractive efficiencies of each engine mode were compared using the Brixius prediction model. The statistical analysis of the predicted tractive efficiencies and those in the tests showed that there were no significant differences among the engine modes; this indicates that the APS controller could perform with high accuracy. In the conventional mode, the power delivery, tractive efficiency, and fuel consumption were approximately 66.48%, 55.89%, and 17.04 L/h, respectively, which were comparatively low. However, the slip ratio in the conventional mode was 18.80%, which was higher than that in the APS power and APS ECO modes. On the other hand, PDE, TE, and fuel consumption when using APS ECO were around 77.57%, 58.44%, and 19.39 L/h, respectively, which were higher than those of the conventional mode, but lower than those of the APS power mode. Furthermore, the comparative analysis showed that the working loads in the APS ECO mode were located in the ungoverned region and were very close to the engine’s maximum torque, which could allow sudden and unwanted engine turn-off due to the fluctuations in working loads, which is to be avoided. The fuel consumption was also comparatively low. However, the working loads in the conventional and APS power modes were located in the governed region, which was outside the engine’s operating range. Therefore, we recommend that users operate tractors in the APS ECO engine mode for rotary tillage, considering fuel economics and high working loads. Full article
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<p>A map of the engine characteristics of a 95 kW tractor.</p>
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<p>The tractor used for the field experiment.</p>
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<p>The control algorithm for engine modes during rotary tillage.</p>
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<p>The engine power required for rotary tillage in the different engine modes: (a) preparation, (b) operation, and (c) PTO lifting and turning of the tractor.</p>
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<p>The axle power for rotary tillage in the different engine modes: (a) preparation, (b) operation, and (c) PTO lifting and turning of the tractor.</p>
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<p>The PTO power for rotary tillage in the different engine modes: (a) preparation, (b) operation, and (c) PTO lifting and turning of the tractor.</p>
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<p>A comparison of the axle and PTO power and total engine power required in the different engine modes.</p>
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<p>The power delivery efficiency for each engine mode during rotary tillage.</p>
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<p>The fuel consumption in each engine mode during rotary tillage: (a) preparation, (b) operation, and (c) PTO lifting and turning of the tractor.</p>
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<p>The comparison of the tractive efficiency in the different engine modes during rotary tillage.</p>
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<p>A comparative analysis of the working load in each engine mode during rotary tillage.</p>
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13 pages, 2496 KiB  
Article
Continuous and Impact Cutting in Mechanized Sugarcane Harvest: Quality, Losses and Impurities
by João Vitor Paulo Testa, Murilo Battistuzzi Martins, Aldir Carpes Marques Filho, Kléber Pereira Lanças, Renato Lustosa Sobrinho, Taciane Finatto, Mohammad K. Okla and Hamada AbdElgawad
Agriculture 2023, 13(7), 1329; https://doi.org/10.3390/agriculture13071329 - 29 Jun 2023
Cited by 2 | Viewed by 2351
Abstract
Sugarcane harvesting requires improvements, particularly in cutting tools. Continuous cutting saws have been introduced as a solution to this issue. This study evaluates the performance of two basal sugarcane cutting systems in different fields: a traditional impact cut system (ICS) with knives and [...] Read more.
Sugarcane harvesting requires improvements, particularly in cutting tools. Continuous cutting saws have been introduced as a solution to this issue. This study evaluates the performance of two basal sugarcane cutting systems in different fields: a traditional impact cut system (ICS) with knives and a continuous cut system (CCS) with saw blades. Tests were conducted during two crop cycles in three areas, using a 3 × 2 factorial design with two cutting devices and four replications per treatment. Cut quality indices and ratoon damage were analyzed using descriptive statistics. Raw material losses were subjected to the Shapiro–Wilk normality test, ANOVA, and Tukey’s test at 5% probability. Significant differences in cutting quality were found across different areas. The total crop productivity influenced sugarcane cut quality, with the CCS showing (0.8 Mg ha−1) visible losses in higher productivity areas, which is a 74% increase compared to the ICS. In lower productivity areas, the CCS demonstrated better loss performance (0.8 Mg ha−1). Additionally, the stumps damage rate for the CCS was lower than that for the ICS (0.15 and 0.28, respectively), indicating that saws can preserve cane fields and enhance longevity. Full article
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<p>Sugarcane cutting systems. (<b>a</b>) Continuous cutting system (CCS), composed of steel saws; (<b>b</b>) Conventional impact cutting system (ICS), consisting of carbon steel cutting knives.</p>
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<p>Total losses averages in the field areas for each cutting device. ANOVA: F test (area): 30.75 *; F test (cutoff): 7.73 *; F test (interaction): 3.44 ns; CV (%): 30.7. Means followed by the same letters (lowercase blue box—loss/area) and average uppercase tool do not differ by Tukey’s test (α = 5%), CV: variation coefficient, * significant at 5% probability and ns not significant at 5% probability.</p>
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<p>Visible material losses to total productivity; limit zones of material loss according to Benedini, Brod and Perticarrari (2013) [<a href="#B24-agriculture-13-01329" class="html-bibr">24</a>].</p>
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<p>Stalks and ratoons damage index.</p>
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<p>Plant foreign matter (%) in the harvested material. ANOVA: means by the same letters do not differ by Tukey’s test (α = 5%), CV—Deviation Coefic. 10.2%, * significant at 5% probability and NS not significant at 5% probability. F test (area) 31.5 *; F test (s.cut) 0.31 NS; F test (interaction) 0.37 NS.</p>
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<p>Averages of mineral foreign matter present in the raw material. ANOVA: Means by equal lowercase letters do not differ by Tukey’s test (α = 5%), CV—Deviation Coefic. 15.1%, * significant at 5% probability and NS not significant at 5% probability. F test (area) 30.23 *; F test (s.cut) 0.97 NS; F test (interaction) 0.17 NS.</p>
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22 pages, 4132 KiB  
Article
Carbon Footprint of an Orchard Tractor through a Life-Cycle Assessment Approach
by Salvatore Martelli, Francesco Mocera and Aurelio Somà
Agriculture 2023, 13(6), 1210; https://doi.org/10.3390/agriculture13061210 - 7 Jun 2023
Cited by 9 | Viewed by 3936
Abstract
The effects of climate change are reaching a point of no return. The necessity to reduce greenhouse gasses (GHGs) is currently notorious on several levels: academic, industrial, and political. The Paris Climate Agreement set a clear roadmap to limit pollutant emissions and reach [...] Read more.
The effects of climate change are reaching a point of no return. The necessity to reduce greenhouse gasses (GHGs) is currently notorious on several levels: academic, industrial, and political. The Paris Climate Agreement set a clear roadmap to limit pollutant emissions and reach carbon neutrality. Consequently, everything related to product life cycles, considering the entire supply chain, needs to be analyzed and reconsidered. The agricultural sector is no exception: indeed, it is responsible for 11% of global anthropogenic GHG emissions. Agri-construction sector accounts for 20–30% of all GHG emissions referred to the agricultural field. This study aimed to evaluate the GHG emissions of an orchard-specialized tractor operating in Europe considering a service life of ten years. The assessment was conducted through the life-cycle assessment (LCA) standardized methodology, combining secondary data, primary data, and a software database (Open LCA (v 1.10.3) software, Environmental Footprint (v 4) database). First, the functional unit, and the boundaries of the analysis are defined. Then, the tractor life cycle is analyzed considering its three main stages: manufacture, use, and disposal. Lastly, the results are discussed according to gate-to-gate and cradle-to-gate approaches. What emerged from the assessment was the production of 5.75 kg CO2eq. · kgvehicle−1 · year−1 for a single orchard specialized tractor and the predominance of use phase emissions (around 90% of the total). Full article
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<p>Boundaries of the analysis.</p>
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<p>Antonio Carraro TRG 10900.</p>
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<p>PCAN-GPS.</p>
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<p>Example of a mechanical weeder.</p>
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<p>Weeding-operation fuel rate; the dashed red-line represents the average diesel fuel rate recorded during weeding-operation.</p>
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<p>Spraying-operation fuel rate; the dashed red line represents the average diesel fuel rate recorded during spraying-operation; the green dashed lines represent the average diesel fuel rate recorded when the axial fan was turned on, whereas the magenta ones represent the average fuel rate recorded when the fan was turned off.</p>
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<p>Sprayer used during the tractor’s operation.</p>
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<p>Diesel-engine efficiency map and operation working points (WPs).</p>
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<p>Manufacturing-phase GWP distribution for different input flows.</p>
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<p>Use-phase GWP distribution for different input flows.</p>
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<p>EOL-phase GWP distributions for different input flows.</p>
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<p>GWP analysis global results.</p>
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<p>Effects of diesel-fuel rate variance on use phase in terms of GWP.</p>
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13 pages, 2354 KiB  
Article
Using Image Texture Analysis to Evaluate Soil–Compost Mechanical Mixing in Organic Farms
by Elio Romano, Massimo Brambilla, Carlo Bisaglia and Alberto Assirelli
Agriculture 2023, 13(6), 1113; https://doi.org/10.3390/agriculture13061113 - 24 May 2023
Cited by 6 | Viewed by 1768
Abstract
Soil amendments (e.g., compost) require uniform incorporation in the soil profile to benefit plants. However, machines may not mix them uniformly throughout the upper soil layer commonly explored by plant roots. The study focuses on using image texture analysis to determine the level [...] Read more.
Soil amendments (e.g., compost) require uniform incorporation in the soil profile to benefit plants. However, machines may not mix them uniformly throughout the upper soil layer commonly explored by plant roots. The study focuses on using image texture analysis to determine the level of mixing uniformity in the soil following the passage of two kinds of harrows. A 12.3-megapixel DX-format digital camera acquired images of soil/expanded polystyrene (in the laboratory) and soil/compost mixtures (in field conditions). In the laboratory, pictures captured the soil before and during the simulated progressive mixing of expanded polystyrene particles. In field conditions, images captured the exposed superficial horizons of compost-amended soil after the passage of a combined spike-tooth–disc harrow and a disc harrow. Image texture analysis based on the gray-level co-occurrence matrix calculated the sums of dissimilarity, contrast, entropy, and uniformity metrics. In the laboratory conditions, the progressive mixing resulted in increased image dissimilarity (from 1.15 ± 0.74 × 106 to 1.65 ± 0.52 × 106) and contrast values (from 2.69 ± 2.06 × 106 to 5.67 ± × 1.93 106), almost constant entropy (3.50 ± 0.25 × 106), and decreased image uniformity (from 6.65 ± 0.31 × 105 to 4.49 ± 1.36 × 105). Using a tooth-disc harrow in the open field resulted in higher dissimilarity, contrast, entropy (+73.3%, +62.8%, +16.3%), and lower image uniformity (−50.6%) than the disc harrow, suggesting enhanced mixing in the superficial layer. Full article
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<p>Soil mixed with compost in field conditions: (<b>a</b>) example of image acquisition and image cropping for GLCM analysis; (<b>b</b>) examples of cropped images taken after the passage of a combined spike-tooth–disc harrow (<span class="html-italic">Carpinello</span> farm, <b>above</b>) and a disc harrow (<span class="html-italic">Ponticelli</span> farm, <b>below</b>).</p>
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<p>Picture showing the progress of GLCM image processing from the raw image (top of the figure) to the processed images resulting for each considered metric. The dashed squares refer to the compost particles dispersed into the soil profile.</p>
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<p>Examples of the pictures of the soil profiles from the laboratory test. Image acquisitions represent the initial state with the layer of EPS before mixing (<b>1</b>) and after the first three simulated mixings (from <b>2</b> to <b>4</b>).</p>
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<p>Boxplot representation of the sum of image texture metrics without any EPS on the surface (0), with the EPS layer before mixing (1), and after each mixing action (2–4).</p>
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<p>Boxplots representing the sums of the considered indices in each sampling site. The boxplot contains the median and the mean value: a line connects the latter.</p>
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15 pages, 4327 KiB  
Article
System Design, Analysis, and Control of an Intelligent Vehicle for Transportation in Greenhouse
by Changjie Wu, Xiaolong Tang and Xiaoyan Xu
Agriculture 2023, 13(5), 1020; https://doi.org/10.3390/agriculture13051020 - 7 May 2023
Cited by 5 | Viewed by 2220
Abstract
Smart agriculture represents a significant trend in agricultural development, given its potential to enhance operational efficiency and reduce labor intensity. Despite the adoption of modern greenhouse technologies, such as sensors and automation systems, crop transportation is still largely achieved through manual labor, largely [...] Read more.
Smart agriculture represents a significant trend in agricultural development, given its potential to enhance operational efficiency and reduce labor intensity. Despite the adoption of modern greenhouse technologies, such as sensors and automation systems, crop transportation is still largely achieved through manual labor, largely due to the complex environment and narrow terrain of greenhouses. To address this challenge, this work proposes the design of an intelligent vehicle that is capable of transporting crops in a commercial greenhouse, with the aim of improving operational efficiency and reducing labor intensity. To enable the vehicle to navigate the horizontal and rail surfaces within the greenhouse, a novel chassis structure is designed that is capable of simultaneous driving on both ground and rail surfaces. Additionally, the two-dimensional codes is adopted for positioning and navigation, thereby avoiding the need to modify existing greenhouse road surfaces. Through the implementation of a comprehensive system-control strategy, the intelligent vehicle realized various functions, including ground driving, rail driving, moving up and down the rail, and automatic rail changing. Experimental results demonstrate that the designed intelligent vehicle successfully meets the basic requirements for crop transportation in a greenhouse, providing a solid foundation for future unmanned operations. Full article
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<p>Overall diagram of the vehicle.</p>
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<p>Chassis design of the intelligent vehicle.</p>
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<p>Diagram of the two-dimensional code localization.</p>
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<p>Schematic diagram of vehicle positioning and navigation in the greenhouse.</p>
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<p>Diagram of vehicle kinematics.</p>
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<p>The intelligent vehicle working strategy.</p>
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<p>Velocity variation of mecanum wheels.</p>
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<p>Current variation of mecanum wheel motors.</p>
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<p>Current variation of roller motor.</p>
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18 pages, 4524 KiB  
Article
Analysis of Rollover Characteristics of a 12 kW Automatic Onion Transplanter to Reduce Stability Hazards
by Milon Chowdhury, Mohammod Ali, Eliezel Habineza, Md Nasim Reza, Md Shaha Nur Kabir, Seung-Jin Lim, Il-Su Choi and Sun-Ok Chung
Agriculture 2023, 13(3), 652; https://doi.org/10.3390/agriculture13030652 - 10 Mar 2023
Cited by 2 | Viewed by 2053
Abstract
The rollover tendency of upland farm machinery needs to be carefully considered because upland crop fields are typically irregular, and accidents frequently result in injuries and even death to the operators. In this study, the rollover characteristics of an underdeveloped 12 kW automatic [...] Read more.
The rollover tendency of upland farm machinery needs to be carefully considered because upland crop fields are typically irregular, and accidents frequently result in injuries and even death to the operators. In this study, the rollover characteristics of an underdeveloped 12 kW automatic onion transplanter were determined theoretically and evaluated through simulation and validation tests considering the mounting position of the transplanting unit and load conditions. The center of gravity (CG) coordinates for different mass distributions, and static and dynamic rollover angles were calculated theoretically. Simulation and validation tests were conducted to assess the static rollover angle under different mounting positions of the transplanting unit and load conditions of the onion transplanter. The dynamic rollover tendency was evaluated by operating the onion transplanter on different surfaces and at different speeds. According to the physical properties and mass of the onion transplanter, the theoretical rollover angle was 34.5°, and the coordinates of the CG gradually moved back to the rear wheel axle after attaching the transplanting part and under upward riding conditions. The average simulated rollover angle was 43.9°. A turning difference of 4.5° was observed between the right and left sides, where a 3° angle difference occurred due to the load variation. During the dynamic stability test, angle variations of 2~4° and 3~6° were recorded for both high and low driving speeds in the vehicle platform and transplanting unit, respectively. The overturning angles also satisfied the ISO standard. This study provides helpful information for ensuring the safety of upland crop machinery operating under rough and sloped field conditions. Full article
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<p>A 3-D model of the considered automatic onion transplanter: (<b>a</b>) front view, (<b>b</b>) side view, (<b>c</b>) onion seedlings used for transplantation, and (<b>d</b>) a dimensional representation of the transplanting pattern.</p>
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<p>Schematic diagram of the transference of CG of the onion transplanter: (<b>a</b>) side view, and (<b>b</b>) top view.</p>
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<p>Schematic view of the lateral rollover of the onion transplanter: (<b>a</b>) normal condition, (<b>b</b>) stable condition, (<b>c</b>) critical (trending to rollover) condition.</p>
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<p>Simulation of the static rollover of the onion transplanter: (<b>a</b>) normal condition of the onion transplanter on the tilt bench, (<b>b</b>) simulated lateral stability test, and (<b>c</b>) simulated longitudinal stability test.</p>
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<p>Validation of the static stability: (<b>a</b>) normal condition of the onion transplanter on the tilt bench, (<b>b</b>) validation of lateral stability, and (<b>c</b>) validation of longitudinal stability.</p>
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<p>Schematic view of the dynamic rollover of the onion transplanter during operation on an uphill path: (<b>a</b>) continuous operation in an upward direction, where positions (i) to (iii) indicate the displacement of the CG, and (<b>b</b>) facing an obstacle in the driving path.</p>
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<p>Dynamic stability evaluation of the onion transplanter on soil surface.</p>
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<p>Simulation and validation of the lateral and longitudinal rollover angles of the onion transplanter system under different load conditions and the mounted status of the transplanting unit. Here, a, b, c, d: different letters indicate significant differences (<span class="html-italic">p</span> ≤ 0.05).</p>
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<p>Assessment of dynamic stability of the onion transplanter under: (<b>a</b>) soil surface, (<b>b</b>) unpaved road, and (<b>c</b>) asphalt road conditions considering high and low driving speeds.</p>
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12 pages, 2454 KiB  
Article
Effects of Unmanned Aerial Spray System Flight Altitude and Collector Height on Spray Deposition Measured Using a Food Dye Tracer
by Chun-Gu Lee, Seung-Hwa Yu and Joong-Yong Rhee
Agriculture 2023, 13(1), 96; https://doi.org/10.3390/agriculture13010096 - 29 Dec 2022
Cited by 2 | Viewed by 2210
Abstract
The use of unmanned aerial spray systems (UASS) has increased owing to their many advantages. However, studies related to a standardized method to evaluate the spray performance of UASS are lacking. Therefore, in the present study, a quantitative analytical method using a food [...] Read more.
The use of unmanned aerial spray systems (UASS) has increased owing to their many advantages. However, studies related to a standardized method to evaluate the spray performance of UASS are lacking. Therefore, in the present study, a quantitative analytical method using a food dye tracer was compared with the image analysis method, and the effects of experimental conditions on spray deposition were assessed. Concordance between the results of quantitative and image analyses was examined. The coverage of water-sensitive paper (WSP) and Medley Velvet (MV) was compared using image analysis. Moreover, the effects of flight altitude and collector height on spray deposition amount and effective spray width were evaluated. The results showed a significant correlation between the deposition and the coverage of MV (R2 = 0.6782, p-level < 0.001). The coverage of MV is different from that of WSP. In addition, the correlation coefficient between the coverage of WSP and that of MV was smaller than the correlation coefficient between depositions and the coverage of MV. Therefore, MV should be used instead of WSP for more accurate analysis. The lower the collector height, the smaller the deposition amount. The effective spray width increased as the distance between the collector and UASS increased, whereas the total deposition amount decreased when the collector was close to the ground. Overall, using a food dye tracer, both quantitative and qualitative analyses can be applied simultaneously, and this method may replace analysis using WSP. Full article
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<p>UASS (SG-10P).</p>
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<p>Spray deposition collector (<b>a</b>) Medley velvet; (<b>b</b>) WSP.</p>
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<p>View of test site.</p>
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<p>Image capture system.</p>
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<p>Layout of spray deposition test.</p>
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<p>Correlation analysis between amounts of tracer from extration and coverage from image analysis.</p>
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<p>Correlation analysis between MV and WSP coverage.</p>
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<p>Spray deposition of UASS by (<b>a</b>) collector height, (<b>b</b>) UASS altitude.</p>
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15 pages, 4447 KiB  
Article
Development, Validation, and Evaluation of Partial PST Tractor Simulation Model for Different Engine Modes during Field Operations
by Md. Abu Ayub Siddique, Seung-Min Baek, Seung-Yun Baek, Yong-Joo Kim and Ryu-Gap Lim
Agriculture 2023, 13(1), 44; https://doi.org/10.3390/agriculture13010044 - 23 Dec 2022
Cited by 5 | Viewed by 1992
Abstract
The objectives of this study are the development and verification of a simulation model of the partial PST (power-shift transmission) tractor based on actual field operations. The PST simulation model was verified for the asphalt driving condition, and performance was evaluated for asphalt [...] Read more.
The objectives of this study are the development and verification of a simulation model of the partial PST (power-shift transmission) tractor based on actual field operations. The PST simulation model was verified for the asphalt driving condition, and performance was evaluated for asphalt driving, plow, and rotary tillage. In this study, the traditional, APS (Auto Power Shift) ECO, and APS power engine modes were used to analyze fuel consumption. The statistical analysis proved that the experimental and simulation results were in a linear relationship, with an accuracy of over 98%. Finally, the results suggested that users could utilize the 95-kW partial PST tractor in the APS ECO engine mode with higher fuel economy compared to the traditional and APS power modes. Full article
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<p>The block diagram of a 95-kW partial PST tractor transmission.</p>
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<p>The simulation model of a 95-kW partial PST tractor.</p>
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<p>The hydraulic component design for the partial PST tractor. (<b>a</b>) Relief valve, (<b>b</b>) accumulator.</p>
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<p>The 95-kW engine characteristics map used in this study.</p>
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<p>Proportional valves assembly with command current signal. (<b>a</b>) Proportional valve block of T130, (<b>b</b>) command signal of proportional valve.</p>
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<p>Axle loads of the target partial PST tractor during operations. (<b>a</b>) Asphalt driving, (<b>b</b>) plow tillage, (<b>c</b>) rotary tillage.</p>
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<p>The experiment using a 95-kW partial PST tractor.</p>
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<p>Regression analysis of fuel consumption during asphalt operation.</p>
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<p>Regression analysis of fuel consumption during plow tillage.</p>
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<p>Regression analysis of fuel consumption during rotary tillage.</p>
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<p>Performance evaluation of a partial PST tractor during asphalt, plow, and rotary tillage.</p>
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24 pages, 10416 KiB  
Article
Process Analysis and Parameters Optimization of Black Soldier Fly Sand Mixture with Two-Stage Sieve Surface Vibration Separating Machine
by Shisheng Song, Yushi Wang, Ting Zhou, Songlin Sun, Yulong Yin and Caiwang Peng
Agriculture 2022, 12(12), 2099; https://doi.org/10.3390/agriculture12122099 - 8 Dec 2022
Viewed by 2756
Abstract
The application of the conventional vibrating screen to the separation of the black soldier fly (BSF) sand mixture has several problems (e.g., high rate of impurity and low efficiency). A two-stage sieve surface vibratory sorting device with combined planar and curved surfaces was [...] Read more.
The application of the conventional vibrating screen to the separation of the black soldier fly (BSF) sand mixture has several problems (e.g., high rate of impurity and low efficiency). A two-stage sieve surface vibratory sorting device with combined planar and curved surfaces was investigated, and its critical operating parameters were determined. Moreover, a coupling simulation model of the sieve surface and the larvae–sand mixture was built based on the characteristics of the BSF breeding process, and its critical operating parameters were optimized. Next, the Plackett–Burman test was set to determine the significant factors for the separation of two-stage sieve surface vibrations as amplitude and curved height. The process of crushing separation of frass aggregates and the process of collision transport of BSF larvae were studied through simulation, and the actual test stand was built for parameter verification tests. The preferred parameter combinations comprised 0.012 m amplitude and 0.007 m curved surface height at the impurity rate of 2.34% and the insect injury rate of 5.65%, as well as 0.013 m amplitude and 0.005 m curved surface height at the impurity rate of 3.15% and the insect injury rate of 4.3%, respectively, thus conforming to the requirement of separating BSF larvae–sand mixture to reduce the impurity and prevent larvae injury. The results of this study can lay a basis for the structural improvement and operational parameter adjustment of the BSF larvae–sand mixture separation device. Full article
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<p>Collection and sorting process of black soldier fly sand mixture.</p>
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<p>Schematic diagram of two-stage sieve surfaces separation.</p>
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<p>Triaxial size distribution of BSF frass aggregates.</p>
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<p>Scatter plot of the relationship between the triaxial dimensions of the frass aggregates.</p>
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<p>Contact model between frass particles.</p>
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<p>Simulation model of black soldier fly larvae. (<b>a</b>) Straight BSFL. (<b>b</b>) Curled BSFL.</p>
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<p>Discrete element model of vibrating screen.</p>
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<p>Throwing trajectory of BSF sand mixture on curved screen.</p>
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<p>Platform for sorting test of black soldier fly sand mixture. 1. Computer; 2. Vibrating Screen; 3. High-speed camera; 4. Recycling bin at the end of the screen; 5. Collection box of screen underflow.</p>
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<p>Velocity graph of frass agglomerates at an amplitude of 0.01 m. (<b>a</b>) t = 1.8 s. (<b>b</b>) t = 2.2 s. (<b>c</b>) t = 2.6 s.</p>
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<p>Velocity graph of frass agglomerates at an amplitude of 0.011 m. (<b>a</b>) t = 1.8 s. (<b>b</b>) t = 2.2 s. (<b>c</b>) t = 2.6 s.</p>
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<p>Velocity graph of frass agglomerates at an amplitude of 0.012 m. (<b>a</b>) t = 1.8 s. (<b>b</b>) t = 2.2 s. (<b>c</b>) t = 2.6 s.</p>
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<p>Velocity graph of frass agglomerates at an amplitude of 0.013 m. (<b>a</b>) t = 1.8 s. (<b>b</b>) t = 2.2 s. (<b>c</b>) t = 2.6 s.</p>
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<p>Velocity graph of frass agglomerates at an amplitude of 0.014 m. (<b>a</b>) t = 1.8 s. (<b>b</b>) t = 2.2 s. (<b>c</b>) t = 2.6 s.</p>
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<p>Ultimate pressure on black soldier fly larvae at different amplitudes and curved heights.</p>
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<p>Force analysis in the separation movement of black soldier fly. (<b>a</b>) <span class="html-italic">X</span><sub>1</sub> = 0.011 m, <span class="html-italic">X</span><sub>3</sub> = 0.005 m. (<b>b</b>) <span class="html-italic">X</span><sub>1</sub> = 0.011 m, <span class="html-italic">X</span><sub>3</sub> = 0.006 m. (<b>c</b>) <span class="html-italic">X</span><sub>1</sub> = 0.011 m, <span class="html-italic">X</span><sub>3</sub> = 0.007 m. (<b>d</b>) <span class="html-italic">X</span><sub>1</sub> = 0.012 m, <span class="html-italic">X</span><sub>3</sub> = 0.005 m. (<b>e</b>) <span class="html-italic">X</span><sub>1</sub> = 0.012 m, <span class="html-italic">X</span><sub>3</sub> = 0.006 m. (<b>f</b>) <span class="html-italic">X</span><sub>1</sub> = 0.012 m, <span class="html-italic">X</span><sub>3</sub> = 0.007 m. (<b>g</b>) <span class="html-italic">X</span><sub>1</sub> = 0.013 m, <span class="html-italic">X</span><sub>3</sub> = 0.005 m. (<b>h</b>) <span class="html-italic">X</span><sub>1</sub> = 0.013 m, <span class="html-italic">X</span><sub>3</sub> = 0.006 m. (<b>i</b>) <span class="html-italic">X</span><sub>1</sub> = 0.013 m, <span class="html-italic">X</span><sub>3</sub> = 0.007 m.</p>
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<p>Resultant velocity magnitude analysis in the separation movement of black soldier fly. (<b>a</b>) <span class="html-italic">X</span><sub>1</sub> = 0.011 m, <span class="html-italic">X</span><sub>3</sub> = 0.005 m. (<b>b</b>) <span class="html-italic">X</span><sub>1</sub> = 0.011 m, <span class="html-italic">X</span><sub>3</sub> = 0.006 m. (<b>c</b>) <span class="html-italic">X</span><sub>1</sub> = 0.011 m, <span class="html-italic">X</span><sub>3</sub> = 0.007 m. (<b>d</b>) <span class="html-italic">X</span><sub>1</sub> = 0.012 m, <span class="html-italic">X</span><sub>3</sub> = 0.005 m. (<b>e</b>) <span class="html-italic">X</span><sub>1</sub> = 0.012 m, <span class="html-italic">X</span><sub>3</sub> = 0.006 m. (<b>f</b>) <span class="html-italic">X</span><sub>1</sub> = 0.012 m, <span class="html-italic">X</span><sub>3</sub> = 0.007 m. (<b>g</b>) <span class="html-italic">X</span><sub>1</sub> = 0.013 m, <span class="html-italic">X</span><sub>3</sub> = 0.005 m. (<b>h</b>) <span class="html-italic">X</span><sub>1</sub> = 0.013 m, <span class="html-italic">X</span><sub>3</sub> = 0.006 m. (<b>i</b>) <span class="html-italic">X</span><sub>1</sub> = 0.013 m, <span class="html-italic">X</span><sub>3</sub> = 0.007 m.</p>
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<p>Resultant velocity magnitude analysis in the separation movement of black soldier fly. (<b>a</b>) <span class="html-italic">X</span><sub>1</sub> = 0.011 m, <span class="html-italic">X</span><sub>3</sub> = 0.005 m. (<b>b</b>) <span class="html-italic">X</span><sub>1</sub> = 0.011 m, <span class="html-italic">X</span><sub>3</sub> = 0.006 m. (<b>c</b>) <span class="html-italic">X</span><sub>1</sub> = 0.011 m, <span class="html-italic">X</span><sub>3</sub> = 0.007 m. (<b>d</b>) <span class="html-italic">X</span><sub>1</sub> = 0.012 m, <span class="html-italic">X</span><sub>3</sub> = 0.005 m. (<b>e</b>) <span class="html-italic">X</span><sub>1</sub> = 0.012 m, <span class="html-italic">X</span><sub>3</sub> = 0.006 m. (<b>f</b>) <span class="html-italic">X</span><sub>1</sub> = 0.012 m, <span class="html-italic">X</span><sub>3</sub> = 0.007 m. (<b>g</b>) <span class="html-italic">X</span><sub>1</sub> = 0.013 m, <span class="html-italic">X</span><sub>3</sub> = 0.005 m. (<b>h</b>) <span class="html-italic">X</span><sub>1</sub> = 0.013 m, <span class="html-italic">X</span><sub>3</sub> = 0.006 m. (<b>i</b>) <span class="html-italic">X</span><sub>1</sub> = 0.013 m, <span class="html-italic">X</span><sub>3</sub> = 0.007 m.</p>
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<p>Comparison of actual and simulated images of larvae movement at the screen tail.</p>
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15 pages, 5862 KiB  
Article
Analysis of HMCVT Shift Quality Based on the Engagement Characteristics of Wet Clutch
by Kai Lu and Zhixiong Lu
Agriculture 2022, 12(12), 2012; https://doi.org/10.3390/agriculture12122012 - 25 Nov 2022
Cited by 3 | Viewed by 1516
Abstract
A wet clutch is the key shift part of the hydro-mechanical continuously variable transmission (HMCVT), and the working characteristics have an important influence on the shift quality of HMCVT. To reduce impact during the shift and improve engagement quality, this paper analyzed the [...] Read more.
A wet clutch is the key shift part of the hydro-mechanical continuously variable transmission (HMCVT), and the working characteristics have an important influence on the shift quality of HMCVT. To reduce impact during the shift and improve engagement quality, this paper analyzed the influence of system oil pressure and the clutch’s working flow on the engagement characteristics of the wet clutch in terms of shift quality. Firstly, the engagement characteristics (including oil pressure variation characteristics and dynamic torque characteristics) of the wet clutch were tested with different working flows and system oil pressures based on the HMCVT shift clutch bench. Then, the shift impact and sliding friction work were used to evaluate the shift quality. An evaluation function was established based on the maximum shift impact and the maximum sliding friction work to obtain the optimal shift quality. Finally, a shift model was built using Simulation X to simulate the shift quality of nine groups of engagement characteristics. The results showed that increasing the working flow can reduce the wet clutch engagement time by 1.7 s at most, and increasing the system oil pressure can only reduce this by 0.1 s. The higher working flow and system oil pressure can increase the shift impact and reduce the sliding friction work. The combination of the working flow and system oil pressure with the minimum evaluation function value is (10 L/min, 2.0 MPa), and the shift quality is the best. The research can provide a reference for the design, shift control, and shift quality improvement of an HMCVT wet clutch. Full article
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<p>The diagram of the three-stage HMCVT.</p>
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<p>The HMCVT shift clutch test bench.</p>
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<p>The working principle of the wet clutch and its hydraulic control device. Note: 1: active component; 2: support bearing; 3: driven member; 4: friction element; 5: solenoid valve; 6: one-way throttle valve; 7: hydraulic pump; 8: relief valve; 9: piston sealing ring; 10: piston; 11: return spring.</p>
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<p>A three-stage HMCVT model for shift quality based on Simulation X.</p>
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<p>The relationship between the transmission ratio and displacement ratio of three-stage HMCVT.</p>
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<p>The oil pressure variation characteristics. (<b>a</b>) Data of test with 1.5–2.5 MPa, and 2, 4 L/min; (<b>b</b>) Data of test with 1.5–2.5 MPa, and 6, 8 L/min; (<b>c</b>) Data of test with 1.5–2.5 MPa, and 10, 12 L/min.</p>
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<p>Dynamic torque characteristics of wet clutch.</p>
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<p>Changes in HMCVT output shaft speed and torque in the nine groups of tests. (<b>a</b>) Changes in speed in the nine groups of tests; (<b>b</b>) Changes in torque in the nine groups of tests.</p>
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<p>Simulation results of HMCVT shift quality. (<b>a</b>) Simulation results of shift impact; (<b>b</b>) Simulation results of sliding friction work.</p>
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<p>The evaluation function values of nine groups of combinations.</p>
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15 pages, 3734 KiB  
Article
Automatic Milk Quantity Recording System for Small-Scale Dairy Farms Based on Internet of Things
by Sanya Kaunkid, Apinan Aurasopon and Anut Chantiratiku
Agriculture 2022, 12(11), 1877; https://doi.org/10.3390/agriculture12111877 - 9 Nov 2022
Cited by 2 | Viewed by 4071
Abstract
The milk quantity of dairy cows is the most important piece of data in farm management. However, it is difficult to measure and record the milk quantity for small-scale dairy farms. Therefore, the automatic milk quantity recording system for small-scale dairy farms is [...] Read more.
The milk quantity of dairy cows is the most important piece of data in farm management. However, it is difficult to measure and record the milk quantity for small-scale dairy farms. Therefore, the automatic milk quantity recording system for small-scale dairy farms is studied. It consists of a weight scale mechanism and an embedded system installed on a wheelbarrow for measuring and recording milk quantity. For the process of the system, the milk quantity of each cow is measured based on the load cell in kilogram units. The data such as real-time clock, cow ID, and individual and total milk quantity are recorded on a microSD memory card and sent based on the Internet of Things (IoT) for recording in a Google sheet. Furthermore, the system can alert the farmers to remove the teat cups when the cow milk comes to the end by detecting the derivative of milk quantity with respect to time. The experimental results show that the proposed system can correctly measure and record milk quantity. This system can help the farmers in improving and managing dairy farms effectively. Full article
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<p>Bucket milking machine: two milking units.</p>
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<p>Milk quantity recording system by hand.</p>
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<p>Proposed system.</p>
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<p>Milk wheelbarrow: (<b>a</b>) weight scale mechanism and (<b>b</b>) milk bucket positioning.</p>
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<p>Circuit diagram for the proposed system.</p>
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<p>State transition diagram.</p>
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<p>Milk weight and its derivative with respect to time.</p>
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<p>Data format: (<b>a</b>) Google sheet and (<b>b</b>) SD memory card.</p>
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<p>Prototype of the proposed system.</p>
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<p>Milking stall used in experiments.</p>
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<p>Real-time weight on LCD displaying (<b>a</b>) Control unit#2 and (<b>b</b>) Control unit#1.</p>
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<p>Data recording on SD memory card: (<b>a</b>) Control unit#1 and (<b>b</b>) Control unit#2.</p>
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<p>Data recording in Google sheet.</p>
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<p>Milk weight change of cow, C_10, versus time plot.</p>
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15 pages, 2959 KiB  
Article
Development of the Reliability Assessment Process of the Hydraulic Pump for a 78 kW Tractor during Major Agricultural Operations
by Md. Abu Ayub Siddique, Yong-Joo Kim, Seung-Min Baek, Seung-Yun Baek, Tae-Ho Han, Wan-Soo Kim, Yeon-Soo Kim, Ryu-Gap Lim and Yong Choi
Agriculture 2022, 12(10), 1609; https://doi.org/10.3390/agriculture12101609 - 4 Oct 2022
Cited by 3 | Viewed by 2114
Abstract
This study focuses on the development of the reliability test method for the hydraulic pump of a tractor during major agricultural operations (plow, rotary, baler, and wrapping) at various driving and PTO (power take-off) gear stages. The hydraulic-pressure-measurement system was installed on the [...] Read more.
This study focuses on the development of the reliability test method for the hydraulic pump of a tractor during major agricultural operations (plow, rotary, baler, and wrapping) at various driving and PTO (power take-off) gear stages. The hydraulic-pressure-measurement system was installed on the tractor. The measured hydraulic pressure and engine rotational speed were converted to the equivalent pressure and engine speed for each agricultural operation using a mathematical formula. Additionally, the overall equivalent pressure and overall engine speed were calculated to determine the acceleration lifetime. The average equivalent pressure and engine speed for plow tillage were calculated at around 5.44 MPa and 1548.37 rpm, respectively, whereas the average equivalent pressure and engine speed for rotary tillage were almost 5.70 MPa and 2074.73 rpm, accordingly. In the case of baler and wrapping operations, the average equivalent pressure and engine speed were approximately 11.22 MPa and 2203.01 rpm, and 11.86 MPa and 913.76 rpm, respectively. The overall hydraulic pressure of the pump and the engine rotational speed were found to be around 10.07 MPa and 1512.93 rpm, respectively. The acceleration factor was calculated using the overall pressure and engine speed accounting for 336. In summary, the developed reliability test method was evaluated by RS-B-0063, which is the existing reliability evaluation standard for agricultural hydraulic gear pumps. The evaluation results proved that the developed reliability test method for the hydraulic pump of a tractor satisfied the standard criteria. Therefore, it could be said that the developed reliability test method could be applicable to the hydraulic pump of the tractor during agricultural field operations. Full article
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<p>Pressure sensors installed in the experimental tractor.</p>
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<p>The data−acquisition system installed in the tractor.</p>
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<p>The pressure and engine rotational speed for major agricultural operations.</p>
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