Pub Date : 2024-01-10DOI: 10.3390/agriengineering6010006
César de Oliveira Ferreira Silva, C. R. Grego, R. Manzione, Stanley Robson de Medeiros Oliveira
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.
咖啡生产的精准农业需要了解作物产量的空间知识。然而，在取样较少的地区实施困难重重。此外，这种作物的非同步性也增加了建模的复杂性。它导致田间物候阶段的多样性以及咖啡产量的连续性。从遥感中获取的大数据可用于改进空间建模。本研究建议将哨兵-2 号植被指数（NDVI）和哨兵-1 号双极化 C 波段合成孔径雷达（SAR）数据集作为辅助变量，用于以存在异常值为特征的咖啡产量多元地理统计建模，并评估改进情况。在巴西东南部一个准规则网格中的 4 公顷区域内，共对 66 个咖啡产量点进行了采样。应用了普通克里金法（OK）和块克里金法（BCOK）。总体而言，在 BCOK 插值中将咖啡产量与 NDVI 和/或 SAR 相结合，即使存在异常值，也能提高咖啡产量空间插值的准确性。要结合大数据改进低采样率田块的建模，需要考虑不同数据集之间的支持差异，因为这种差异会增加不可控的不确定性。因此，在未来的研究中，我们将考虑使用其他协变量进行新的测试。这项研究有望为精准农业应用提供支持，如针对具体地点的植物养分管理。
{"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}
Pub Date : 2024-01-09DOI: 10.3390/agriengineering6010005
L. P. Corrêdo, J. Molin, Ricardo Canal Filho
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.
农产品的田间质量预测主要基于近红外光谱（NIR）。然而，应用于甘蔗质量的举措只能在实验室控制条件下进行观察。本研究提出了一个近红外光谱传感框架，用于在实际收割作业中测量甘蔗质量。建立了一个平台来支持由近红外传感器和甘蔗收割机升降机上的外部照明组成的系统。实时数据是在商业田地中获取的。通过偏最小二乘法（PLS）回归，收集了用于校准、验证和调整多元模型的地理参照样本。此外，还采集了去纤维甘蔗子样本的近红外光谱，以便通过分片直接标准化（PDS）建立校准转移模型。该方法允许对实时采集的光谱进行调整，以预测可溶性固形物含量（Brix）、果汁表观蔗糖（Pol）、纤维、甘蔗 Pol 和总可回收糖（TRS）的质量特性。预测结果的相对均方误差（RRMSEP）为 1.80% 至 2.14%，四分位数间性能比（RPIQ）为 1.79% 至 2.46%。PLS-PDS 模型适用于实时采集的数据，可用于估算质量特性和识别质量的空间变化。结果表明，在田间监测甘蔗质量特性的空间变化是可行的。今后需要对更广泛的质量属性值进行研究，并对传感设备的不同配置、校准方法和数据处理进行评估。这项研究成果将为甘蔗产业提供一个宝贵的空间信息层，无论是用于农艺决策、产业运营规划，还是用于糖厂和供应商之间的财务管理。
{"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}
Pub Date : 2024-01-08DOI: 10.3390/agriengineering6010004
Han Yang, Qian Chen, Jianping Qian, Jiali Li, Xintao Lin, Zihan Liu, Nana Fan, Wei Ma
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.
{"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}
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.
{"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}
Pub Date : 2024-01-05DOI: 10.3390/agriengineering6010002
Marcelo Araújo Junqueira Ferraz, Thiago Orlando Costa Barboza, Pablo de Sousa Arantes, R. G. Von Pinho, Adão Felipe dos Santos
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.
{"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}
Pub Date : 2023-12-22DOI: 10.3390/agriengineering6010001
M. Pelletier, J. Wanjura, Jon R. Wakefield, Gregory A. Holt, Neha Kothari
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.
皮棉中的塑料污染给美国棉花产业带来了巨大挑战，其中来自约翰迪尔圆形模块收割机的塑料包装是主要污染物。尽管在拆卸模块的过程中人工清除了这些塑料，但仍有一些不可避免地进入了轧棉机的加工系统。为了解决这个问题，我们开发了一种机器视觉检测和清除系统。该系统使用廉价的彩色摄像头来识别轧棉机架喂棉围裙上的塑料，并触发一个装置将塑料从棉花流中排出。然而，该系统由 30-50 台基于 Linux 的 ARM 计算机组成，需要花费大量精力进行校准和调整，对典型的轧棉工人来说存在技术障碍。本研究旨在通过实施自动校准功能，将该系统过渡为更方便用户使用的即插即用模式。建议的功能可动态跟踪棉花颜色，同时排除可能影响性能的塑料图像。这种自动校准算法的一个关键组件是手部入侵探测器，即本文讨论的 "HID"。在轧棉机的正常运行过程中，轧棉人员需要定期清理机器，这就需要用棍子或手臂/手从检测摄像头下穿过。这会导致系统捕捉到假阳性，从而影响自动校准算法的正常运行。因此，亟需一种 HID 来消除记录中的这些误报。自动校准功能的预期效益包括减少设置和维护费用，减少对技术人员的依赖，以及提高轧棉行业对塑料清除系统的采用率。
{"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}
Pub Date : 2023-12-15DOI: 10.3390/agriengineering5040150
Mohamed Deef, Helal Samy Helal, Islam El-Sebaee, M. Nadimi, J. Paliwal, Ayman Ibrahim
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.
{"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}
Pub Date : 2023-12-15DOI: 10.3390/agriengineering5040151
N. L. Bento, G. Ferraz, L. S. Santana, Mirian de Lourdes Oliveira e Silva
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.
近几十年来，遥控飞机系统（RPAS）的地位日益突出，这主要是由于它们在各个经济领域的广泛应用。在农业领域，遥控飞机系统可以优化流程，提高采样、测量和作业效率，最终提高作物生产的盈利能力。咖啡种植业是全球经济平衡中不可或缺的商品，这项技术正在成为现实，这主要归功于学术界的关注和适用性。本研究对咖啡种植中的遥感技术进行了文献计量分析，以构建现有学术文献的结构，揭示研究课题的趋势和见解。为此，我们在 Web of Science 和 Scopus 科学数据库中对过去 20 年（2002 年至 2022 年）的文献进行了检索。随后，使用 R 中的 Biblioshiny for Bibliometrix 软件（2022.07.1 版）进行了文献计量分析，重点是该主题研究的时间演变，突出重点出版物、期刊、研究人员、机构、国家的绩效分析，以及共同作者、关键词和未来趋势/可能性的科学映射。结果显示，有 42 篇关于该主题的出版物，其中先驱性研究的引用率最高。巴西的研究人员和机构（拉夫拉斯联邦大学）在有关该主题的出版物和侧重于技术应用的期刊中占有重要地位。作为未来的趋势和可能性，技术的应用优化了咖啡种植的生产率和收益率研究，从而及时有效地应用航空成像技术。
{"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}
Pub Date : 2023-12-14DOI: 10.3390/agriengineering5040149
Ángel Tlatelpa Becerro, Ramiro Rico Martínez, Erick César López-Vidaña, Esteban Montiel Palacios, César Torres Segundo, José Luis Gadea Pacheco
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.
本研究介绍了利用人工神经网络（ANN）预测太阳能干燥器腔室温度的方法。该干燥机为强制流式间接干燥机。建立 ANN 模型时考虑了气候条件、温度、气流和几何参数。该模型是一个使用反向传播算法和 Levenberg-Marquardt 优化训练的前馈网络。进行验证和确认过程的最佳神经网络配置为：输入层九个神经元，输出层一个神经元，两个分别由十三个和十二个神经元组成的隐藏层（9-13-12-1）。预测模型的误差率低于 1%。该预测模型已成功通过测试，实现了良好的预测能力。这种一致性体现在预测温度和实验温度之间的相对误差上。在对模型进行验证和确认时，误差低于 0.25%。此外，该模型可作为开发强大的实时操作优化工具和间接太阳能干燥器优化设计的基础，以降低食品干燥过程的成本和时间。
{"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}
Pub Date : 2023-12-12DOI: 10.3390/agriengineering5040148
V. Damanauskas, A. Janulevičius
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.
{"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}
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