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Sustainable Technology in Agricultural Engineering

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Agriculture".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 18740

Special Issue Editor

Dr. Chao Zhao

E-Mail Website
Guest Editor
College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou, Zhejiang 311300, China
Interests: biomass conversion; bioresource utilization technology

Special Issue Information

Dear Colleagues,

Agricultural engineering covers resources concerning many engineering applications in agriculture, including the design of machines, equipment, and buildings; soil and water engineering; irrigation and drainage engineering; crop harvesting, processing, and storage; animal production technology, housing, and equipment; precision agriculture; post-harvest processing and technology; rural development; agricultural mechanization; horticultural engineering; greenhouse structures and engineering; bioenergy; and aquacultural engineering.

Agricultural engineering plays an important role in the sustainable development of agriculture. This Special Issue aims to address the Sustainable Technology of Agricultural Engineering. The potential topics of interest include but are not limited to:

  • Design and development of agricultural equipment;
  • Processing and non-destructive testing of agricultural products;
  • Information and intelligence of agricultural equipment;
  • Conversion and utilization of agricultural residues;
  • The application of agricultural Internet of Things;
  • Intelligent animal husbandry;
  • Sustainable techniques for facility agriculture.

For this Special Issue, original research articles and reviews are welcome.

I look forward to receiving your contributions.

Dr. Chao Zhao
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • agricultural equipment
  • agricultural products processing
  • non-destructive testing technology
  • agricultural residues utilization
  • facility agriculture

Published Papers (18 papers)

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Research

16 pages, 9686 KiB  
Article
A Deep-Learning-Based System for Pig Posture Classification: Enhancing Sustainable Smart Pigsty Management
by Chanhui Jeon, Haram Kim and Dongsoo Kim
Sustainability 2024, 16(7), 2888; https://doi.org/10.3390/su16072888 - 29 Mar 2024
Viewed by 615
Abstract
This paper presents a deep-learning-based system for classifying pig postures, aiming to improve the management of sustainable smart pigsties. The classification of pig postures is a crucial concern for researchers investigating pigsty environments and for on-site pigsty managers. To address this issue, we [...] Read more.
This paper presents a deep-learning-based system for classifying pig postures, aiming to improve the management of sustainable smart pigsties. The classification of pig postures is a crucial concern for researchers investigating pigsty environments and for on-site pigsty managers. To address this issue, we developed a comprehensive system framework for pig posture classification within a pigsty. We collected image datasets from an open data sharing site operated by a public organization and systematically conducted the following steps: object detection, data labeling, image preprocessing, model development, and training. These processes were carried out using the acquired datasets to ensure comprehensive and effective training for our pig posture classification system. Subsequently, we analyzed and discussed the classification results using techniques such as Grad-CAM. As a result of visual analysis through Grad-CAM, it is possible to identify image features when posture is correctly classified or misclassified in a pig image. By referring to these results, it is expected that the accuracy of pig posture classification can be further improved. Through this analysis and discussion, we can identify which features of pig postures in images need to be emphasized to improve the accuracy of pig posture classification. The findings of this study are anticipated to significantly improve the accuracy of pig posture classification. In practical applications, the proposed pig posture classification system holds the potential to promptly detect abnormal situations in pigsties, leading to prompt responses. Ultimately, this can greatly contribute to increased productivity in pigsty operations, fostering efficiency enhancements in pigsty management. Full article
(This article belongs to the Special Issue Sustainable Technology in Agricultural Engineering)
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20 pages, 3315 KiB  
Article
Nondestructive Quantification of Isoflavones in Cotyledons by Near-Infrared Spectroscopy and Potential and Limits for Sustainable Soybean Breeding
by Jean Brustel, Monique Berger, Amandine Arnal, Patrice Jeanson, Jean Dayde and Cecile Levasseur-Garcia
Sustainability 2024, 16(6), 2436; https://doi.org/10.3390/su16062436 - 15 Mar 2024
Viewed by 647
Abstract
The isoflavones in the cotyledon of soybean seeds mimic human estrogen in structure, conferring them complex effects on health. Their regulation represents a major challenge for the sustainable breeding of new varieties with lower levels of potential endocrine disruptors. To develop a rapid, [...] Read more.
The isoflavones in the cotyledon of soybean seeds mimic human estrogen in structure, conferring them complex effects on health. Their regulation represents a major challenge for the sustainable breeding of new varieties with lower levels of potential endocrine disruptors. To develop a rapid, nondestructive, and eco-friendly analysis method, this study explores how sample grinding affects the results of near-infrared spectroscopy (NIRS) and the preprocessing methods. The prediction of the daidzein and genistein content would help the specific reduction in isoflavones in the cotyledon without harming seed development. The potential of a nonlinear approach (artificial neural network) is also compared with the more conventional partial least squares (PLS) regression. The isoflavone content of cotyledons from 529 soybean samples (65 genotypes) was quantified by HPLC, and the NIR spectra of these samples were collected using a Brucker multi-purpose analyzer. The spectra of whole and ground cotyledons were also collected for 155 samples. The results show that grain fragmentation improves the model calibration, although spectral preprocessing can harmonize this effect. Although the best PLS regression in cross-validation did not suffice to quantify the daidzein and genistein percentages, the artificial neural network (ANN) approach allowed us to develop much more reliable models than PLS. The performance of ANNs in external validation is remarkable in terms of both precision and applicability (R2 = 0.89 and a ratio of prediction to deviation of 2.92), making ANNs suitable in the breeding context for screening soybean grains regarding their isoflavone content. Full article
(This article belongs to the Special Issue Sustainable Technology in Agricultural Engineering)
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13 pages, 1769 KiB  
Article
Non-Destructive Analysis Using Near-Infrared Spectroscopy to Predict Albumin, Globulin, Glutelin, and Total Protein Content in Sunflower Seeds
by Cecile Levasseur-Garcia, Pierre Castellanet, Camille Henry, Christelle Florin, Marion Laporte, Virginie Mirleau-Thebaud, Sandrine Plut and Anne Calmon
Sustainability 2024, 16(2), 737; https://doi.org/10.3390/su16020737 - 15 Jan 2024
Viewed by 766
Abstract
This pilot study explores the potential of near-infrared spectroscopy (NIRS) for predicting sunflower seed protein content, focusing on both crushed and husked samples to address agricultural sustainability concerns. Sunflower seeds are renowned for their richness in both oil and protein content. The important [...] Read more.
This pilot study explores the potential of near-infrared spectroscopy (NIRS) for predicting sunflower seed protein content, focusing on both crushed and husked samples to address agricultural sustainability concerns. Sunflower seeds are renowned for their richness in both oil and protein content. The important role of sunflower seeds in the food and feed industries underscores the importance of using precise analytical tools to determine their composition. In essence, the nature of the hull of sunflower seeds, which skews the interaction between the seed and light, necessitates a sophisticated analysis. This study analyzes 326 samples using a near-infrared spectrometer to develop robust partial least squares (PLS) models. High accuracy is achieved in predicting total protein for crushed samples (r²c = 0.97, RMSEC 0.54%, RPDc 6; r²p = 0.78, RMSEP 1.24%, RPDp 2.1). Extending the scope to husked samples, promising results emerge for crude protein prediction (r²c = 0.93, RMSEC 0.86%, RPDc 3.9; r²cv = 0.83, RMSECV 1.39%, RPDcv 2.4). Additionally, this study delves into protein fractions (globulin, albumin, and glutelin) in crushed seeds, adding depth to the analysis. In conclusion, NIR spectroscopy proves valuable for rapid prescreening in breeding, especially when working with hulled grains, offering non-destructive efficiency and predictive accuracy in agricultural analysis. The novel exploration of protein fractions in sunflower seeds further enhances this study’s importance, providing a valuable contribution to the field and underscoring the practical applications of NIR spectroscopy in sustainable agriculture. In conclusion, the opacity of sunflower seed hulls poses challenges in infrared spectroscopy, limiting light penetration and accuracy. Dehulled seeds are preferred for reliable results, overcoming hull-related limitations. Although grinding provides the advantages of uniformity and reproducibility for near-infrared (NIR) spectroscopy, the preference for dehulled grains persists. The practical need for accurate analysis in agriculture and breeding drives the choice of spectroscopy on dehulled seeds, allowing for replanting. Full article
(This article belongs to the Special Issue Sustainable Technology in Agricultural Engineering)
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18 pages, 5322 KiB  
Article
Study of the Sustainability of Ecological and Chemical Indicators of Soils in Organic Farming
by Vladimir Ivanovich Trukhachev, Sergey Leonidovich Belopukhov, Marina Grigoryeva and Inna Ivanovna Dmitrevskaya
Sustainability 2024, 16(2), 665; https://doi.org/10.3390/su16020665 - 12 Jan 2024
Viewed by 732
Abstract
Organic farming is often seen as a sustainable alternative to intensive agricultural systems. The studies conducted in this direction analyze various factors, as well as their assemblies, and show contradictory results. In order to assess the impact of the organic method of soil [...] Read more.
Organic farming is often seen as a sustainable alternative to intensive agricultural systems. The studies conducted in this direction analyze various factors, as well as their assemblies, and show contradictory results. In order to assess the impact of the organic method of soil cultivation on the stable composition of the most important mineral and organic substances in the production process, the organic agriculture procedure was implemented with an agrochemical analysis for 12 years. The content of mobile phosphorus, exchangeable potassium, and humus in the soil was determined. An elemental analysis of soil samples was conducted for a more in-depth analysis of its composition. It was established that the soils of the farm contained a sufficient amount of exchangeable potassium and humus. The content of these components remained stable during the study period. It was discovered that the soils of the farm have a low content of mobile phosphorus, which also remained stable during the study period. In the studied farm, the applied farming technologies contribute to the stable content of the main nutrient components of the soil. But to correct the content of mobile forms of phosphorus, additional agrotechnical measures are required. Full article
(This article belongs to the Special Issue Sustainable Technology in Agricultural Engineering)
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18 pages, 2121 KiB  
Article
The Impact of Purchasing New Agricultural Machinery on Fuel Consumption on Farms
by Maciej Kuboń, Michał Cupiał, Anna Szeląg-Sikora and Marcin Kobuszewski
Sustainability 2024, 16(1), 52; https://doi.org/10.3390/su16010052 - 20 Dec 2023
Viewed by 570
Abstract
The aim of this study was to see how purchases of new agricultural machinery affected fuel consumption on farms. This study, conducted in the Małopolska region in Poland, covered two reporting periods (before and after the purchase of machinery). The analysis included factors [...] Read more.
The aim of this study was to see how purchases of new agricultural machinery affected fuel consumption on farms. This study, conducted in the Małopolska region in Poland, covered two reporting periods (before and after the purchase of machinery). The analysis included factors relevant to the indicators analyzed, including changes in fuel consumption, changes in the area of agrotechnical treatments, changes in working time, and changes in installed power. To study how fuel consumption evolves under different conditions, the following variables were used as grouping variables: area of farms, power of the largest tractor, index of technological modernization (ITM), groups of crops, groups of agrotechnical treatments, and groups of machinery. Statistical analysis showed significant differences between the analyzed groups. The research showed that the purchases of new agricultural machinery increased fuel consumption on farms. In the population studied, the volume increased by 8% compared to the initial period. The increase in consumption after modernization was mainly due to the purchase of more powerful tractors, while the increase in productivity and the changes in technology due to more modern ones did not compensate for the increase in power demand. Full article
(This article belongs to the Special Issue Sustainable Technology in Agricultural Engineering)
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18 pages, 7780 KiB  
Article
Simulation and Experiment of the Smoothness Performance of an Electric Four-Wheeled Chassis in Hilly and Mountainous Areas
by Yuan Fu, Zheng Liu, Yuxiao Jiang, Yuancai Leng, Jialong Tang, Renqi Wang and Xiaorong Lv
Sustainability 2023, 15(24), 16868; https://doi.org/10.3390/su152416868 - 15 Dec 2023
Viewed by 806
Abstract
This paper addresses the issues caused by traditional tractors during seeding operations, such as soil compaction, decreased soil fertility, use of unclean fuel leading to environmental pollution, and the disruption of sustainable development. In response, the study designs a compact and lightweight electric [...] Read more.
This paper addresses the issues caused by traditional tractors during seeding operations, such as soil compaction, decreased soil fertility, use of unclean fuel leading to environmental pollution, and the disruption of sustainable development. In response, the study designs a compact and lightweight electric four-wheel-drive chassis for a seeding robot suitable for strip planting of soybeans and corn. Using RecurDyn(V9R2) software and MATLAB/Simulink(2020a) modules, the paper conducts simulation and analysis of the straight-line driving process of the electric four-wheel-drive chassis on hilly terrain in field conditions. The simulation results demonstrate that when the suspension stiffness is 14.4 kN/m and the damping is 900 N·s/m, the chassis achieves optimal vibration reduction and straight-line driving performance. Experimental results based on the simulation findings indicate a high consistency between the simulation and actual models, confirming that optimizing the suspension damping parameters effectively improves chassis smoothness and enhances operational quality. Full article
(This article belongs to the Special Issue Sustainable Technology in Agricultural Engineering)
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19 pages, 32483 KiB  
Article
Design and Simulation Test of the Control System for the Automatic Unloading and Replenishment of Baskets of the 4UM-120D Electric Leafy Vegetable Harvester
by Gongpu Wang, Wenming Chen, Xinhua Wei, Lianglong Hu, Jiwen Peng, Jianning Yuan, Guocheng Bao, Yemeng Wang and Haiyang Shen
Sustainability 2023, 15(18), 13444; https://doi.org/10.3390/su151813444 - 07 Sep 2023
Cited by 2 | Viewed by 884
Abstract
This study designed a control system for the automatic unloading and replenishment of baskets based on the cooperative detection of photoelectric sensors and pressure sensors based on analyzing the structure of the 4UM-120D electric leafy vegetable harvester. The goal of this study was [...] Read more.
This study designed a control system for the automatic unloading and replenishment of baskets based on the cooperative detection of photoelectric sensors and pressure sensors based on analyzing the structure of the 4UM-120D electric leafy vegetable harvester. The goal of this study was to increase the operation efficiency of leafy vegetable harvesters and decrease the work intensity of operators. A control system for the automatic unloading and replenishment of baskets based on the cooperative detection of a photoelectric sensor and pressure sensor was designed and constructed after an analysis of the operating principle and system components of automatic basket unloading and basket replenishment control at the rear of the harvester. The bench test results showed that the bottom photoelectric sensor and top photoelectric sensors 1 and 2 on the touch screen were not lit and the pressure sensor value was displayed as −0.00075531 kg, after pressing the system start button on the touch screen. On the touch screen, only the basket feeding motor was on: the transverse conveyor motor and the basket unloading motor were not, indicating that there was no collection basket on the unloading basket conveyor belt at this time and that the basket feeding motor was conveying an empty basket to the unloading basket conveyor belt. At 26 s, on the touch screen, only the top photoelectric sensor 2 was not on: the top photoelectric sensor 1 and the bottom photoelectric sensor were on and the pressure sensor value was shown as 1.38488 kg. Only the transverse conveyor motor lit up on the touch screen, the basket unloading motor and the basket feeding motor did not light up, indicating that the leafy vegetables temporarily stored in the transverse conveyor belt started to fall into the collection basket at this time and had not yet reached the expected capacity of the collection basket. At 43 s, the bottom photoelectric sensor and top photoelectric sensors 1 and 2 were lit on the touch screen and the pressure sensor value was shown as 2.37229 kg. On the touch screen, only the basket unloading motor lit up: the transverse conveyor motor and the basket feeding motor were not lit up, indicating that the collection basket capacity had reached the expected capacity at this time and the unloading was in progress. At 83 s, the bottom photoelectric sensor and top photoelectric sensors 1 and 2 were not lit on the touch screen and the pressure sensor value was displayed as −0.0040102 kg. On the touch screen, only the basket feeding motor lit up: the transverse conveyor motor and the basket unloading motor did not light up, indicating that the collection basket with the expected capacity had been unloaded to the ground, and the basket feeding motor was transporting empty baskets to the basket unloading conveyor belt. Through bench simulation tests, it was determined that the control system for the automatic unloading and replenishment of baskets based on the cooperative detection control strategy of the photoelectric sensor and pressure sensor reduced the probability of misjudgment and misoperation and improved system performance. This was conducted with the probability of system misjudgment and misoperation serving as the main evaluation index. The simulation results demonstrated that the control system for the automatic unloading and replenishment of baskets based on a photoelectric sensor and pressure sensor cooperative detection control strategy could be error-free judgment and avoid misoperation, effectively improving the stability, accuracy, and rapidity of the system. The study’s findings could suggest a strategy to lessen the workload of operators and increase the operational effectiveness of harvesters for leafy vegetables. Full article
(This article belongs to the Special Issue Sustainable Technology in Agricultural Engineering)
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13 pages, 1613 KiB  
Article
A Sustainable Way to Determine the Water Content in Torreya grandis Kernels Based on Near-Infrared Spectroscopy
by Jiankai Xiang, Yu Huang, Shihao Guan, Yuqian Shang, Liwei Bao, Xiaojie Yan, Muhammad Hassan, Lijun Xu and Chao Zhao
Sustainability 2023, 15(16), 12423; https://doi.org/10.3390/su151612423 - 16 Aug 2023
Cited by 1 | Viewed by 847
Abstract
Water content is an important parameter of Torreya grandis (T. grandis) kernels that affects their quality, processing and storage. The traditional drying method for water content determination is time-consuming and laborious. Water content detection based on modern analytical techniques such as [...] Read more.
Water content is an important parameter of Torreya grandis (T. grandis) kernels that affects their quality, processing and storage. The traditional drying method for water content determination is time-consuming and laborious. Water content detection based on modern analytical techniques such as spectroscopy is accomplished in a fast, accurate, nondestructive, and sustainable way. The aim of this study was to realize the rapid detection of the water content in T. grandis kernels using near-infrared spectroscopy. The water content of T. grandis kernels was measured by the traditional drying method. Meanwhile, the corresponding near-infrared spectra of these samples were collected. A quantitative water content model of T. grandis kernels was established using the full spectrum after 10 outlier samples were removed by the Mahalanobis distance method and concentration residual analysis. The results showed that the prediction model developed from the partial least squares regression (PLS) method after the spectra were pretreated by the standard normal variate transform (SNV) achieved optimal performance. The correlation coefficient of the calibration set (R2c) and the cross-validation set (R2cv) were 0.9879 and 0.9782, respectively, and the root mean square error of the calibration set (RMSEC) and the root mean square error of the cross-validation set (RMSECV) were 0.0029 and 0.0039, respectively. Thus, near-infrared spectroscopy is feasible for the rapid nondestructive detection of the water content in T. grandis seeds. Detecting the water content of agricultural and forestry products in such an environmentally friendly manner is conducive to the sustainable development of agriculture. Full article
(This article belongs to the Special Issue Sustainable Technology in Agricultural Engineering)
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12 pages, 4284 KiB  
Article
Measurement and Analysis of the Influence Factors of Tractor Tire Contact Area Based on a Multiple Linear Regression Equation
by Chunxia Jiang, Zhixiong Lu, Wenbin Dong, Bo Cao and Kyoosik Shin
Sustainability 2023, 15(13), 10017; https://doi.org/10.3390/su151310017 - 24 Jun 2023
Cited by 2 | Viewed by 1470
Abstract
Tractor tire three-dimensional (3D) contact area is one of the significant concerns of the soil-tire coupling mechanism, and it influence soil compaction and the sustainable development of agriculture. In this study, we developed a method to measure the 3D contact area of a [...] Read more.
Tractor tire three-dimensional (3D) contact area is one of the significant concerns of the soil-tire coupling mechanism, and it influence soil compaction and the sustainable development of agriculture. In this study, we developed a method to measure the 3D contact area of a pneumatic tire using a laser profiler on a signal tire soil-bin testing facility. A 6.00-14 bias-ply tire with high lugs was driven on sandy loam soil in a soil-bin testing facility under different vertical loads, driving speeds, and inflation pressures. Then, we developed a multiple linear regression equation between the influence factors and tractor tire contact area. The results indicated that the contact area was impacted by the three factors involved in this study, and the inflation pressure significantly influenced results, and the combination of high speed (3 m/s), low inflation pressure (69 kPa), and high tire load (2.5 kN) led to a relatively high contact area on the soil-tire contact interface and possible severe soil compaction. Also, we found that the contact area varied in a quadratic manner with speed at a given inflation pressure and tire load and varied in a quadratic manner with inflation pressure at a given speed and tire load and varied linearly with the tire load for a given speed and inflation pressure. Full article
(This article belongs to the Special Issue Sustainable Technology in Agricultural Engineering)
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15 pages, 10565 KiB  
Article
Sustainable Improvement of Planting Quality for a Planar 5R Parallel Transplanting Mechanism from the Perspective of Machine and Soil Interaction
by Gaowei Xu, Huimin Fang and Junxiao Liu
Sustainability 2023, 15(12), 9582; https://doi.org/10.3390/su15129582 - 14 Jun 2023
Viewed by 871
Abstract
The poor shape of the cavity formed by the planar 5R parallel transplanting mechanism will cause Salvia miltiorrhiza seedlings to tilt while transplanting them. In order to improve the quality of the cavity in Salvia miltiorrhiza planting, this paper analyzed the structural composition [...] Read more.
The poor shape of the cavity formed by the planar 5R parallel transplanting mechanism will cause Salvia miltiorrhiza seedlings to tilt while transplanting them. In order to improve the quality of the cavity in Salvia miltiorrhiza planting, this paper analyzed the structural composition and working principle of a planar 5R parallel transplanting mechanism for Salvia miltiorrhiza and established the bidirectional coupling model between the transplanting mechanism and the soil. Based on the model, a regression analysis model and the influence of three factors and five levels were obtained by using the experimental optimization design method, which reflected the relationship between the parameters of the mechanism on the parameters of the cavity. In terms of the optimization objective and regression model, the optimal parameter combination of the transplanting mechanism was obtained by multi-objective parameter optimization. A virtual test of cavity formation was conducted on the transplanting mechanism for Salvia miltiorrhiza with an optimal parameter combination. The results proved that the parameters of cavity output via the regression model and the measurement from the bidirectional coupling model were basically consistent, which verifies the accuracy of our parameter optimization for the transplanting mechanism. This paper provides a new approach to the sustainable improvement of a Salvia miltiorrhiza transplanting mechanism from the perspective of the interaction between the machine and the soil. Full article
(This article belongs to the Special Issue Sustainable Technology in Agricultural Engineering)
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16 pages, 7057 KiB  
Article
Vibration Test and Analysis of Crawler Pepper Harvester under Multiple Working Conditions
by Xinzhong Wang, Yuhao Cao, Weiquan Fang and Haoran Sheng
Sustainability 2023, 15(10), 8112; https://doi.org/10.3390/su15108112 - 16 May 2023
Cited by 1 | Viewed by 1184
Abstract
Nowadays, many scholars at home and abroad have studied the vibration of agricultural machinery, especially harvesting machinery. However, this research has lacked the analysis of vibration characteristics of harvesters under the condition of multi-vibration excitation in field work. Therefore, by taking the chassis [...] Read more.
Nowadays, many scholars at home and abroad have studied the vibration of agricultural machinery, especially harvesting machinery. However, this research has lacked the analysis of vibration characteristics of harvesters under the condition of multi-vibration excitation in field work. Therefore, by taking the chassis frame and main vibration sources of a 4JZ-1700 crawler pepper harvester as the research object, this paper aims to investigate the vibration characteristics of the pepper harvester under different working conditions, and the impact of the excitation of various working parts on the chassis frame. Firstly, a modal simulation was carried out with the modal module of ANSYS Workbench to study the natural frequency of the chassis frame. The results demonstrated that the natural frequency of the chassis frame was within 23–76 Hz. A DH5902 dynamic signal acquisition instrument was used to collect vibration signals from seven measuring points under different working conditions of the whole machine, and the collected time domain signals were extracted by Fourier transform. According to the time domain signal, the amplitude at the engine support was the largest under the static no-load condition, and the transmission of engine vibration was attenuated to a certain extent, which imposes a significant effect on the vibration isolation and vibration reduction of the harvester frame. Under the field walking condition, the amplitudes of the left front of the chassis frame and the driving shaft of the cleaning separation device were abnormal, which was mainly attributed to the unequal road surface and the high center of gravity of the cleaning separation device. Through frequency domain analysis, it can be found that the main vibration frequency of most measuring points of the harvester was close to the vibration frequency of the engine under the static no-load condition, and the excitation frequency of most measuring points approximated to the working frequency of the picking drum and the cleaning separation device under the field walking condition. In addition, there were plenty of phenomena in which the main frequency of vibration was detected in the high frequency region above 200 Hz, with messy frequency values. This is due to the poor lubrication of the bearing part of the harvester, causing intense friction between the rotating shaft and the bearing, which also drives the high frequency vibration of the chassis frame. In general, this study can provide a method reference for vibration analysis of agricultural machinery and propose effective measures to reduce vibration based on the conclusions. Full article
(This article belongs to the Special Issue Sustainable Technology in Agricultural Engineering)
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12 pages, 1965 KiB  
Article
Storage Time Detection of Torreya grandis Kernels Using Near Infrared Spectroscopy
by Shihao Guan, Yuqian Shang and Chao Zhao
Sustainability 2023, 15(10), 7757; https://doi.org/10.3390/su15107757 - 09 May 2023
Cited by 3 | Viewed by 975
Abstract
To achieve the rapid identification of Torreya grandis kernels (T. grandis kernels) with different storage times, the near infrared spectra of 300 T. grandis kernels with storage times of 4~9 months were collected. The collected spectral data were modeled, analyzed, and compared [...] Read more.
To achieve the rapid identification of Torreya grandis kernels (T. grandis kernels) with different storage times, the near infrared spectra of 300 T. grandis kernels with storage times of 4~9 months were collected. The collected spectral data were modeled, analyzed, and compared using unsupervised and supervised classification methods to determine the optimal rapid identification model for T. grandis kernels with different storage times. The results indicated that principal component analysis (PCA) after derivative processing enabled the visualization of spectral differences and achieved basic detection of samples with different storage times under unsupervised classification. However, it was unable to differentiate samples with storage times of 4~5 and 8~9 months. For supervised classification, the classification accuracy of support vector machine (SVM) modeling was found to be 97.33%. However, it still could not detect the samples with a storage time of 8~9 months. The classification accuracy of linear discriminant analysis after principal component analysis (PCA-DA) was found to be 99.33%, which enabled the detection of T. grandis kernels with different storage times. This research showed that near-infrared spectroscopy technology could be used to achieve the rapid detection of T. grandis kernels with different storage times. Full article
(This article belongs to the Special Issue Sustainable Technology in Agricultural Engineering)
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16 pages, 4206 KiB  
Article
Locating Tea Bud Keypoints by Keypoint Detection Method Based on Convolutional Neural Network
by Yifan Cheng, Yang Li, Rentian Zhang, Zhiyong Gui, Chunwang Dong and Rong Ma
Sustainability 2023, 15(8), 6898; https://doi.org/10.3390/su15086898 - 19 Apr 2023
Cited by 1 | Viewed by 1320
Abstract
Tea is one of the most consumed beverages in the whole world. Premium tea is a kind of tea with high nutrition, quality, and economic value. This study solves the problem of detecting premium tea buds in automatic plucking by training a modified [...] Read more.
Tea is one of the most consumed beverages in the whole world. Premium tea is a kind of tea with high nutrition, quality, and economic value. This study solves the problem of detecting premium tea buds in automatic plucking by training a modified Mask R-CNN network for tea bud detection in images. A new anchor generation method by adding additional anchors and the CIoU loss function were used in this modified model. In this study, the keypoint detection branch was optimized to locate tea bud keypoints, which, containing a fully convolutional network (FCN), is also built to locate the keypoints of bud objects. The built convolutional neural network was trained through our dataset and obtained an 86.6% precision and 88.3% recall for the bud object detection. The keypoint localization had a precision of 85.9% and a recall of 83.3%. In addition, a dataset for the tea buds and picking points was constructed in study. The experiments show that the developed model can be robust for a range of tea-bud-harvesting scenarios and introduces the possibility and theoretical basis for fully automated tea bud harvesting. Full article
(This article belongs to the Special Issue Sustainable Technology in Agricultural Engineering)
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14 pages, 1022 KiB  
Article
Effects of Ammonification–Steam Explosion Pretreatment on the Production of True Protein from Rice Straw during Solid-State Fermentation
by Bin Li, Chao Zhao, Qian Sun, Kunjie Chen, Xiangjun Zhao, Lijun Xu, Zidong Yang and Hehuan Peng
Sustainability 2023, 15(7), 5964; https://doi.org/10.3390/su15075964 - 29 Mar 2023
Cited by 1 | Viewed by 1057
Abstract
It is difficult to obtain high-protein contents from rice straw using direct fermentation due to its low nitrogen content. This study investigates the effects of ammonification–steam explosion pretreatment of rice straw on the protein content after solid-state fermentation (SSF). The pretreatment is carried [...] Read more.
It is difficult to obtain high-protein contents from rice straw using direct fermentation due to its low nitrogen content. This study investigates the effects of ammonification–steam explosion pretreatment of rice straw on the protein content after solid-state fermentation (SSF). The pretreatment is carried out under multi-strain inoculation conditions. The samples of rice straw after ammonification (TA), steam explosion (TSE), and ammonification and steam explosion (TA-SE) were compared to the control group (TC). The results indicate that both ammonification and steam explosion could disintegrate rice straw’s lignocellulosic structure, releasing nutrients that can be used for microbial reproduction. In addition, amino compounds are formed along with depolymerization products, thus effectively promoting the true protein content. Post-fermentation, total crude protein contents of TA, TSE, and TA-SE samples were 2.56, 1.83, and 4.37 times higher than that of Tc samples, respectively, and true protein contents were 2.52, 1.83, and 5.03 times higher. This study shows that the true protein content by combined ammonification and steam explosion pretreatment of rice straw during 96 h of solid-state fermentation was 46.7% of its total matter, rendering it a suitable alternative to high-protein animal feed. Full article
(This article belongs to the Special Issue Sustainable Technology in Agricultural Engineering)
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15 pages, 5475 KiB  
Article
Image Segmentation of Cucumber Seedlings Based on Genetic Algorithm
by Taotao Xu, Lijian Yao, Lijun Xu, Qinhan Chen and Zidong Yang
Sustainability 2023, 15(4), 3089; https://doi.org/10.3390/su15043089 - 08 Feb 2023
Cited by 4 | Viewed by 1081
Abstract
To solve the problems of the low target-positioning accuracy and weak algorithm robustness of target-dosing robots in greenhouse environments, an image segmentation method for cucumber seedlings based on a genetic algorithm was proposed. Firstly, images of cucumber seedlings in the greenhouse were collected [...] Read more.
To solve the problems of the low target-positioning accuracy and weak algorithm robustness of target-dosing robots in greenhouse environments, an image segmentation method for cucumber seedlings based on a genetic algorithm was proposed. Firstly, images of cucumber seedlings in the greenhouse were collected under different light conditions, and grayscale histograms were used to evaluate the quality of target and background sample images. Secondly, the genetic algorithm was used to determine the optimal coefficient of the graying operator to further expand the difference between the grayscale of the target and background in the grayscale images. Then, the Otsu algorithm was used to perform the fast threshold segmentation of grayscale images to obtain a binary image after coarse segmentation. Finally, morphological processing and noise reduction methods based on area threshold were used to remove the holes and noise from the image, and a binary image with good segmentation was obtained. The proposed method was used to segment 60 sample images, and the experimental results show that under different lighting conditions, the average F1 score of the obtained binary images was over 94.4%, while the average false positive rate remained at about 1.1%, and the image segmentation showed strong robustness. This method can provide new approaches for the accurate identification and positioning of targets as performed by target-dosing robots in a greenhouse environment. Full article
(This article belongs to the Special Issue Sustainable Technology in Agricultural Engineering)
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16 pages, 3424 KiB  
Article
Performance Improvement of a Geared Five-Bar Transplanting Mechanism for Salvia miltiorrhiza by Orthogonal Design Based on an Interactive Human–Computer Auxiliary Interface
by Gaowei Xu, Huimin Fang, Yumin Song, Wensheng Du and Ning Wang
Sustainability 2023, 15(3), 2219; https://doi.org/10.3390/su15032219 - 25 Jan 2023
Cited by 1 | Viewed by 1096
Abstract
A geared five-bar transplanting mechanism can meet the agronomic requirements for the vertical planting of Salvia miltiorrhiza. In order to improve the planting quality, this paper analyzed the structural composition and working principle of a transplanting mechanism and established an interactive human–computer [...] Read more.
A geared five-bar transplanting mechanism can meet the agronomic requirements for the vertical planting of Salvia miltiorrhiza. In order to improve the planting quality, this paper analyzed the structural composition and working principle of a transplanting mechanism and established an interactive human–computer auxiliary interface through a kinematic model. With the aid of an auxiliary interface, by taking the parameters of the transplanting mechanism as the factors and the parameters of the absolute trajectory and posture for the planter as the index, an orthogonal experimental design with five factors and five levels was carried out, and the optimal combination of the parameters of the mechanism was obtained. According to the optimal combination of the parameters of the mechanism, the structure of the transplanting mechanism was designed, a geared five-bar transplanting mechanism for Salvia miltiorrhiza prototype was developed, and a test bench system was built. The actual trajectory of the endpoint for the transplanting mechanism’s prototype was obtained using high-speed photographic technology. The bench test results showed that according to a comparison of the actual trajectory, the posture for the planter and the theoretical analysis results were basically consistent, which verified the correctness, rationality, and consistency of the optimal design for the mechanism. Full article
(This article belongs to the Special Issue Sustainable Technology in Agricultural Engineering)
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14 pages, 3132 KiB  
Article
Establishment of a Model and System for Secondary Fertilization of Nutrient Solution and Residual Liquid
by Xinzhong Wang, Weiquan Fang and Zhongfeng Zhao
Sustainability 2023, 15(3), 1851; https://doi.org/10.3390/su15031851 - 18 Jan 2023
Viewed by 1093
Abstract
At present, the nutrient solution of soilless culture is mostly configured by simply using the standard fertilizer formula, lacking the precise matching technology of nutrient solutions based on nutrient elements. It is unable to change the formula configuration according to vegetable types, different [...] Read more.
At present, the nutrient solution of soilless culture is mostly configured by simply using the standard fertilizer formula, lacking the precise matching technology of nutrient solutions based on nutrient elements. It is unable to change the formula configuration according to vegetable types, different growth stages and growth needs, especially in the secondary fertilizer reuse of nutrient solution reflux. In order to make precise secondary fertilization, a model and system for secondary fertilization of nutrient solution residual liquid were established in this paper. It can be used for secondary fertilization based on nutrient ions and reused after the sterilization of the residual liquid. A nutrient solution fertilizer system based on nutrient elements was designed. The nutrient solution fertilizer system based on the online detection of ions was determined with different element compounds as the fertilizer unit. Combined with the existing hydroponic water-soluble inorganic salts, the ion concentration and its proportioning quantitative model of the nutrient solution recovery solution were established. The experimental verification and result analysis of the fertilizer model were carried out to test the accuracy and practicability of the established model. The ion concentration error obtained from the mathematical model was established as 0.0093–0.5294 mg·L−1.The precise proportioning technology of nutrient solution based on nutrient elements can realize the precise and intelligent proportioning of nutrient elements in the nutrient solution of crops and can also make full use of the nutrient solution. It also improves the efficiency of greenhouse cultivation. Full article
(This article belongs to the Special Issue Sustainable Technology in Agricultural Engineering)
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13 pages, 2261 KiB  
Article
Study of Mechanical-Chemical Synergistic Weeding on Characterization of Weed–Soil Complex and Weed Control Efficacy
by Huimin Fang, Gaowei Xu, Xinyu Xue, Mengmeng Niu and Lu Qiao
Sustainability 2023, 15(1), 665; https://doi.org/10.3390/su15010665 - 30 Dec 2022
Cited by 2 | Viewed by 1362
Abstract
Mechanical-chemical synergy has been proven efficient in weed control. However, characterizing the state of the weed–soil complex after mechanical weeding and revealing its effects on subsequent herbicide application is still challenging, which restricts the implementation of this technology. This paper first presents a [...] Read more.
Mechanical-chemical synergy has been proven efficient in weed control. However, characterizing the state of the weed–soil complex after mechanical weeding and revealing its effects on subsequent herbicide application is still challenging, which restricts the implementation of this technology. This paper first presents a method to characterize the state of the weed–soil complex from the perspectives of the fragmentation and composite characteristics. The regrowth of the weed–soil complex and the effects of complemented herbicide-reduced spraying on weed control efficacy and crop yield were then investigated. The results showed that the typical diameters of the weed–soil complexes were 10.67 cm and 2.82 cm after inter-row hoe shovel and intra-row finger weeding, respectively. There were mainly two and four weed–soil complex states after inter-row and intra-row weeding, respectively. The regrowth rate corresponding to the weed–soil complex state with the largest component proportion after inter-row and intra-row weeding was 76.91% and 18.37%, respectively. The additional chemical herbicide sprayed on the weed–soil complex significantly improved the fresh weight control efficacy of 95.12% for the preposed inter-row mechanical weeding and 138.07% for the preposed intra-row mechanical weeding in the maize silking stage. The maize yield of inter-row mechanical–75% chemical application treatment was 9.27% higher than that of chemical treatment. Mechanical weeding creates a suitable weed–soil complex state for subsequent chemical application and improves the synergistic weeding effect. Full article
(This article belongs to the Special Issue Sustainable Technology in Agricultural Engineering)
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