AgriEngineering最新文献_第5页

Improving Coffee Yield Interpolation in the Presence of Outliers Using Multivariate Geostatistics and Satellite Data 利用多元地理统计和卫星数据改进异常值情况下的咖啡产量内插法

AgriEngineering Pub Date : 2024-01-10 DOI: 10.3390/agriengineering6010006

César de Oliveira Ferreira Silva, C. R. Grego, R. Manzione, Stanley Robson de Medeiros Oliveira

{"title":"Improving Coffee Yield Interpolation in the Presence of Outliers Using Multivariate Geostatistics and Satellite Data","authors":"César de Oliveira Ferreira Silva, C. R. Grego, R. Manzione, Stanley Robson de Medeiros Oliveira","doi":"10.3390/agriengineering6010006","DOIUrl":"https://doi.org/10.3390/agriengineering6010006","url":null,"abstract":"Precision agriculture for coffee production requires spatial knowledge of crop yield. However, difficulties in implementation lie in low-sampled areas. In addition, the asynchronicity of this crop adds complexity to the modeling. It results in a diversity of phenological stages within a field and also continuous production of coffee over time. Big Data retrieved from remote sensing can be tested to improve spatial modeling. This research proposes to apply the Sentinel-2 vegetation index (NDVI) and the Sentinel-1 dual-polarization C-band Synthetic Aperture Radar (SAR) dataset as auxiliary variables in the multivariate geostatistical modeling of coffee yield characterized by the presence of outliers and assess improvement. A total of 66 coffee yield points were sampled from a 4 ha area in a quasi-regular grid located in southeastern Brazil. Ordinary kriging (OK) and block cokriging (BCOK) were applied. Overall, coupling coffee yield with the NDVI and/or SAR in BCOK interpolation improved the accuracy of spatial interpolation of coffee yield even in the presence of outliers. Incorporating Big Data for improving the modeling for low-sampled fields requires taking into account the difference in supports between different datasets since this difference can increase uncontrolled uncertainty. In this manner, we will consider, for future research, new tests with other covariates. This research has the potential to support precision agriculture applications as site-specific plant nutrient management.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"8 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139439464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Is It Possible to Measure the Quality of Sugarcane in Real-Time during Harvesting Using Onboard NIR Spectroscopy? 利用机载近红外光谱仪在收割过程中实时测量甘蔗质量可行吗？

AgriEngineering Pub Date : 2024-01-09 DOI: 10.3390/agriengineering6010005

L. P. Corrêdo, J. Molin, Ricardo Canal Filho

{"title":"Is It Possible to Measure the Quality of Sugarcane in Real-Time during Harvesting Using Onboard NIR Spectroscopy?","authors":"L. P. Corrêdo, J. Molin, Ricardo Canal Filho","doi":"10.3390/agriengineering6010005","DOIUrl":"https://doi.org/10.3390/agriengineering6010005","url":null,"abstract":"In-field quality prediction in agricultural products is mainly based on near-infrared spectroscopy (NIR). However, initiatives applied to sugarcane quality are only observed under laboratory-controlled conditions. This study proposed a framework for NIR spectroscopy sensing to measure sugarcane quality during a real harvest operation. A platform was built to support the system composed of the NIR sensor and external lighting on the elevator of a sugarcane harvester. Real-time data were acquired in commercial fields. Georeferenced samples were collected for calibration, validation, and adjustment of the multivariate models by partial least squares (PLS) regression. In addition, subsamples of defibrated cane were NIR-acquired for the development of calibration transfer models by piecewise direct standardization (PDS). The method allowed the adjustment of the spectra collected in real time to predict the quality properties of soluble solids content (Brix), apparent sucrose in juice (Pol), fiber, cane Pol, and total recoverable sugar (TRS). The results of the relative mean square error of prediction (RRMSEP) were from 1.80 to 2.14%, and the ratio of interquartile performance (RPIQ) was from 1.79 to 2.46. The PLS-PDS models were applied to data acquired in real-time, allowing estimation of quality properties and identification of the existence of spatial variability in quality. The results showed that it is possible to monitor the spatial variability of quality properties in sugarcane in the field. Future studies with a broader range of quality attribute values and the evaluation of different configurations for sensing devices, calibration methods, and data processing are needed. The findings of this research will enable a valuable spatial information layer for the sugarcane industry, whether for agronomic decision-making, industrial operational planning, or financial management between sugar mills and suppliers.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"44 49","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139442408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Determination of Dry-Matter Content of Kiwifruit before Harvest Based on Hyperspectral Imaging 基于高光谱成像测定收获前猕猴桃的干物质含量

AgriEngineering Pub Date : 2024-01-08 DOI: 10.3390/agriengineering6010004

Han Yang, Qian Chen, Jianping Qian, Jiali Li, Xintao Lin, Zihan Liu, Nana Fan, Wei Ma

{"title":"Determination of Dry-Matter Content of Kiwifruit before Harvest Based on Hyperspectral Imaging","authors":"Han Yang, Qian Chen, Jianping Qian, Jiali Li, Xintao Lin, Zihan Liu, Nana Fan, Wei Ma","doi":"10.3390/agriengineering6010004","DOIUrl":"https://doi.org/10.3390/agriengineering6010004","url":null,"abstract":"Determining pre-harvest fruit maturity is vital to ensure the quality of kiwifruit, and dry-matter content is an important indicator of kiwifruit ripeness. To predict the pre-harvest dry-matter content of kiwifruit continuously in real-time with high accuracy, this study uses hyperspectral data of pre-harvest Jintao kiwifruit obtained by using a hyperspectral image acquisition device. The raw data underwent whiteboard correction, spectral data extraction, spectral pre-processing, and feature-band extraction, following which the dry-matter content of the fruit was predicted by using partial least squares (PLS) regression. The feature bands extracted by the random frog method were 538.93, 671.14, 693.41, 770.61, 796.98, 813.24, 841.21, 843.29, and 856.80 nm, which improve the accuracy of the PLS method for predicting dry-matter content, with R2 = 0.92 and a root mean square error (RMSE) of 0.41% for the training set, and R2 = 0.85 and a RMSE of 0.50% for the test set. These results show that the proposed method reduces the number of required bands while maintaining the prediction accuracy, thereby demonstrating the reliability of using hyperspectral data to predict the pre-harvest dry-matter content of kiwifruit. This method can effectively guide the management of kiwifruit harvesting period, establishing a theoretical foundation for precise unmanned harvesting.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"12 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139445316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Influencing Factors Analysis of Aquaculture Mechanization Development in Liaoning, China 中国辽宁水产养殖机械化发展的影响因素分析

AgriEngineering Pub Date : 2024-01-08 DOI: 10.3390/agriengineering6010003

Lixingbo Yu, Hai-hui Wang, Anqi Ren, Fengfan Han, Fei Jia, Haochen Hou, Ying Liu

{"title":"The Influencing Factors Analysis of Aquaculture Mechanization Development in Liaoning, China","authors":"Lixingbo Yu, Hai-hui Wang, Anqi Ren, Fengfan Han, Fei Jia, Haochen Hou, Ying Liu","doi":"10.3390/agriengineering6010003","DOIUrl":"https://doi.org/10.3390/agriengineering6010003","url":null,"abstract":"Promoting the mechanization of aquaculture is one of the most important supporting measures to ensure the high-quality development of the aquaculture industry in China. In order to solve the problems of predominantly manual work and to decrease the costs of aquaculture, the influencing factors of China’s aquaculture mechanization were systematically analyzed. The triple bottom theory was selected, and three aspects were identified, including environmental, economic, and social aspects. Through the literature review, the Delphi method, and the analytic hierarchy process, the comprehensive evaluation indicator system, including 18 influencing factors, was proposed. Moreover, the fuzzy comprehensive evaluation method was combined with the model to solve the evaluation results. A case study in Liaoning Province was offered and, according to the analysis results, the economic aspect at the first level was the most critical factor; the financial subsidy for the purchase of aquaculture machinery, the energy consumption of the machinery and equipment, and the promotion and use of aquaculture technology were the most important factors and had the greatest impact on the development of aquaculture mechanization in China. The effective implementation paths and countermeasures were proposed, such as the promotion of mechanized equipment and the enhancement of the machinery purchase subsidies, in order to provide an important decision-making basis for the improvement of the level of aquaculture mechanization.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"5 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139446759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrating Satellite and UAV Technologies for Maize Plant Height Estimation Using Advanced Machine Learning 整合卫星和无人机技术，利用高级机器学习估算玉米株高

AgriEngineering Pub Date : 2024-01-05 DOI: 10.3390/agriengineering6010002

Marcelo Araújo Junqueira Ferraz, Thiago Orlando Costa Barboza, Pablo de Sousa Arantes, R. G. Von Pinho, Adão Felipe dos Santos

{"title":"Integrating Satellite and UAV Technologies for Maize Plant Height Estimation Using Advanced Machine Learning","authors":"Marcelo Araújo Junqueira Ferraz, Thiago Orlando Costa Barboza, Pablo de Sousa Arantes, R. G. Von Pinho, Adão Felipe dos Santos","doi":"10.3390/agriengineering6010002","DOIUrl":"https://doi.org/10.3390/agriengineering6010002","url":null,"abstract":"The integration of aerial monitoring, utilizing both unmanned aerial vehicles (UAVs) and satellites, alongside sophisticated machine learning algorithms, has witnessed a burgeoning prevalence within contemporary agricultural frameworks. This study endeavors to systematically explore the inherent potential encapsulated in high-resolution satellite imagery, concomitantly accompanied by an RGB camera seamlessly integrated into an UAV. The overarching objective is to elucidate the viability of this technological amalgamation for accurate maize plant height estimation, facilitated by the application of advanced machine learning algorithms. The research involves the computation of key vegetation indices—NDVI, NDRE, and GNDVI—extracted from PlanetScope satellite images. Concurrently, UAV-based plant height estimation is executed using digital elevation models (DEMs). Data acquisition encompasses images captured on days 20, 29, 37, 44, 50, 61, and 71 post-sowing. The study yields compelling results: (1) Maize plant height, derived from DEMs, demonstrates a robust correlation with manual field measurements (r = 0.96) and establishes noteworthy associations with NDVI (r = 0.80), NDRE (r = 0.78), and GNDVI (r = 0.81). (2) The random forest (RF) model emerges as the frontrunner, displaying the most pronounced correlations between observed and estimated height values (r = 0.99). Additionally, the RF model’s superiority extends to performance metrics when fueled by input parameters, NDVI, NDRE, and GNDVI. This research underscores the transformative potential of combining satellite imagery, UAV technology, and machine learning for precision agriculture and maize plant height estimation.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"72 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139450133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cotton Gin Stand Machine-Vision Inspection and Removal System for Plastic Contamination: Hand Intrusion Sensor Design 棉花轧花机架塑料污染检测和清除系统：手工入侵传感器设计

AgriEngineering Pub Date : 2023-12-22 DOI: 10.3390/agriengineering6010001

M. Pelletier, J. Wanjura, Jon R. Wakefield, Gregory A. Holt, Neha Kothari

{"title":"Cotton Gin Stand Machine-Vision Inspection and Removal System for Plastic Contamination: Hand Intrusion Sensor Design","authors":"M. Pelletier, J. Wanjura, Jon R. Wakefield, Gregory A. Holt, Neha Kothari","doi":"10.3390/agriengineering6010001","DOIUrl":"https://doi.org/10.3390/agriengineering6010001","url":null,"abstract":"Plastic contamination in cotton lint poses significant challenges to the U.S. cotton industry, with plastic wrap from John Deere round module harvesters being a primary contaminant. Despite efforts to manually remove this plastic during module unwrapping, some inevitably enters the cotton gin’s processing system. To address this, a machine-vision detection and removal system has been developed. This system uses inexpensive color cameras to identify plastic on the gin stand feeder apron, triggering a mechanism that expels the plastic from the cotton stream. However, the system, composed of 30–50 Linux-based ARM computers, requires substantial effort for calibration and tuning and presents a technological barrier for typical cotton gin workers. This research aims to transition the system to a more user-friendly, plug-and-play model by implementing an auto-calibration function. The proposed function dynamically tracks cotton colors while excluding plastic images that could hinder performance. A critical component of this auto-calibration algorithm is the hand intrusion detector, or “HID”, which is discussed in this paper. In the normal operation of a cotton gin, the gin personnel periodically have to clear the machine, which entails running a stick or their arm/hand under the detection cameras. This results in the system capturing a false positive, which interferes with the ability of auto-calibration algorithms to function correctly. Hence, there is a critical need for an HID to remove these false positives from the record. The anticipated benefits of the auto-calibration function include reduced setup and maintenance overhead, less reliance on skilled personnel, and enhanced adoption of the plastic removal system within the cotton ginning industry.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"73 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139164288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Harnessing Solar Energy: A Novel Hybrid Solar Dryer for Efficient Fish Waste Processing 利用太阳能：高效处理鱼类废弃物的新型混合太阳能干燥器

AgriEngineering Pub Date : 2023-12-15 DOI: 10.3390/agriengineering5040150

Mohamed Deef, Helal Samy Helal, Islam El-Sebaee, M. Nadimi, J. Paliwal, Ayman Ibrahim

{"title":"Harnessing Solar Energy: A Novel Hybrid Solar Dryer for Efficient Fish Waste Processing","authors":"Mohamed Deef, Helal Samy Helal, Islam El-Sebaee, M. Nadimi, J. Paliwal, Ayman Ibrahim","doi":"10.3390/agriengineering5040150","DOIUrl":"https://doi.org/10.3390/agriengineering5040150","url":null,"abstract":"Facing severe climate change, preserving the environment, and promoting sustainable development necessitate innovative global solutions such as waste recycling, extracting value-added by-products, and transitioning from traditional to renewable energy sources. Accordingly, this study aims to repurpose fish waste into valuable, nutritionally rich products and extract essential chemical compounds such as proteins and oils using a newly developed hybrid solar dryer (HSD). This proposed HSD aims to produce thermal energy for drying fish waste through the combined use of solar collectors and solar panels. The HSD, primarily composed of a solar collector, drying chamber, auxiliary heating system, solar panels, battery, pump, heating tank, control panel, and charging unit, has been designed for the effective drying of fish waste. We subjected the fish waste samples to controlled drying at three distinct temperatures: 45, 50, and 55 °C. The results indicated a reduction in moisture content from 75.2% to 24.8% within drying times of 10, 7, and 5 h, respectively, at these temperatures. Moreover, maximum drying rates of 1.10, 1.22, and 1.41 kgH2O/kg dry material/h were recorded at 45, 50, and 55 °C, respectively. Remarkable energy efficiency was also observed in the HSD’s operation, with savings of 79.2%, 75.8%, and 62.2% at each respective temperature. Notably, with an increase in drying temperature, the microbial load, crude lipid, and moisture content decreased, while the crude protein and ash content increased. The outcomes of this study indicate that the practical, solar-powered HSD can recycle fish waste, enhance its value, and reduce the carbon footprint of processing operations. This sustainable approach, underpinned by renewable energy, offers significant environmental preservation and a reduction in fossil fuel reliance for industrial operations.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"66 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139000220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Coffee Growing with Remotely Piloted Aircraft System: Bibliometric Review 利用遥控飞机系统种植咖啡：文献计量学评论

AgriEngineering Pub Date : 2023-12-15 DOI: 10.3390/agriengineering5040151

N. L. Bento, G. Ferraz, L. S. Santana, Mirian de Lourdes Oliveira e Silva

{"title":"Coffee Growing with Remotely Piloted Aircraft System: Bibliometric Review","authors":"N. L. Bento, G. Ferraz, L. S. Santana, Mirian de Lourdes Oliveira e Silva","doi":"10.3390/agriengineering5040151","DOIUrl":"https://doi.org/10.3390/agriengineering5040151","url":null,"abstract":"Remotely piloted aircraft systems (RPASs) have gained prominence in recent decades primarily due to their versatility of application in various sectors of the economy. In the agricultural sector, they stand out for optimizing processes, contributing to improved sampling, measurements, and operational efficiency, ultimately leading to increased profitability in crop production. This technology is becoming a reality in coffee farming, an essential commodity in the global economic balance, mainly due to academic attention and applicability. This study presents a bibliometric analysis focused on using RPASs in coffee farming to structure the existing academic literature and reveal trends and insights into the research topic. For this purpose, searches were conducted over the last 20 years (2002 to 2022) in the Web of Science and Scopus scientific databases. Subsequently, bibliometric analysis was applied using Biblioshiny for Bibliometrix software in R (version 2022.07.1), with emphasis on the temporal evolution of research on the topic, performance analysis highlighting key publications, journals, researchers, institutions, countries, and the scientific mapping of co-authorship, keywords, and future trends/possibilities. The results revealed 42 publications on the topic, with the pioneering studies being the most cited. Brazilian researchers and institutions (Federal University of Lavras) have a strong presence in publications on the subject and in journals focusing on technological applications. As future trends and possibilities, the employment of technology optimizes the productivity and profitability studies of coffee farming for the timely and efficient application of aerial imaging.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"39 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139000648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic Behavior Forecast of an Experimental Indirect Solar Dryer Using an Artificial Neural Network 利用人工神经网络预测试验性间接太阳能干燥器的动态行为

AgriEngineering Pub Date : 2023-12-14 DOI: 10.3390/agriengineering5040149

Ángel Tlatelpa Becerro, Ramiro Rico Martínez, Erick César López-Vidaña, Esteban Montiel Palacios, César Torres Segundo, José Luis Gadea Pacheco

{"title":"Dynamic Behavior Forecast of an Experimental Indirect Solar Dryer Using an Artificial Neural Network","authors":"Ángel Tlatelpa Becerro, Ramiro Rico Martínez, Erick César López-Vidaña, Esteban Montiel Palacios, César Torres Segundo, José Luis Gadea Pacheco","doi":"10.3390/agriengineering5040149","DOIUrl":"https://doi.org/10.3390/agriengineering5040149","url":null,"abstract":"This research presents the prediction of temperatures in the chamber of a solar dryer using artificial neural networks (ANN). The dryer is a forced-flow type and indirect. Climatic conditions, temperatures, airflow, and geometric parameters were considered to build the ANN model. The model was a feed-forward network trained using a backpropagation algorithm and Levenberg–Marquardt optimization. The configuration of the optimal neural network to carry out the verification and validation processes was nine neurons in the input layer, one in the output layer, and two hidden layers of thirteen and twelve neurons each (9-13-12-1). The percentage error of the predictive model was below 1%. The predictive model has been successfully tested, achieving a predictor with good capabilities. This consistency is reflected in the relative error between the predicted and experimental temperatures. The error is below 0.25% for the model’s verification and validation. Moreover, this model could be the basis for developing a powerful real-time operation optimization tool and the optimal design for indirect solar dryers to reduce cost and time in food-drying processes.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"20 S2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138971852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Validation of Criteria for Predicting Tractor Fuel Consumption and CO2 Emissions When Ploughing Fields of Different Shapes and Dimensions 犁不同形状和尺寸的田地时预测拖拉机燃料消耗量和二氧化碳排放量的标准验证

AgriEngineering Pub Date : 2023-12-12 DOI: 10.3390/agriengineering5040148

V. Damanauskas, A. Janulevičius

{"title":"Validation of Criteria for Predicting Tractor Fuel Consumption and CO2 Emissions When Ploughing Fields of Different Shapes and Dimensions","authors":"V. Damanauskas, A. Janulevičius","doi":"10.3390/agriengineering5040148","DOIUrl":"https://doi.org/10.3390/agriengineering5040148","url":null,"abstract":"Climate change is linked to CO2 emissions, the reduction of which has become a top priority. In response to these circumstances, scientists must constantly develop new technologies that increase fuel efficiency and reduce emissions. Agriculture today is dominated by arable fields of various sizes, shapes, and dimensions, and to achieve fuel economy and environmental impact requirements, it is not enough to know only the principles of optimization of tillage processes; it is also necessary to understand the influence of field size and its shape and dimensions on tillage performance. The purpose of this research is to present a methodology that allows predicting tractor fuel demand and CO2 emissions per unit of ploughed area when ploughing field plots with different shapes and dimensions and to confirm a suitable variable for such a prediction. Theoretical calculations and experimental tests have shown that the field ploughing time efficiency coefficient is a useful metric for comparing field plots of different shapes and dimensions. This coefficient effectively describes tractor fuel consumption and CO2 emissions during ploughing operations on differently configured field plots. A reasonable method for calculating the real field ploughing time efficiency coefficient is based on field and tillage data and a practical determination method using tractor engine load reports. It was found that during the research, when ploughing six field plots of different shapes and dimensions, with an area of 6 ha, the field ploughing time efficiency coefficient varied from 0.68 to 0.82, and fuel consumption between 15.6 and 16.5 kg/ha. In the field plot of 6 ha, where the field ploughing time efficiency coefficient was 15% higher, the fuel consumption per unit area was lower by about 5.5%. The results of this study will help to effectively predict tillage time and tractor fuel consumption required for different field shapes and dimensions.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"29 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139009565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0