Special Issue "Applied Agri-Technologies"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Environmental and Sustainable Science and Technology".

Deadline for manuscript submissions: 31 May 2021.

Special Issue Editors

Dr. Dimitrios Kateris
E-Mail Website
Guest Editor
Institute for Bio-economy and Agri-technology (iBO), Centre for Research & Technology Hellas (CERTH), Thessaloniki, Greece
Interests: agricultural engineering, agri-robotics, operations management, agricultural automations, condition monitoring, artificial intelligence
Special Issues and Collections in MDPI journals
Prof. Dionysis Bochtis
E-Mail Website
Guest Editor
Institute for Bio-economy and Agri-Technology (IBO), Centre for Research and Technology – Hellas (CERTH), 38333 Volos, Greece
Interests: operations management; supply chain automation; agri-business; ICT-agri; bioeconomy
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Agriculture plays a vital role in the global economy, with the majority of the rural population in developing countries depending on it. The depletion of natural resources makes the improvement of agricultural production more important but also more difficult than ever. In this context, effective interventions in agriculture are essential for the fulfilment of its vital role, which is to maintain rural social and economic stability within sustainable development. The extensive use of applied agri-technologies results in productivity improvement and better resource use and reduces the time needed for farm management, marketing, logistics, and quality assurance.
The purpose of this Special Issue is to publish research papers, as well as review articles, addressing recent advances on systems and processes in the field of applied agri-technologies. Original, high-quality contributions that have not yet been published and that are not currently under review by other journals or peer-reviewed conferences are sought. Indicatively, research topics include:

  • Robotics and automation in agriculture;
  • Machine-embedded ICT tools,
  • Internet of Things (IoT) in agri-food production and agri-food chains;
  • Remote sensing and GIS applications;
  • AI applications in agriculture;
  • Decision support systems for agriculture;
  • ICT applications for precision farming;
  • Traceability and agri-food chains systems;
  • Big data and data mining for agricultural information systems;
  • Sustainability aspects (environmental-social) of new technologies in agriculture.  

Dr. Dimitrios Kateris
Prof. Dr. Dionysis Bochtis
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 papers will be 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. Applied Sciences 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 2000 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

  • Agri-robotics
  • Traceability
  • Farm management information system (FMIS)
  • Precision farming
  • Agri-technologies sustainability
  • ICT applications
  • Agricultural logistics
  • Internet of Things (IoT)
  • GIS applications
  • Remote sensing

Published Papers (20 papers)

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Research

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Open AccessArticle
Design and Experimental Evaluation of a Form Trimming Machine for Horticultural Plants
Appl. Sci. 2021, 11(5), 2230; https://doi.org/10.3390/app11052230 - 03 Mar 2021
Viewed by 244
Abstract
Form trimming is an important practice in horticulture. Currently, handheld trimming tools are the most commonly used in China, which presents certain disadvantages including high human labor input, low productivity and inconsistent performance. In this work, a wheeled form trimming machine was designed [...] Read more.
Form trimming is an important practice in horticulture. Currently, handheld trimming tools are the most commonly used in China, which presents certain disadvantages including high human labor input, low productivity and inconsistent performance. In this work, a wheeled form trimming machine was designed for shrub plants with the aim of reducing labor input, increasing efficiency and improving trimming performance. The machine was mainly composed of three parts: a supporting frame, a rotary base and a knife system. The design and construction of the key components of the machine were introduced. The knife system was a combination of multiple cutter units with reciprocating motions. The number of units and their connecting angles could be adjusted to realize different trimming shapes. The knife system was carried by the rotary base and could realize 360° rotations to cut the plants into a desired form. Experiments were performed to determine the optimal working parameters (cutting frequency of the cutter unit and rotating speed of the rotary base). The similarity between the plant profile after trimming and the profile of the knife system and the consumed time in each operation were chosen as two evaluation indexes. Results showed that when the cutting frequency was 16.7 Hz and the rotating speed of the rotary base was 13.5 r/min, the trimming operation could be completed by two circles, and the time consumption was 8.89 s. Furthermore, to test the adaptability of the machine, five different shrub plants were chosen and trimmed by the machine, and results showed that the overall similarity was above 93%. Therefore, the form trimming machine developed could meet the requirements of shrub trimming in horticulture with desirable precision and adaptability. Full article
(This article belongs to the Special Issue Applied Agri-Technologies)
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Open AccessArticle
Human Activity Recognition through Recurrent Neural Networks for Human–Robot Interaction in Agriculture
Appl. Sci. 2021, 11(5), 2188; https://doi.org/10.3390/app11052188 - 02 Mar 2021
Viewed by 336
Abstract
The present study deals with human awareness, which is a very important aspect of human–robot interaction. This feature is particularly essential in agricultural environments, owing to the information-rich setup that they provide. The objective of this investigation was to recognize human activities associated [...] Read more.
The present study deals with human awareness, which is a very important aspect of human–robot interaction. This feature is particularly essential in agricultural environments, owing to the information-rich setup that they provide. The objective of this investigation was to recognize human activities associated with an envisioned synergistic task. In order to attain this goal, a data collection field experiment was designed that derived data from twenty healthy participants using five wearable sensors (embedded with tri-axial accelerometers, gyroscopes, and magnetometers) attached to them. The above task involved several sub-activities, which were carried out by agricultural workers in real field conditions, concerning load lifting and carrying. Subsequently, the obtained signals from on-body sensors were processed for noise-removal purposes and fed into a Long Short-Term Memory neural network, which is widely used in deep learning for feature recognition in time-dependent data sequences. The proposed methodology demonstrated considerable efficacy in predicting the defined sub-activities with an average accuracy of 85.6%. Moreover, the trained model properly classified the defined sub-activities in a range of 74.1–90.4% for precision and 71.0–96.9% for recall. It can be inferred that the combination of all sensors can achieve the highest accuracy in human activity recognition, as concluded from a comparative analysis for each sensor’s impact on the model’s performance. These results confirm the applicability of the proposed methodology for human awareness purposes in agricultural environments, while the dataset was made publicly available for future research. Full article
(This article belongs to the Special Issue Applied Agri-Technologies)
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Open AccessArticle
Why Farmers Get Involved in Participatory Research Projects? The Case of Arable Crops Farmers in Greece
Appl. Sci. 2021, 11(1), 6; https://doi.org/10.3390/app11010006 - 22 Dec 2020
Viewed by 530
Abstract
This paper seeks to underline the driving factors of farmers’ engagement in Participatory Research Projects (PRPs). This is a critical issue for formulating efficient and effective technology transfer channels, essential for improving the operational status of agricultural holdings. A survey was conducted on [...] Read more.
This paper seeks to underline the driving factors of farmers’ engagement in Participatory Research Projects (PRPs). This is a critical issue for formulating efficient and effective technology transfer channels, essential for improving the operational status of agricultural holdings. A survey was conducted on a sample of 326 Greek arable crops farmers. An explanatory framework consisting of three major factor categories and 11 variables was developed. A logistic regression analysis empirically tests the effect of the variables on the participation of farmers in PRP. Furthermore, the relative importance of variables and factors is extracted with the Shapley–Owen decomposition analysis. The results show that Farmers’ Willingness and Social Influences are the factors that mostly affect their decision to engage in a PRP. The farmers’ ability consisting of socioeconomic and demographic variables has a small effect on their decision-making process. The estimated effects can help decision-makers to shape and prioritize more targeted policies for farmers’ engagement in research. Additionally, this paper sets the basis for shifting research from simple estimations of the effect of variables on farmers’ decision-making, to a more comprehensive estimation that also accounts for the strength of these relationships. The paper fills a gap in the literature of studies on farmers’ decisions for participating in PRPs, by developing and testing an explanatory framework which also accounts for the relative importance of each factor/variable. Full article
(This article belongs to the Special Issue Applied Agri-Technologies)
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Open AccessArticle
Photoconversion Fluoropolymer Films for the Cultivation of Agricultural Plants Under Conditions of Insufficient Insolation
Appl. Sci. 2020, 10(22), 8025; https://doi.org/10.3390/app10228025 - 12 Nov 2020
Viewed by 347
Abstract
Plants are capable of using mainly the quanta of the red and blue parts of a spectrum for the reception of energy during photosynthesis. However, for many crops grown indoors in high latitudes or under conditions of insufficient insolation, the average daily intensity [...] Read more.
Plants are capable of using mainly the quanta of the red and blue parts of a spectrum for the reception of energy during photosynthesis. However, for many crops grown indoors in high latitudes or under conditions of insufficient insolation, the average daily intensity of the red and blue parts of the spectrum is usually sufficient only on clear summer days. A technology has been proposed to produce a photoconversion fluoropolymer film for greenhouses, which is based on the modification of fluoropolymer by nanoparticles with fluorescence in the blue or red part of the spectrum (quantum dots). The films are capable of converting UV and violet radiation into the blue and red region of the visible spectrum, the most important for plants. It has been shown that the use of photoconversion fluoropolymer films promotes biomass growth. The area of cucumber leaves grown under photoconversion films increases by 20%, pumpkins by 25%, pepper by 30%, and tomatoes by 55%. The use of photoconversion fluoropolymer films for greenhouses also allows obtaining 15% more fruit biomass from one bush. In general, the use of photoconversion fluoropolymer films may be in great demand for greenhouses lying in high latitudes and located in areas with insufficient insolation. Full article
(This article belongs to the Special Issue Applied Agri-Technologies)
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Open AccessArticle
In Vitro Assessment of Kurdish Rice Genotypes in Response to PEG-Induced Drought Stress
Appl. Sci. 2020, 10(13), 4471; https://doi.org/10.3390/app10134471 - 28 Jun 2020
Viewed by 541
Abstract
Rice (Oryza sativa L.) is productively affected by different environmental factors, including biotic and abiotic stress. The objectives of this research were to evaluate the genetic distinction among Kurdish rice genotypes using the simple sequence repeats (SSRs) molecular markers and to perform [...] Read more.
Rice (Oryza sativa L.) is productively affected by different environmental factors, including biotic and abiotic stress. The objectives of this research were to evaluate the genetic distinction among Kurdish rice genotypes using the simple sequence repeats (SSRs) molecular markers and to perform in vitro tests to characterize the drought tolerance of six local rice genotypes. The polymorphic information content (PIC) varied from 0.38 to 0.84 with an average of 0.56. The genetic distance ranged from 0.33 to 0.88. Drought stress had a significant impact (p ≤ 0.05) on callus growth parameters. Enzymatic antioxidant systems were predicted and exhibited a significant variation. The findings revealed that proline levels increase in proportion to polyethylene glycol (PEG) concentrations. Kalar and Gwll Swr genotypes showed the worst performances in phenotypic and biochemical traits, while Choman and Shawre exhibited the best phenotypic and biochemical performances. A positive and substantial relationship between callus fresh weight (CFW) and callus dry weight (CDW) was found under stressful and optimized conditions. Callus induction (CI) was positively and significantly associated with the catalase activity (CAT) in all stressed treatments. Based on the results for callus growth and the biochemical parameters under stress conditions, a remarkable genotype distinction, based on the tolerance reaction, was noted as follows: PEG resistant > susceptible, Choman > Shawre > White Bazyan > Red Bazyan > Gwll Swr > Kalar. The CI and CAT characteristics were considered as reliable predictors of drought tolerance in rice genotypes. Full article
(This article belongs to the Special Issue Applied Agri-Technologies)
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Open AccessArticle
Development of a Prediction Model for Tractor Axle Torque during Tillage Operation
Appl. Sci. 2020, 10(12), 4195; https://doi.org/10.3390/app10124195 - 18 Jun 2020
Cited by 4 | Viewed by 610
Abstract
In general, the tractor axle torque is used as an indicator for making various decisions when engineers perform transmission fatigue life analysis, optimal design, and accelerated life testing. Since the existing axle torque measurement method requires an expensive torque sensor, an alternative method [...] Read more.
In general, the tractor axle torque is used as an indicator for making various decisions when engineers perform transmission fatigue life analysis, optimal design, and accelerated life testing. Since the existing axle torque measurement method requires an expensive torque sensor, an alternative method is required. Therefore, the aim of this study is to develop a prediction model for the tractor axle torque during tillage operation that can replace expensive axle torque sensors. A prediction model was proposed through regression analysis using key variables affecting the tractor axle torque. The engine torque, engine speed, tillage depth, slip ratio, and travel speed were selected as explanatory variables. In order to collect explanatory and dependent variable data, a load measurement system was developed, and a field experiment was performed on moldboard plow tillage using a tractor with a load measurement system. A total of eight axle torque prediction regression models were proposed using the measured calibration dataset. The adjusted coefficient of determination (R2) of the proposed regression model showed a range of 0.271 to 0.925. Among them, the prediction model E showed an adjusted R2 of 0.925. All of the prediction models were verified using a validation set. All of the axle torque prediction models showed an mean absolute percentage error (MAPE) of less than 2.8%. In particular, Model E, adopting engine torque, engine speed, and travel speed as variables, and Model H, adopting engine torque, tillage depth and travel speed as variables, showed MAPEs of 1.19 and 1.30%, respectively. Therefore, it was found that the proposed prediction models are applicable to actual axle torque prediction. Full article
(This article belongs to the Special Issue Applied Agri-Technologies)
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Open AccessArticle
Visual Information Requirements for Remotely Supervised Autonomous Agricultural Machines
Appl. Sci. 2020, 10(8), 2794; https://doi.org/10.3390/app10082794 - 17 Apr 2020
Cited by 1 | Viewed by 500
Abstract
A study to determine the visual requirements for a remote supervisor of an autonomous sprayer was conducted. Observation of a sprayer operator identified 9 distinct “look zones” that occupied his visual attention, with 39% of his time spent viewing the look zone ahead [...] Read more.
A study to determine the visual requirements for a remote supervisor of an autonomous sprayer was conducted. Observation of a sprayer operator identified 9 distinct “look zones” that occupied his visual attention, with 39% of his time spent viewing the look zone ahead of the sprayer. While observation of the sprayer operator was being completed, additional GoPro cameras were used to record video of the sprayer in operation from 10 distinct perspectives (some look zones were visible from the operator’s seat, but other look zones were selected to display other regions of the sprayer that might be of interest to a sprayer operator). In a subsequent laboratory study, 29 experienced sprayer operators were recruited to view and comment on video clips selected from the video footage collected during the initial ride-along. Only the two views from the perspective of the operator’s seat were rated highly as providing important information even though participants were able to identify relevant information from all ten of the video clips. Generally, participants used the video clips to obtain information about the boom status, the location and movement of the sprayer within the field, the weather conditions (especially the wind), obstacles to be avoided, crop conditions, and field conditions. Sprayer operators with more than 15 years of experience provided more insightful descriptions of the video clips than their less experienced peers. Designers can influence which features the user will perceive by positioning the camera such that those specific features are prominent in the camera’s field of view. Overall, experienced sprayer operators preferred the concept of presenting visual information on an automation interface using live video rather than presenting that same information using some type of graphical display using icons or symbols. Full article
(This article belongs to the Special Issue Applied Agri-Technologies)
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Open AccessArticle
Research on 2D Laser Automatic Navigation Control for Standardized Orchard
Appl. Sci. 2020, 10(8), 2763; https://doi.org/10.3390/app10082763 - 16 Apr 2020
Cited by 1 | Viewed by 612
Abstract
With the increase of labor cost and the development of agricultural mechanization, standardized orchards suitable for autonomous operations of agricultural machinery will be a future development trend of the fruit-planting industry. For field-planting processes of standardized orchards, autonomous navigation of orchard vehicles in [...] Read more.
With the increase of labor cost and the development of agricultural mechanization, standardized orchards suitable for autonomous operations of agricultural machinery will be a future development trend of the fruit-planting industry. For field-planting processes of standardized orchards, autonomous navigation of orchard vehicles in complex environments is the foundation of mechanized and intelligent field operations. In order to realize autonomous driving and path-tracking of vehicles in complex standardized orchards that involve much noise and interference between rows of fruit trees, an automatic navigation system was designed for orchard vehicles, based on 2D lasers. First, considering the agronomic requirements for orchard planting such as plant spacing, row spacing and trunk diameter, different filtering thresholds were established to eliminate discrete points of 2D laser point cloud data effectively. Euclidean clustering algorithm and the important geometric theorems of three points collinearity was used to extract the central feature points of the trunk, as the same time, navigation path was fitted based on the least square method. Secondly, an automatic navigation control algorithm was designed, and the fuzzy control was used to realize the dynamic adjustment of the apparent distance of the pure pursuit model. Finally, the reliability of the proposed approach was verified by simulation using MATLAB/Simulink, and field tests were carried out based on electric agricultural vehicle. Experimental results show that the method proposed in this study can effectively improve the precision of automatic navigation in complex orchard environment and realize the autonomous operation of orchard vehicles. Full article
(This article belongs to the Special Issue Applied Agri-Technologies)
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Open AccessArticle
Behavior of Different Grafting Strategies Using Automated Technology for Splice Grafting Technique
Appl. Sci. 2020, 10(8), 2745; https://doi.org/10.3390/app10082745 - 16 Apr 2020
Viewed by 645
Abstract
Even though the splicing graft technique is relatively recent, it has become the most commonly used grafting method for solanaceae, and in particular, for tomato. Today, almost everyone has standardized the use of plastic or silicone grafting clips, equipped with manipulating wings and [...] Read more.
Even though the splicing graft technique is relatively recent, it has become the most commonly used grafting method for solanaceae, and in particular, for tomato. Today, almost everyone has standardized the use of plastic or silicone grafting clips, equipped with manipulating wings and a frontal opening, to ensure proper bonding and allow for wound healing. Numerous factors influence the success or failure of the grafting process, factors such as the seedling varieties combined, climatic conditions, pre-graft and post-graft care, cutting point, cutting angle, pressure of the clips, blade edge, or substrate water content, among others. In this work, several alternatives in the graft assembly and coupling protocol were evaluated. Having studied the different working alternatives for grafting using a robotic system, two modes of joining order were analyzed. It has been shown that there are 20% more recorded successes if one first joins the graft seedlings and then places the grafting clip to guarantee their union. In addition, we studied the different orientation alternatives for the cutting line and the seedling union with respect to the clip opening—there were approximately 10% more successes obtained in grafts where the splice-union cutting line between the two plants faced the clip opening. Full article
(This article belongs to the Special Issue Applied Agri-Technologies)
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Open AccessArticle
Optimized LED-Integrated Agricultural Facilities for Adjusting the Growth of Water Bamboo (Zizania latifolia)
Appl. Sci. 2020, 10(4), 1330; https://doi.org/10.3390/app10041330 - 16 Feb 2020
Viewed by 1284
Abstract
We investigated a light emitting diode (LED) lighting system applied to a water bamboo field during winter season at night, and the results indicated that this lighting system can prevent the stunting of water bamboo leaves and further assist its growth. Compared with [...] Read more.
We investigated a light emitting diode (LED) lighting system applied to a water bamboo field during winter season at night, and the results indicated that this lighting system can prevent the stunting of water bamboo leaves and further assist its growth. Compared with previous LED systems, in which the LED bulbs were placed directly above water bamboo leaves, our LED lighting system presents the benefit of easy handling during harvest. To prevent the inhomogeneous coverage of LED light patterns, a new design of LED lenses was also incorporated. Full article
(This article belongs to the Special Issue Applied Agri-Technologies)
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Open AccessArticle
A Context-Aware Middleware Cloud Approach for Integrating Precision Farming Facilities into the IoT toward Agriculture 4.0
Appl. Sci. 2020, 10(3), 813; https://doi.org/10.3390/app10030813 - 23 Jan 2020
Cited by 12 | Viewed by 1901
Abstract
The adoption of Precision Farming (PF) practices involving ubiquitous computing advancements and conceptual innovations of “smart” agricultural production toward Agriculture 4.0 is a significant factor for the benefit of sustainable growth. In this context, the dynamic integration of PF facility systems into the [...] Read more.
The adoption of Precision Farming (PF) practices involving ubiquitous computing advancements and conceptual innovations of “smart” agricultural production toward Agriculture 4.0 is a significant factor for the benefit of sustainable growth. In this context, the dynamic integration of PF facility systems into the Internet of Things (IoT) represents an excessive challenge considering the large amount of heterogeneous raw data acquired in agricultural environments by Wireless Sensor and Actuator Networks (WSANs). This paper focuses on the issue of facilitating the management, process, and exchange of the numerous and diverse data points generated in multiple PF environments by introducing a framework of a cloud-based context-aware middleware solution as part of a responsive, adaptive, and service-oriented IoT integrated system. More particularly, the paper presents in detail a layered hierarchical structure according to which all functional elements of the system cope with context, while the context awareness operation is accomplished into a cloud-based distributed middleware component that is the core of the entire system acting as a Decision Support System (DSS). Furthermore, as proof of concept, the functionality of the proposed system is studied in real conditions where some evaluation results regarding its performance are quoted. Full article
(This article belongs to the Special Issue Applied Agri-Technologies)
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Open AccessArticle
A Convolutional Neural Networks Based Method for Anthracnose Infected Walnut Tree Leaves Identification
Appl. Sci. 2020, 10(2), 469; https://doi.org/10.3390/app10020469 - 08 Jan 2020
Cited by 7 | Viewed by 1111
Abstract
Anthracnose is a fungal disease that infects a large number of trees worldwide, damages intensively the canopy, and spreads with ease to neighboring trees, resulting in the potential destruction of whole crops. Even though it can be treated relatively easily with good sanitation, [...] Read more.
Anthracnose is a fungal disease that infects a large number of trees worldwide, damages intensively the canopy, and spreads with ease to neighboring trees, resulting in the potential destruction of whole crops. Even though it can be treated relatively easily with good sanitation, proper pruning and copper spraying, the main issue is the early detection for the prevention of spreading. Machine learning algorithms can offer the tools for the on-site classification of healthy and affected leaves, as an initial step towards managing such diseases. The purpose of this study was to build a robust convolutional neural network (CNN) model that is able to classify images of leaves, depending on whether or not these are infected by anthracnose, and therefore determine whether a tree is infected. A set of images were used both in grayscale and RGB mode, a fast Fourier transform was implemented for feature extraction, and a CNN architecture was selected based on its performance. Finally, the best performing method was compared with state-of-the-art convolutional neural network architectures. Full article
(This article belongs to the Special Issue Applied Agri-Technologies)
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Open AccessArticle
Introduction of a New Index of Field Operations Efficiency
Appl. Sci. 2020, 10(1), 329; https://doi.org/10.3390/app10010329 - 01 Jan 2020
Cited by 1 | Viewed by 819
Abstract
The evaluation and prediction of the agricultural machinery field efficiency is essential for agricultural operations management. Field efficiency is affected by unpredictable (e.g., machine breakdowns) and stochastic (e.g., yield) factors, and thus, it is generally provided by average norms. However, the average values [...] Read more.
The evaluation and prediction of the agricultural machinery field efficiency is essential for agricultural operations management. Field efficiency is affected by unpredictable (e.g., machine breakdowns) and stochastic (e.g., yield) factors, and thus, it is generally provided by average norms. However, the average values and ranges of the field efficiency are of limited value when a decision has to be made on the selection of the appropriate machinery system for a specific operational set up. To this end, in this paper, a new index for field operability, the field traversing efficiency (FTE), a distance-based measure, is introduced and a dedicated tool for estimation of this measure is presented. In order to show the degree of the dependence of the FTE index on the operational features, a number of 864 scenarios derived from the consideration of six sample field shapes, three conventional fieldwork patterns, four driving directions, and twelve combinations of machine unit kinematics and implement width were evaluated by the developed tool. The test results showed that variation of FTE was up to 23% in the tested scenarios when using different operational setups. Full article
(This article belongs to the Special Issue Applied Agri-Technologies)
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Open AccessArticle
Phase Space Analysis of Pig Ear Skin Temperature during Air and Road Transport
Appl. Sci. 2019, 9(24), 5527; https://doi.org/10.3390/app9245527 - 16 Dec 2019
Viewed by 498
Abstract
High or variable ambient temperature can affect thermal regulation in livestock, but few studies have studied thermal variability during air and road transport, partly due to the lack of tools to compare thermal data from a long time series over periods of different [...] Read more.
High or variable ambient temperature can affect thermal regulation in livestock, but few studies have studied thermal variability during air and road transport, partly due to the lack of tools to compare thermal data from a long time series over periods of different duration. In this study, we recorded the ear skin temperature (EST) of 11 Duroc breeder pigs (7 females and 4 males) during commercial intercontinental transport from Canada to Spain, which included both road and aircraft travel and lasted 65 h. The EST was measured using a logger placed inside the left ear. Phase space diagrams EST, that is EST time series vs. itself delayed in time, were used to quantify the variability of the time-temperature series based on the areas that included all the points in the phase space. Phase space areas were significantly higher for all the animals during air travel, almost doubling that of road transport. Using the phase spaces, we identified an event during air transport that lasted 57 min, leading to a general decrease in EST by 8 °C, with respect to the average EST (34.1 °C). We also found that thermal variability was more stable in males (F = 20.81, p = 0.0014), which were also older and heavier. Full article
(This article belongs to the Special Issue Applied Agri-Technologies)
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Review

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Open AccessReview
Deep Learning Sensor Fusion in Plant Water Stress Assessment: A Comprehensive Review
Appl. Sci. 2021, 11(4), 1403; https://doi.org/10.3390/app11041403 - 04 Feb 2021
Viewed by 564
Abstract
Water stress is one of the major challenges to food security, causing a significant economic loss for the nation as well for growers. Accurate assessment of water stress will enhance agricultural productivity through optimization of plant water usage, maximizing plant breeding strategies, and [...] Read more.
Water stress is one of the major challenges to food security, causing a significant economic loss for the nation as well for growers. Accurate assessment of water stress will enhance agricultural productivity through optimization of plant water usage, maximizing plant breeding strategies, and preventing forest wildfire for better ecosystem management. Recent advancements in sensor technologies have enabled high-throughput, non-contact, and cost-efficient plant water stress assessment through intelligence system modeling. The advanced deep learning sensor fusion technique has been reported to improve the performance of the machine learning application for processing the collected sensory data. This paper extensively reviews the state-of-the-art methods for plant water stress assessment that utilized the deep learning sensor fusion approach in their application, together with future prospects and challenges of the application domain. Notably, 37 deep learning solutions fell under six main areas, namely soil moisture estimation, soil water modelling, evapotranspiration estimation, evapotranspiration forecasting, plant water status estimation and plant water stress identification. Basically, there are eight deep learning solutions compiled for the 3D-dimensional data and plant varieties challenge, including unbalanced data that occurred due to isohydric plants, and the effect of variations that occur within the same species but cultivated from different locations. Full article
(This article belongs to the Special Issue Applied Agri-Technologies)
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Open AccessReview
Blockchain in Agriculture Traceability Systems: A Review
Appl. Sci. 2020, 10(12), 4113; https://doi.org/10.3390/app10124113 - 15 Jun 2020
Cited by 11 | Viewed by 2374
Abstract
Food holds a major role in human beings’ lives and in human societies in general across the planet. The food and agriculture sector is considered to be a major employer at a worldwide level. The large number and heterogeneity of the stakeholders involved [...] Read more.
Food holds a major role in human beings’ lives and in human societies in general across the planet. The food and agriculture sector is considered to be a major employer at a worldwide level. The large number and heterogeneity of the stakeholders involved from different sectors, such as farmers, distributers, retailers, consumers, etc., renders the agricultural supply chain management as one of the most complex and challenging tasks. It is the same vast complexity of the agriproducts supply chain that limits the development of global and efficient transparency and traceability solutions. The present paper provides an overview of the application of blockchain technologies for enabling traceability in the agri-food domain. Initially, the paper presents definitions, levels of adoption, tools and advantages of traceability, accompanied with a brief overview of the functionality and advantages of blockchain technology. It then conducts an extensive literature review on the integration of blockchain into traceability systems. It proceeds with discussing relevant existing commercial applications, highlighting the relevant challenges and future prospects of the application of blockchain technologies in the agri-food supply chain. Full article
(This article belongs to the Special Issue Applied Agri-Technologies)
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Open AccessReview
Applications of Artificial Neural Networks in Greenhouse Technology and Overview for Smart Agriculture Development
Appl. Sci. 2020, 10(11), 3835; https://doi.org/10.3390/app10113835 - 31 May 2020
Cited by 9 | Viewed by 1044
Abstract
This article reviews the applications of artificial neural networks (ANNs) in greenhouse technology, and also presents how this type of model can be developed in the coming years by adapting to new technologies such as the internet of things (IoT) and machine learning [...] Read more.
This article reviews the applications of artificial neural networks (ANNs) in greenhouse technology, and also presents how this type of model can be developed in the coming years by adapting to new technologies such as the internet of things (IoT) and machine learning (ML). Almost all the analyzed works use the feedforward architecture, while the recurrent and hybrid networks are little exploited in the various tasks of the greenhouses. Throughout the document, different network training techniques are presented, where the feasibility of using optimization models for the learning process is exposed. The advantages and disadvantages of neural networks (NNs) are observed in the different applications in greenhouses, from microclimate prediction, energy expenditure, to more specific tasks such as the control of carbon dioxide. The most important findings in this work can be used as guidelines for developers of smart protected agriculture technology, in which systems involve technologies 4.0. Full article
(This article belongs to the Special Issue Applied Agri-Technologies)
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Open AccessReview
A Review on Ergonomics in Agriculture. Part II: Mechanized Operations
Appl. Sci. 2020, 10(10), 3484; https://doi.org/10.3390/app10103484 - 18 May 2020
Cited by 4 | Viewed by 780
Abstract
Background: Musculoskeletal disorders (MSDs) have long been recognized as the most common risks that operation of agricultural machineries poses, thus, undermining the ability to labor and quality of life. The purpose of this investigation was to thoroughly review the recent scholarly literature on [...] Read more.
Background: Musculoskeletal disorders (MSDs) have long been recognized as the most common risks that operation of agricultural machineries poses, thus, undermining the ability to labor and quality of life. The purpose of this investigation was to thoroughly review the recent scholarly literature on ergonomics in agricultural mechanized operations; Methods: Electronic database research over the last ten years was conducted based on specific inclusion criteria. Furthermore, an assessment of the methodological quality and strength of evidence of potential risk factors causing MSDs was performed; Results: The results demonstrated that ergonomics in agriculture is an interdisciplinary topic and concerns both developed and developing countries. The machines with driving seats seem to be associated with painful disorders of the low back, while handheld machines with disorders of the upper extremities. The main roots of these disorders are the whole-body vibration (WBV) and hand-arm transmitted vibration (HATV). However, personal characteristics, awkward postures, mechanical shocks and seat discomfort were also recognized to cause MSDs; Conclusions: The present ergonomic interventions aim mainly at damping of vibrations and improving the comfort of operator. Nevertheless, more collaborative efforts among physicians, ergonomists, engineers and manufacturers are required in terms of both creating new ergonomic technologies and increasing the awareness of workers for the involved risk factors. Full article
(This article belongs to the Special Issue Applied Agri-Technologies)
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Open AccessReview
Mobile Robotics in Agricultural Operations: A Narrative Review on Planning Aspects
Appl. Sci. 2020, 10(10), 3453; https://doi.org/10.3390/app10103453 - 17 May 2020
Cited by 5 | Viewed by 938
Abstract
The advent of mobile robots in agriculture has signaled a digital transformation with new automation technologies optimize a range of labor-intensive, resources-demanding, and time-consuming agri-field operations. To that end a generally accepted technical lexicon for mobile robots is lacking as pertinent terms are [...] Read more.
The advent of mobile robots in agriculture has signaled a digital transformation with new automation technologies optimize a range of labor-intensive, resources-demanding, and time-consuming agri-field operations. To that end a generally accepted technical lexicon for mobile robots is lacking as pertinent terms are often used interchangeably. This creates confusion among research and practice stakeholders. In addition, a consistent definition of planning attributes in automated agricultural operations is still missing as relevant research is sparse. In this regard, a “narrative” review was adopted (1) to provide the basic terminology over technical aspects of mobile robots used in autonomous operations and (2) assess fundamental planning aspects of mobile robots in agricultural environments. Based on the synthesized evidence from extant studies, seven planning attributes have been included: (i) high-level control-specific attributes, which include reasoning architecture, the world model, and planning level, (ii) operation-specific attributes, which include locomotion–task connection and capacity constraints, and (iii) physical robot-specific attributes, which include vehicle configuration and vehicle kinematics. Full article
(This article belongs to the Special Issue Applied Agri-Technologies)
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Open AccessReview
A Review on Ergonomics in Agriculture. Part I: Manual Operations
Appl. Sci. 2020, 10(6), 1905; https://doi.org/10.3390/app10061905 - 11 Mar 2020
Cited by 6 | Viewed by 1216
Abstract
Background: Agriculture involves several harmful diseases. Among the non-fatal ones, musculoskeletal disorders (MSDs) are the most prevalent, as they have reached epidemic proportions. The main aim of this investigation is to systematically review the major risk factors regarding MSDs as well as evaluate [...] Read more.
Background: Agriculture involves several harmful diseases. Among the non-fatal ones, musculoskeletal disorders (MSDs) are the most prevalent, as they have reached epidemic proportions. The main aim of this investigation is to systematically review the major risk factors regarding MSDs as well as evaluate the existing ergonomic interventions. Methods: The search engines of Google Scholar, PubMed, Scopus, and ScienceDirect were used to identify relevant articles during the last decade. The imposed exclusive criteria assured the accuracy and current progress in this field. Results: It was concluded that MSDs affect both developed and developing countries, thus justifying the existing global concern. Overall, the most commonly studied task was harvesting, followed by load carrying, pruning, planting, and other ordinary manual operations. Repetitive movements in awkward postures, such as stooping and kneeling; individual characteristics; as well as improper tool design were observed to contribute to the pathogenesis of MSDs. Furthermore, low back disorders were reported as the main disorder. Conclusions: The present ergonomic interventions seem to attenuate the MSDs to a great extent. However, international reprioritization of the safety and health measures is required in agriculture along with increase of the awareness of the risk factors related to MSDs. Full article
(This article belongs to the Special Issue Applied Agri-Technologies)
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