Special Issue "Engineering of Smart Agriculture"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Agricultural Engineering".

Deadline for manuscript submissions: 25 March 2022.

Special Issue Editors

Dr. Paweł Kiełbasa
E-Mail Website
Guest Editor
Faculty of Production and Power Engineering, University of Agriculture in Krakow, Balicka 116, Kraków, Poland
Interests: machine management in agriculture; ergonomics in agricultural technology; electromagnetic identification of plant quality structure; soil type; subsoil compaction; agricultural engineering; electromagnetism
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Tadeusz Juliszewski
E-Mail Website
Guest Editor
Faculty of Production and Power Engineering, University of Agriculture in Krakow, Balicka 116, Kraków, Poland
Interests: ergonomics; biofuels; production engineering
Prof. Dr. Sławomir Kurpaska
E-Mail Website
Guest Editor
Faculty of Production and Power Engineering, University of Agriculture in Krakow, Balicka 116, Kraków, Poland
Interests: air source heat pumps; water heaters; applied sciences; Horticulture; solar heating; greenhouses; microclimate; natural ventilation

Special Issue Information

Dear Colleagues,

Modern agricultural production has two main tasks that now must coexist. The first is yield maximization in order to satisfy market needs, and the second is minimization of interference with the soil environment. One of the basic criteria of a balance between these tasks is the degree of soil biological improvement, the parameterization of which is an important issue in modern production systems. Among the innovative technologies that have been developed in the last few decades, precision agriculture can be considered the most important, which is considered to be an excellent tool for the development of sustainable agriculture and allows us to optimize production for present and future generations while taking into account economic, ecological, and social aspects. This concept was born from the conviction that the variability in plant growth conditions is the factor that contributes most to the variability in yields at the field scale and, therefore, it would be advantageous to adapt the amount of input to the local soil conditions and, therefore, to perform the right treatment in the right place and at the right time. A very important issue is the search for the most effective methods that will allow us to delineate in the field areas differing in production conditions, among which soil properties are the most important. A number of technologically advanced devices have been developed, thanks to which large amounts of data can be acquired in real time under field conditions in a continuous measurement mode using proximity detection. Modern technical solutions allow for integration of satellite-based surface condition identification systems with ground-based systems and aircraft. Integrating various measurements into a single system for mapping soil properties is a current research problem. It is predicted that geophysical surveys with the simultaneous use of more sensors will become the standard because of the broad range of field information necessary for proper management. Modern farm and production technologies are monitored through the use of telematic systems and software that allow for real-time analysis and then simulation of the economic outcome of a given activity or process, which consequently leads to its optimization. In addition, networking of the entire machine park enables us to automatically plan maintenance services.

Dr. Paweł Kiełbasa
Prof. Dr. Tadeusz Juliszewski
Prof. Dr. Sławomir Kurpaska
Guest Editors

Manuscript Submission Information

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Keywords

  • precision agriculture telematics
  • geoinformatics
  • agricultural production technology
  • measurement systems
  • agricultural engineering
  • mechanical engineering
  • geophysics
  • soil
  • plant

Published Papers (5 papers)

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Research

Article
Measuring System Design and Experiment for Ground Pressure on Seeding Skateboard of Rice Direct Seeding Machine
by , , , , and
Appl. Sci. 2021, 11(21), 10024; https://doi.org/10.3390/app112110024 (registering DOI) - 26 Oct 2021
Abstract
Acquiring real-time ground pressure measurements from the surface of the soil in working parts of paddy fields is a challenging task. The real-time data can be used to monitor the changing state of the ground pressure of the working parts in a paddy [...] Read more.
Acquiring real-time ground pressure measurements from the surface of the soil in working parts of paddy fields is a challenging task. The real-time data can be used to monitor the changing state of the ground pressure of the working parts in a paddy field. To effectively reduce the accumulation of choked mud at the front end of the seeding skateboard and the contact adhesion between the skateboard and the paddy soil, a ground pressure measuring device suitable for paddy fields was designed. The device uses an Arduino controller, combined with Internet of things technology and wireless measurement technology. It can measure the pressure from 16 measuring points at the same time and transmit the measurement data to the computer remotely through the Internet of things technology, which greatly reduces the labor intensity of measuring personnel in the muddy paddy field. Analysis of the data showed that the forward tilt angle, ground pressure, and forward resistance of the seeding skateboard also increased with the increase of forward speed and vertical load. In addition, the distribution law of the ground pressure between the skateboard and the paddy soil was obtained. The conclusions show that the ground pressure measurement system can work stably in the paddy field and the measured data can be wirelessly transmitted to the computer and mobile phone. Full article
(This article belongs to the Special Issue Engineering of Smart Agriculture)
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Article
Preliminary Research on the Influence of a Pulsed Magnetic Field on the Cationic Profile of Sunflower, Cress, and Radish Sprouts and on Their Germination Rate
Appl. Sci. 2021, 11(20), 9678; https://doi.org/10.3390/app11209678 - 17 Oct 2021
Viewed by 162
Abstract
Magnetic stimulation of seeds before sowing can have a significant impact on the speed of their germination. Sprouts are sought after by consumers for their high nutrient content. The purpose of the study was to investigate the influence of a pulsed magnetic field [...] Read more.
Magnetic stimulation of seeds before sowing can have a significant impact on the speed of their germination. Sprouts are sought after by consumers for their high nutrient content. The purpose of the study was to investigate the influence of a pulsed magnetic field on the dynamics of seed germination and on the content of ions in sunflower, cress, and radish sprouts. The research material in the experiment was provided by seeds of sunflower (Helianthus annuus L.), garden cress (Lepidium sativum L.), and garden radish (Raphanus sativus L.) intended for sprouting, which were supplied by PNOS Ożarów Mazowiecki. The research methods involved germinating seeds under strictly defined conditions for 14 days. Then, the mineral composition of the previously mineralised sprout material was determined using emission spectrometry on a ICP-OES iCAP Duo 6500 Termo spectrometer. Greater dynamics of germination were noted in the first half of the growth period in seeds stimulated with a pulsed magnetic field with the parameters 100 µT and 100 Hz. However, the application of the magnetic field produced no increase in the capacity of the seeds to germinate. The research showed an increase in the content of macronutrients in sprouts, such as calcium, magnesium, phosphorus, and sulphur. In the case of the field with parameters of 100 µT and 200 Hz, the effect was similar for both the germination percentage and the accumulation of macronutrients. However, in the case of both frequencies of magnetic field applied, the effect on individual plant seed species was different. Pre-sowing stimulation of seeds with a pulsed magnetic field may affect the rate of seed germination and the content of ions in the sprouts; however, these effects vary in individual plant matrices. Full article
(This article belongs to the Special Issue Engineering of Smart Agriculture)
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Article
Comparing Performances of CNN, BP, and SVM Algorithms for Differentiating Sweet Pepper Parts for Harvest Automation
Appl. Sci. 2021, 11(20), 9583; https://doi.org/10.3390/app11209583 - 14 Oct 2021
Viewed by 211
Abstract
For harvest automation of sweet pepper, image recognition algorithms for differentiating each part of a sweet pepper plant were developed and performances of these algorithms were compared. An imaging system consisting of two cameras and six halogen lamps was built for sweet pepper [...] Read more.
For harvest automation of sweet pepper, image recognition algorithms for differentiating each part of a sweet pepper plant were developed and performances of these algorithms were compared. An imaging system consisting of two cameras and six halogen lamps was built for sweet pepper image acquisition. For image analysis using the normalized difference vegetation index (NDVI), a band-pass filter in the range of 435 to 950 nm with a broad spectrum from visible light to infrared was used. K-means clustering and morphological skeletonization were used to classify sweet pepper parts to which the NDVI was applied. Scale-invariant feature transform (SIFT) and speeded-up robust features (SURFs) were used to figure out local features. Classification performances of a support vector machine (SVM) using the radial basis function kernel and backpropagation (BP) algorithm were compared to classify local SURFs of fruits, nodes, leaves, and suckers. Accuracies of the BP algorithm and the SVM for classifying local features were 95.96 and 63.75%, respectively. When the BP algorithm was used for classification of plant parts, the recognition success rate was 94.44% for fruits, 84.73% for nodes, 69.97% for leaves, and 84.34% for suckers. When CNN was used for classifying plant parts, the recognition success rate was 99.50% for fruits, 87.75% for nodes, 90.50% for leaves, and 87.25% for suckers. Full article
(This article belongs to the Special Issue Engineering of Smart Agriculture)
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Article
A Digital Twin Architecture to Optimize Productivity within Controlled Environment Agriculture
Appl. Sci. 2021, 11(19), 8875; https://doi.org/10.3390/app11198875 - 24 Sep 2021
Viewed by 399
Abstract
To ensure food security, agricultural production systems should innovate in the direction of increasing production while reducing utilized resources. Due to the higher level of automation with respect to traditional agricultural systems, Controlled Environment Agriculture (CEA) applications generally achieve better yields and quality [...] Read more.
To ensure food security, agricultural production systems should innovate in the direction of increasing production while reducing utilized resources. Due to the higher level of automation with respect to traditional agricultural systems, Controlled Environment Agriculture (CEA) applications generally achieve better yields and quality crops at the expenses of higher energy consumption. In this context, Digital Twin (DT) may constitute a fundamental tool to reach the optimization of the productivity, intended as the ratio between production and resource consumption. For this reason, a DT Architecture for CEA systems is introduced within this work and applied to a case study for its validation. The proposed architecture is potentially able to optimize productivity since it utilizes simulation software that enables the optimization of: (i) Climate control strategies related to the control of the crop microclimate; (ii) treatments related to crop management. Due to the importance of food security in the worldwide landscape, the authors hope that this work may impulse the investigation of strategies for improving the productivity of CEA systems. Full article
(This article belongs to the Special Issue Engineering of Smart Agriculture)
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Article
Design of an Intelligent Variable-Flow Recirculating Aquaculture System Based on Machine Learning Methods
Appl. Sci. 2021, 11(14), 6546; https://doi.org/10.3390/app11146546 - 16 Jul 2021
Cited by 1 | Viewed by 535
Abstract
A recirculating aquaculture system (RAS) can reduce water and land requirements for intensive aquaculture production. However, a traditional RAS uses a fixed circulation flow rate for water treatment. In general, the water in an RAS is highly turbid only when the animals are [...] Read more.
A recirculating aquaculture system (RAS) can reduce water and land requirements for intensive aquaculture production. However, a traditional RAS uses a fixed circulation flow rate for water treatment. In general, the water in an RAS is highly turbid only when the animals are fed and when they excrete. Therefore, RAS water quality regulation technology based on process control is proposed in this paper. The intelligent variable-flow RAS was designed based on the circulating pump-drum filter linkage working model. Machine learning methods were introduced to develop the intelligent regulation model to maintain a clean and stable water environment. Results showed that the long short-term memory network performed with the highest accuracy (training set 100%, test set 96.84%) and F1-score (training 100%, test 93.83%) among artificial neural networks. Optimization methods including grid search, cuckoo search, linear squares, and gene algorithm were proposed to improve the classification ability of support vector machine models. Results showed that all support vector machine models passed cross-validation and could meet accuracy standards. In summary, the gene algorithm support vector machine model (accuracy: training 100%, test 98.95%; F1-score: training 100%, test 99.17%) is suitable as an optimal variable-flow regulation model for an intelligent variable-flow RAS. Full article
(This article belongs to the Special Issue Engineering of Smart Agriculture)
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