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Agriculture
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Applications of Electric, Electronics, and Computer Science to Agricultural Machines
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Applications of Electric, Electronics, and Computer Science to Agricultural Machines

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

Deadline for manuscript submissions: closed (20 November 2021) | Viewed by 49676

Special Issue Editors

Prof. Dr. Michele Mattetti

E-Mail Website
Guest Editor
Department of Agricultural and Food Sciences, University of Bologna, Viale G Fanin 50, I-40127 Bologna, Italy
Interests: agricultural digitalisation; agricultural machines; data analysis; telemetry systems in agriculture
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Luigi Alberti

E-Mail Website
Guest Editor
Department of Industrial Engineering, Università di Padova, Padova, Italy
Interests: vehicle electrification; sustainable transportation; green energy conversion; variable speed electric drives
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the last decade, the number of sensors, actuators, and ECUs in agricultural machines has increased, and this trend is expected to be even more pronounced in the coming years. This will present an opportunity for smart, more sustainable, and more productive agricultural machines. The advantages of electronic systems in agricultural machines are manifold; these systems allow for higher precision of farming systems, higher power density, higher productivity, higher comfort, lower operating costs, new features, and the digitalisation of agricultural operations. Despite the numerous opportunities of such advanced machines, the market is not ready, and studies are required to propose solutions that are able to meet farmers’ expectations.

The Special Issue covers current trends and future developments of electric and electronic algorithms in agricultural machines, including both mechanical as well as electrical engineering aspects.

Prof. Dr. Michele Mattetti
Prof. Dr. Luigi Alberti
Guest Editors

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. Agriculture is an international peer-reviewed open access monthly 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 2600 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

  • Electrification
  • Data-driven design
  • CANBUS
  • ISOBUS
  • Artificial intelligence
  • Hybrid vehicles
  • Engine emissions
  • Digitalization
  • ICT
  • Sustainability

Published Papers (10 papers)

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Research

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13 pages, 4236 KiB  
Article
Research on Dynamic Load Characteristics of Advanced Variable Speed Drive System for Agricultural Machinery during Engagement
by Zhun Cheng and Zhixiong Lu
Agriculture 2022, 12(2), 161; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture12020161 - 24 Jan 2022
Cited by 11 | Viewed by 2400
Abstract
A wet clutch is the key component to realize power uninterrupted in agricultural machinery operation. To reduce impact of the system and improve engagement quality, this paper studies and establishes the dynamic load characteristics model of a wet clutch and analyzes three kinds [...] Read more.
A wet clutch is the key component to realize power uninterrupted in agricultural machinery operation. To reduce impact of the system and improve engagement quality, this paper studies and establishes the dynamic load characteristics model of a wet clutch and analyzes three kinds of tractor working conditions. This paper proposes and adopts the method of combining ‘PLS analysis-Improved SA—Comparison of various models-Actual test data’. The results show that with the limit of 100 Nm, the relationship between dynamic load characteristics and oil pressure is opposite. Load is highly inversely correlated with dynamic load, and it has enough precision to build a power curve model only by load (MAPE is 4.5929%). Take a certain type of tractor for example, oil pressure should be maintained at a low level, plowing resistance should be greater than 1600 N and the mass of transportation should avoid 600~1800 kg. This study provides a direct basis for the control, design and performance improvement of agricultural machinery. Full article
(This article belongs to the Special Issue Applications of Electric, Electronics, and Computer Science to Agricultural Machines)
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17 pages, 7868 KiB  
Article
Travel Reduction Control of Distributed Drive Electric Agricultural Vehicles Based on Multi-Information Fusion
by Chenyang Sun, Pengfei Sun, Jun Zhou and Jiawen Mao
Agriculture 2022, 12(1), 70; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture12010070 - 06 Jan 2022
Cited by 3 | Viewed by 1491
Abstract
In agricultural vehicles with internal combustion engines, owing to the use of rear-wheel drive or four-wheel drive, it is difficult to obtain information regarding the slip of the driving wheels. Excessive wheel slip, an inevitable phenomenon occurring during agricultural activities, can easily damage [...] Read more.
In agricultural vehicles with internal combustion engines, owing to the use of rear-wheel drive or four-wheel drive, it is difficult to obtain information regarding the slip of the driving wheels. Excessive wheel slip, an inevitable phenomenon occurring during agricultural activities, can easily damage the original soil surface and result in excessive energy consumption. To solve these problems, a distributed drive agricultural vehicle (DDAV) based on multi-information fusion was proposed. The actual travel reduction of each wheel was calculated by determining the vehicle parameters in order to deliver the required torque to the four drive wheels via sliding mode control (SMC) and incremental proportional-integral (PI) control. Through this process, the vehicle always operates in a straight line. Test results show that, on a uniform surface, the travel reduction of each wheel can be maintained at the target value by using the incremental PI control strategy, with only minor fluctuations, to make the vehicle run in a straight line (R2 = 0.9999). Furthermore, on a separated surface, the travel reduction of each wheel can be maintained at the target value, and using the SMC strategy enables more identical coefficient of gross tractions for each wheel to make the vehicle run in a straight line (R2 = 0.9902). Unlike the non-control strategy, the vehicle reaches a stable state within 1 s, owing to the use of a controller that can effectively reduce the impact of road changes on vehicle velocity. This study can provide a reference for the drive control of DDAVs. Full article
(This article belongs to the Special Issue Applications of Electric, Electronics, and Computer Science to Agricultural Machines)
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16 pages, 4379 KiB  
Article
Development of a Depth Control System Based on Variable-Gain Single-Neuron PID for Rotary Burying of Stubbles
by Mingkuan Zhou, Junfang Xia, Shuai Zhang, Mengjie Hu, Zhengyuan Liu, Guoyang Liu and Chengming Luo
Agriculture 2022, 12(1), 30; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture12010030 - 28 Dec 2021
Cited by 7 | Viewed by 1734
Abstract
Rotary burying by tractor-hitched rotary tillers is a common practice in southern China for treating rice stubbles. Currently, it is difficult to maintain stable tillage depths due to surface unevenness and the residual stubbles in the field, which leads to unstable tillage quality [...] Read more.
Rotary burying by tractor-hitched rotary tillers is a common practice in southern China for treating rice stubbles. Currently, it is difficult to maintain stable tillage depths due to surface unevenness and the residual stubbles in the field, which leads to unstable tillage quality and nonuniform crop growth in later stages. In this study, an RTK-GNSS was used to measure the real-time height and roll angle of the tractor, and a variable-gain single-neuron PID control algorithm was designed to adjust the coefficients (KP, KI, and KD) and gain K in real-time according to the control effects. An on-board computer sent the angles of the upper swing arm u(t) to an STM32 microcontroller through a CAN bus. Compared with the current angle of the upper swing arm, the microcontroller controlled an electronic-control proportional hydraulic system, so that the height of the rotary tiller could be adjusted to follow the field undulations in real-time. Field experiments showed that when the operation speed of the tractor-rotary tiller system was about 0.61 m/s, the variable-gain single-neuron PID algorithm could effectively improve the stability of the working depth and the stubbles’ burying rate. Compared with a conventional PID controller, the stability coefficient and the stubbles’ burying rate were improved by 5.85% and 4.38%, respectively, and compared with a single-neuron PID controller, the stability coefficient and the stubbles’ burying rate were improved by 4.37% and 3.49%, respectively. This work controlled the working depth of the rotary tiller following the changes in the field surface in real-time and improved the stubbles’ burying rate, which is suitable for the unmanned operation of the rotary burying of stubbles in the future. Full article
(This article belongs to the Special Issue Applications of Electric, Electronics, and Computer Science to Agricultural Machines)
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18 pages, 3763 KiB  
Article
Research on Load Disturbance Based Variable Speed PID Control and a Novel Denoising Method Based Effect Evaluation of HST for Agricultural Machinery
by Zhun Cheng and Zhixiong Lu
Agriculture 2021, 11(10), 960; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture11100960 - 02 Oct 2021
Cited by 18 | Viewed by 1982
Abstract
This paper aims to realize and improve the constant speed control performance of tractors with HST (Hydrostatic Transmission) variable speed units. To achieve this, based on the HST test bench of the tractor, we perform a verification test of the adjustable speed characteristics, [...] Read more.
This paper aims to realize and improve the constant speed control performance of tractors with HST (Hydrostatic Transmission) variable speed units. To achieve this, based on the HST test bench of the tractor, we perform a verification test of the adjustable speed characteristics, a denoising filter test of the response signal, a test on the influence of the load disturbance on the adjustable speed characteristics and a PID-based constant speed performance detection test. The results of the verification test of the adjustable speed characteristics show that the theoretical value and actual value of the adjustable speed transmission characteristics of the HST used are essentially consistent with each other. The results of the test of the load disturbance’s influence on the adjustable speed characteristics show that the increase in load torque inhibits the HST output response. Therefore, the paper proposes and designs a PID-based closed-loop constant speed control system. The paper uses a step response test and a load disturbance test to research the control result of the constant speed system. Collecting and analyzing all test results, we find that the constant speed control based on PID has a very good result. The average error between the average HST output speed and the target speed set was 0.37%, and the average standard deviation of output speed was 1.18 rpm. In addition, the paper proposes a denoising method combing the empirical mode decomposition method and the Gaussian distribution determination. The method shows that the first two orders of the components of the HST response signal should be removed as noise. The paper uses the denoised signal and the partial least squares method to analyze the influencing factors of the constant speed control result. The analysis results show that the rate of change of load torque has the biggest influence on the stability of HST output speed, followed by the target value. Full article
(This article belongs to the Special Issue Applications of Electric, Electronics, and Computer Science to Agricultural Machines)
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17 pages, 3288 KiB  
Article
A Grain Yield Sensor for Yield Mapping with Local Rice Combine Harvester
by Chaiyan Sirikun, Grianggai Samseemoung, Peeyush Soni, Jaturong Langkapin and Jakkree Srinonchat
Agriculture 2021, 11(9), 897; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture11090897 - 18 Sep 2021
Cited by 8 | Viewed by 3281
Abstract
Rice grain yield was estimated from a locally made Thai combine harvester using a specially developed sensing and monitoring system. The yield monitoring and sensing system, mounted on the rice combine harvester, collected and logged grain mass flow rate and moisture content, as [...] Read more.
Rice grain yield was estimated from a locally made Thai combine harvester using a specially developed sensing and monitoring system. The yield monitoring and sensing system, mounted on the rice combine harvester, collected and logged grain mass flow rate and moisture content, as well as pertinent information related to field, position and navigation. The developed system comprised a yield meter, GNSS receiver and a computer installed with customized software, which, when assembled on a local rice combine, mapped real-time rice yield along with grain moisture content. The performance of the developed system was evaluated at three neighboring (identically managed) rice fields. ArcGIS® software was used to create grain yield map with geographical information of the fields. The average grain yield values recorded were 3.63, 3.84 and 3.60 t ha−1, and grain moisture contents (w.b.) were 22.42%, 23.50% and 24.71% from the three fields, respectively. Overall average grain yield was 3.84 t ha−1 (CV = 63.68%) with 578.10 and 7761.58 kg ha−1 as the minimum and maximum values, respectively. The coefficients of variation in grain yield of the three fields were 57.44%, 63.68% and 60.41%, respectively. The system performance was evaluated at four different cutter bar heights (0.18, 0.25, 0.35 and 0.40 m) during the test. As expected, the tallest cutter bar height (0.40 m) offered the least error of 12.50% in yield estimation. The results confirmed that the developed grain yield sensor could be successfully used with the local rice combine harvester; hence, offers and ‘up-gradation’ potential in Thai agricultural mechanization. Full article
(This article belongs to the Special Issue Applications of Electric, Electronics, and Computer Science to Agricultural Machines)
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15 pages, 1539 KiB  
Article
Research on Innovative Business Plan. Smart Cattle Farming Using Artificial Intelligent Robotic Process Automation
by Diana Elena Micle, Florina Deiac, Alexandru Olar, Raul Florentin Drența, Cristian Florean, Ionuț Grigore Coman and Felix Horațiu Arion
Agriculture 2021, 11(5), 430; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture11050430 - 10 May 2021
Cited by 13 | Viewed by 7345
Abstract
Integrating livestock management with the required devices and sensors is now seen as a critical factor in the agricultural sector’s long-term success. The findings revealed that the agricultural business sector is open to implementing Information and Communication Technology (ICT) solutions, so the aim [...] Read more.
Integrating livestock management with the required devices and sensors is now seen as a critical factor in the agricultural sector’s long-term success. The findings revealed that the agricultural business sector is open to implementing Information and Communication Technology (ICT) solutions, so the aim of this paper is to determine how advantageous it is for Romanian farmers to invest in a project that employs smart cattle farming methods that incorporate Artificial Intelligence (AI), Robotic Process Automation (RPA) and the Internet of Things (IOT). An unstructured interview was used to gather empirical evidence during a focus group meeting. Analyzing the selected primary performance metrics, it was projected that the farm’s profitability would increase by 19 percent, productivity would increase by 21 percent, and the farm’s environmental impact would decrease by 22 percent. Automation and remote work would help minimize the farm’s worker burden while also making control panels, decision-making files, and data analysis more available. In order for the domain to be as prosperous as possible, farmers must be made aware of the benefits of using these emerging technologies for closing the gap between farmers and Information Technology (IT) solution providers, and this can be accomplished through continuous training for both farmers and their technology vendors. Full article
(This article belongs to the Special Issue Applications of Electric, Electronics, and Computer Science to Agricultural Machines)
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20 pages, 1577 KiB  
Article
Application of Machine Learning Algorithms to Predict Body Condition Score from Liveweight Records of Mature Romney Ewes
by Jimmy Semakula, Rene A. Corner-Thomas, Stephen T. Morris, Hugh T. Blair and Paul R. Kenyon
Agriculture 2021, 11(2), 162; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture11020162 - 17 Feb 2021
Cited by 3 | Viewed by 2851
Abstract
Body condition score (BCS) in sheep (Ovis aries) is a widely used subjective measure of the degree of soft tissue coverage. Body condition score and liveweight are statistically related in ewes; therefore, it was hypothesized that BCS could be accurately predicted [...] Read more.
Body condition score (BCS) in sheep (Ovis aries) is a widely used subjective measure of the degree of soft tissue coverage. Body condition score and liveweight are statistically related in ewes; therefore, it was hypothesized that BCS could be accurately predicted from liveweight using machine learning models. Individual ewe liveweight and body condition score data at each stage of the annual cycle (pre-breeding, pregnancy diagnosis, pre-lambing and weaning) at 43 to 54 months of age were used. Nine machine learning (ML) algorithms (ordinal logistic regression, multinomial regression, linear discriminant analysis, classification and regression tree, random forest, k-nearest neighbors, support vector machine, neural networks and gradient boosting decision trees) were applied to predict BCS from a ewe’s current and previous liveweight record. A three class BCS (1.0–2.0, 2.5–3.5, >3.5) scale was used due to high-class imbalance in the five-scale BCS data. The results showed that using ML to predict ewe BCS at 43 to 54 months of age from current and previous liveweight could be achieved with high accuracy (>85%) across all stages of the annual cycle. The gradient boosting decision tree algorithm (XGB) was the most efficient for BCS prediction regardless of season. All models had balanced specificity and sensitivity. The findings suggest that there is potential for predicting ewe BCS from liveweight using classification machine learning algorithms. Full article
(This article belongs to the Special Issue Applications of Electric, Electronics, and Computer Science to Agricultural Machines)
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18 pages, 4260 KiB  
Article
Sustainable Agriculture: Stable Robust Control in Presence of Uncertainties for Multi-Functional Indoor Transportation of Farm Products
by Ha Quang Thinh Ngo, Van Ngoc Son Huynh, Thanh Phuong Nguyen and Hung Nguyen
Agriculture 2020, 10(11), 523; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture10110523 - 04 Nov 2020
Cited by 3 | Viewed by 1847
Abstract
Currently, integrated trends play a key role in every aspect of automation applications. In particular, if the mechanization of agriculture becomes a competitive factor among farmers or nations, then the multi-functional transportation of agricultural products is inevitable in global trade. In sustainable transportation, [...] Read more.
Currently, integrated trends play a key role in every aspect of automation applications. In particular, if the mechanization of agriculture becomes a competitive factor among farmers or nations, then the multi-functional transportation of agricultural products is inevitable in global trade. In sustainable transportation, the challenge of overcoming stable control in harsh environments, such as through imprecise parameters or varying loads, should be addressed. In this paper, a novel controller for a nonholonomic mechanical system able to adapt to uncertainties is proposed. Based on the multi-functional autonomous carrier (MAC), the system configuration of the kinematic and dynamic model is launched in order to identify the unstable problems that arise when tracking the trajectory. To solve these troubles, the decoupled formation of a MAC system has been investigated by considering two second-order components, namely a linear speed-based sub-system and angular speed-based sub-system. To stabilize the whole system using the Lyapunov theory, the advanced control techniques are studied. To validate the proposed approach, a series of test scenarios have been carried out. From the superior performance of numerous trials, it is clear that our approach is effective, feasible, and reasonable for the advanced control of agricultural applications. Full article
(This article belongs to the Special Issue Applications of Electric, Electronics, and Computer Science to Agricultural Machines)
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13 pages, 2015 KiB  
Article
Soybean Yield Estimation and Its Components: A Linear Regression Approach
by Marcelo Chan Fu Wei and José Paulo Molin
Agriculture 2020, 10(8), 348; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture10080348 - 11 Aug 2020
Cited by 20 | Viewed by 5559
Abstract
Soybean yield estimation is either based on yield monitors or agro-meteorological and satellite imagery data, but they present several limiting factors regarding on-farm decision level. Aware that machine learning approaches have been largely applied to estimate soybean yield and the availability of data [...] Read more.
Soybean yield estimation is either based on yield monitors or agro-meteorological and satellite imagery data, but they present several limiting factors regarding on-farm decision level. Aware that machine learning approaches have been largely applied to estimate soybean yield and the availability of data regarding soybean yield and its components (number of grains (NG) and thousand grains weight (TGW)), there is an opportunity to study their relationships. The objective was to explore the relationships between soybean yield and its components, generate equations to estimate yield and evaluate its prediction accuracy. The training dataset was composed of soybean yield and its components’ data from 2010 to 2019. Linear regression models based on NG, TGW and yield were fitted on the training dataset and applied to a validation dataset composed of 58 on-field collected samples. It was found that globally TGW and NG presented weak (r = 0.50) and strong (r = 0.92) linear relationships with yield, respectively. In addition to that, applying the fitted models to the validation dataset, model based on NG presented the highest accuracy, coefficient of determination (R2) of 0.70, mean absolute error (MAE) of 639.99 kg ha−1 and root mean squared error (RMSE) of 726.67 kg ha−1. Full article
(This article belongs to the Special Issue Applications of Electric, Electronics, and Computer Science to Agricultural Machines)
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Review

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25 pages, 3095 KiB  
Review
Agriculture-Food Supply Chain Management Based on Blockchain and IoT: A Narrative on Enterprise Blockchain Interoperability
by Showkat Ahmad Bhat, Nen-Fu Huang, Ishfaq Bashir Sofi and Muhammad Sultan
Agriculture 2022, 12(1), 40; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture12010040 - 30 Dec 2021
Cited by 104 | Viewed by 17771
Abstract
Modern-day agriculture supply chains have evolved from sovereign and autonomous local stakeholders to a worldwide interconnected system of multiple participants linked by complicated interactions, impacting the production, processing, transportation, and delivery of food to end consumers. Regular instances of fraudulent acts reveal a [...] Read more.
Modern-day agriculture supply chains have evolved from sovereign and autonomous local stakeholders to a worldwide interconnected system of multiple participants linked by complicated interactions, impacting the production, processing, transportation, and delivery of food to end consumers. Regular instances of fraudulent acts reveal a lack of openness in agriculture supply chains, raising worries about financial losses, eroding customer trust, and lowering corporate brand value. To develop an efficient and reliable trading environment, several fundamental modifications in the present supply chain architecture are required. There is broad consensus that blockchain can improve transparency in agriculture-food supply chains (agri-food SCs). Consumers now demand safe, sustainable, and equitable food production processes, and businesses are using blockchains and the internet of things to meet these needs. For enhanced responsiveness in agri-food SCs, new concepts have evolved that combine blockchains with various Industry 5.0 technologies (e.g., blockchain technology, big data, internet of things (IoT), radio frequency identification (RFID), near field communication (NFC), etc.). It is critical to cut through the hype and examine the technology’s limits, which might stymie its acceptance, implementation, and scalability in agri-food supply chains. This study presents Agri-SCM-BIoT (Agriculture Supply Chain Management using Blockchain and Internet of things) architecture to address the storage and scalability optimization, interoperability, security and privacy issues security, and privacy of personal data along with storage concerns with present single-chain agriculture supply chain systems. We also discussed the classification of security threats with IoT infrastructure and possible available blockchain-based defense mechanisms. Finally, we discussed the features of the proposed supply chain architecture, followed by a conclusion and future work. Full article
(This article belongs to the Special Issue Applications of Electric, Electronics, and Computer Science to Agricultural Machines)
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