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Agronomy
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Agricultural Automation and Innovative Agricultural Systems—2nd Edition
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Agricultural Automation and Innovative Agricultural Systems—2nd Edition

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Innovative Cropping Systems".

Deadline for manuscript submissions: closed (31 January 2025) | Viewed by 32553

Special Issue Editors

Prof. Dr. Egidijus Šarauskis

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Guest Editor
Agriculture Academy, Faculty of Agricultural Engineering, Institute of Agricultural Engineering and Safety, Vytautas Magnus University, Studentu Str. 15A, LT-53362 Akademija, Kaunas Distr., Lithuania
Interests: agricultural engineering; environment engineering; reduced tillage technologies; sowing machinery; precision agriculture; sowing and weed control robots; technological, energetic, and environmental assessment of the impact of agricultural technological operations on soil and environmental pollution
Special Issues, Collections and Topics in MDPI journals
Dr. Vilma Naujokienė

E-Mail Website
Guest Editor
Department of Agricultural Engineering and Safety, Vytautas Magnus University, Akademija, Kaunas District, Studentu Str. 11, LT-53361 Kaunas, Lithuania
Interests: environmental engineering; technologies; energy cost reduction and environmental sustainability in various agricultural systems; multicriteria bioimpact effectiveness for environmental improvement
Special Issues, Collections and Topics in MDPI journals
Dr. Zita Kriauciuniene

E-Mail Website
Guest Editor
Department of Agroecosystems and Soil Sciences, Faculty of Agronomy, Agriculture Academy, Vytautas Magnus University, Studentu Str. 11, LT-53361 Akademija, Lithuania
Interests: soil, crop and residues management improvement; soil health; soil organic matter; soil biological activity; soil agrochemical and agrophysical parameters; use of bioactivators; meat and bone meal fertilisers in crop growing technologies; crop and weed allelopathy; sustainable agrotechnologies; precision agriculture; ICT in agriculture
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Delivering the European Green Deal and UN Sustainable Development Goals (SDGs) has highlighted the importance of achieving agricultural transition via the employment and realization of innovative agricultural systems, digitalization and automation. At present, technological developments are extremely rapid, but are still not implemented sufficiently in the agricultural sector. Innovations and automation are able to ensure the healthy production of food, the sustainable use of resources, and the adaptation and mitigation of climate change with the aim of saving the planet for future generations. Researchers play a crucial role in answering questions and helping to implement innovative adaptations in practice. In this Special Issue, we invite you to share the results of your high-quality research into innovative agrotechnologies, the application of novel preparations and equipment, automation, robotization, digitalization, ICT, sensors in agricultural systems, the application of precision agriculture, smart agricultural engineering in conventional, sustainable and organic crop production, horticulture, animal husbandry, and primary processing.

Prof. Dr. Egidijus Šarauskis
Dr. Vilma Naujokienė
Dr. Zita Kriauciuniene
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. Agronomy 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

  • agricultural machinery
  • automation and robotization
  • digital agriculture
  • precision agriculture
  • organic agriculture
  • innovation in animal husbandry
  • soil management
  • sowing, fertilization, spraying operations
  • plant care
  • harvesting
  • application of innovative bio-products
  • primary processing
  • sensing methods and devices
  • validation of automated methods
  • energy, economic and environmental assessment

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Related Special Issue

Published Papers (6 papers)

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Research

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33 pages, 3882 KiB  
Article
Optimization of Potato Cultivation Through the Use of Biostimulator Supporter
by Piotr Barbaś, Barbara Sawicka, Piotr Pszczółkowski, Talal Seead Hameed and Alaa Kadhim Farhan
Agronomy 2024, 14(10), 2430; https://doi.org/10.3390/agronomy14102430 - 20 Oct 2024
Cited by 1 | Viewed by 1433
Abstract
Seed potato treatment is vital for plant protection, yield enhancement, and product quality. In the conducted research, the plant biostimulator Supporter was applied to evaluate its impact on potato yields and its structure. Supporter contains both synthetic and SL amino acids, which promote [...] Read more.
Seed potato treatment is vital for plant protection, yield enhancement, and product quality. In the conducted research, the plant biostimulator Supporter was applied to evaluate its impact on potato yields and its structure. Supporter contains both synthetic and SL amino acids, which promote plant growth by enhancing nutrient utilization and fostering the development of a more effective root system. Such a formulation allows to maintain better resistance to environmental stresses, which may include drought or nutrient deficiency, among others. The field study was conducted in 2015–2017 in four towns located in different regions of Poland (Barankowo, Głubczyce, Kędrzyno, and Ryn) using a randomized complete block design with a split-plot design. Varieties (‘Innovator’, ‘Lilly’, ‘Lady Claire’, and ‘Verdi’) were tested. The experiment compared the cultivation technology using Supporter biostimulator with which seed potatoes were treated compared to conventional cultivation (control object) by soaking the tubers in distilled water before planting. The total yield of potato tubers after Supporter application was higher by 13.3%, while the commercial yield increased by 21.1% compared to the traditional cultivation method. The most productive, regardless of cultivation technology and years of research, in terms of total tuber yield was the ‘Lilly’ variety with an average yield of 47.95 t∙ha−1, while the least productive variety was the ‘Innovator’ variety with an average yield of 29.93 t∙ha−1. The ‘Lady Claire’ variety had the highest commercial tuber yield, while the ‘Innovator’ variety had the lowest. Full article
(This article belongs to the Special Issue Agricultural Automation and Innovative Agricultural Systems—2nd Edition)
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19 pages, 12861 KiB  
Article
Optimization of Clamping and Conveying Parameters for Spinach Orderly Harvesting with Low Damage by Simulation and Experiment
by Huankun Wang, Chong Qi, Qiaojun Luo, Minglin Chen, Yidong Ma and Xianlong Wang
Agronomy 2024, 14(9), 2164; https://doi.org/10.3390/agronomy14092164 - 22 Sep 2024
Viewed by 829
Abstract
The leaves of spinach are delicate and easily injured during harvesting. To reduce the spinach damage rate and increase the conveyance success rate, an orderly harvester was designed and manufactured, and the key conveying parameters of the harvester were optimized by simulation and [...] Read more.
The leaves of spinach are delicate and easily injured during harvesting. To reduce the spinach damage rate and increase the conveyance success rate, an orderly harvester was designed and manufactured, and the key conveying parameters of the harvester were optimized by simulation and experiments. The compression damage stress of spinach was determined by compression tests. Then, a finite element simulation model for spinach clamping was established, and the influence of different clamping heights on the spinach deformation and equivalent stress were simulated and analyzed. Finally, response surface Box–Behnken experiments were conducted to optimize the combinations of the twisting angle, clamping distance, and height difference. The results of the compression tests showed that the compression damage stresses of spinach leaves, stems, and their connection points were 8.04 × 10−2 MPa, 7.85 × 10−2 MPa, and 11.63 × 10−2 MPa, respectively. The optimal clamping height of spinach for orderly conveyance was obtained to be 20 mm according to the finite element simulation. The response surface experimental results indicated that the significance order of factors affecting the extrusion force was the clamping distance, the height difference, and the twisting angle. The significance order of factors affecting the conveyance success rate was the clamping distance, the twisting angle, and the height difference. The optimal parameter combination was ae twisting angle of 60°, clamping distance of 24 mm, and a height difference of 20 cm. The experimental validation of the optimization results from the finite element simulation and response surface tests demonstrated that the extrusion force and conveyance success rate were 2.37 N and 94%, respectively, with a conveying damage rate of 3% for spinach, meeting the requirements for the low-damage and orderly harvesting of spinach. Full article
(This article belongs to the Special Issue Agricultural Automation and Innovative Agricultural Systems—2nd Edition)
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16 pages, 4185 KiB  
Article
Effectiveness of the Sustainable Manure Pile Model for Ammonia Emission and Soil
by Rolandas Bleizgys, Arvydas Povilaitis, Juozas Pekarskas and Vilma Naujokienė
Agronomy 2024, 14(7), 1475; https://doi.org/10.3390/agronomy14071475 - 8 Jul 2024
Cited by 1 | Viewed by 1273
Abstract
In order to reduce odor emissions and surface water pollution while storing manure in field heaps near a barn, there is a challenge in properly designing manure-storage areas. Therefore, it is important to assess what solutions and conditions, considering environmental requirements, should be [...] Read more.
In order to reduce odor emissions and surface water pollution while storing manure in field heaps near a barn, there is a challenge in properly designing manure-storage areas. Therefore, it is important to assess what solutions and conditions, considering environmental requirements, should be considered when storing manure in field heaps. The goal of the research is to determine the impact of various factors on the risk of nutrient leaching, soil, and gas emissions from solid manure heaps, considering climatic factors in the environment. Through various scientific studies, a manure pile model has been developed and evaluated for its impact on the risk of potential leaching and odor emissions (using hyperspectral gas emission analysis mass flow method) from manure and the dynamics of the 0–80 cm soil layer properties (nitrate (N-NO3) and nitrite (N-NO2), ammonia (NH3), mineral, and total N). Based on the research results, requirements for manure management and storage during the prohibited fertilization period were established, considering the requirements for nitrates from agricultural sources in Lithuania. An optimal new manure heap model has been identified—a layer of not less than 20 cm of compacted straw (density 150–200 kg m−3) or a 10 cm layer of peat for absorbing manure slurries is formed on the soil surface, the manure heap is surrounded by an earth embankment not less than 30 cm high, the manure heap is covered with a layer of finely chopped straw not less than 10 cm thick, or 5 cm of sawdust, or 5 cm of peat. The manure is stored in the heap for 6–12 months. Following the research results, requirements for manure management and storage during the prohibited fertilization period were established, considering the requirements for nitrates from agricultural sources in Lithuania, applicable to the northern part of the temperate climate zone and applying similar requirements to the relevant countries. Full article
(This article belongs to the Special Issue Agricultural Automation and Innovative Agricultural Systems—2nd Edition)
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12 pages, 4966 KiB  
Article
Empirical Modelling of Power Requirements in Olive Pruning Residue Shredding: Effects of Varying Moisture Content and Rotary Speeds
by Mete Yiğit, Murad Çanakcı and Davut Karayel
Agronomy 2024, 14(7), 1455; https://doi.org/10.3390/agronomy14071455 - 4 Jul 2024
Viewed by 789
Abstract
Pruning residues, which occur every year in orchards and have many different utilization potentials, are an important issue for fruit producers. The shredding process is indispensable and critical for the utilization of these residues. The performance of the shredding process is affected by [...] Read more.
Pruning residues, which occur every year in orchards and have many different utilization potentials, are an important issue for fruit producers. The shredding process is indispensable and critical for the utilization of these residues. The performance of the shredding process is affected by the operating parameters of the shredding machine as well as the moisture content of the residues to be shredded. In this study, olive pruning residues with three different moisture contents were shredded at three different rotor speeds in the developed shredding system. We determined how the power requirement of the shredder changed under different conditions, and empirical models were developed. The experiments showed that the average power requirement of the shredder ranged from 7.32 to 10.81 kW, and it was found that residues with low moisture content decreased the power values, while higher rotor speeds increased the power requirement. The developed final model has a mean absolute error (MAE) of 0.376, a root mean square error (RMSE) of 0.441, and a correlation coefficient (R2) of 0.859. The model serves as a reliable tool for estimating power requirements in the shredding of olive pruning residues, enabling the selection of the optimal rotor speed based on moisture content. Full article
(This article belongs to the Special Issue Agricultural Automation and Innovative Agricultural Systems—2nd Edition)
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14 pages, 5898 KiB  
Article
Theoretical and Experimental Verification of the Physical–Mechanical Properties of Organic Bone Meal Granular Fertilizers
by Eglė Jotautienė, Vaidas Bivainis, Davut Karayel and Ramūnas Mieldažys
Agronomy 2024, 14(6), 1171; https://doi.org/10.3390/agronomy14061171 - 30 May 2024
Viewed by 1165
Abstract
Continuous efforts are being made to improve fertilizer efficiency by improving fertilizer technology, quality, and application rates. Granular organic fertilizers are more difficult to achieve uniform application because their physical–mechanical properties differ significantly from mineral fertilizers. The properties of granular organic fertilizers can [...] Read more.
Continuous efforts are being made to improve fertilizer efficiency by improving fertilizer technology, quality, and application rates. Granular organic fertilizers are more difficult to achieve uniform application because their physical–mechanical properties differ significantly from mineral fertilizers. The properties of granular organic fertilizers can best be determined experimentally. However, these studies are often quite complex. Modern engineering modeling software makes it possible to model the properties of granular fertilizers and their dispersion. This study deals with the theoretical and experimental verification of the physical–mechanical properties of organic bone meal granular fertilizer. For the verification of selected properties of bone meal granules, the following studies were carried out on the granules: determination of poured bulk density, static and dynamic angles of repose, static and dynamic friction coefficients of granule surface, etc. The results showed that for modeling fertilizer properties, it is sufficient to carry out a static compression test to determine the modulus of elasticity and a friction test between granules and the contacting surface to determine the static and dynamic friction coefficients. The remaining properties of the granules can be modeled and calibrated with the DEM software Altair EDEM 2023. Full article
(This article belongs to the Special Issue Agricultural Automation and Innovative Agricultural Systems—2nd Edition)
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Review

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27 pages, 792 KiB  
Review
Machine Learning Applications in Agriculture: Current Trends, Challenges, and Future Perspectives
by Sara Oleiro Araújo, Ricardo Silva Peres, José Cochicho Ramalho, Fernando Lidon and José Barata
Agronomy 2023, 13(12), 2976; https://doi.org/10.3390/agronomy13122976 - 1 Dec 2023
Cited by 64 | Viewed by 25973
Abstract
Progress in agricultural productivity and sustainability hinges on strategic investments in technological research. Evolving technologies such as the Internet of Things, sensors, robotics, Artificial Intelligence, Machine Learning, Big Data, and Cloud Computing are propelling the agricultural sector towards the transformative Agriculture 4.0 paradigm. [...] Read more.
Progress in agricultural productivity and sustainability hinges on strategic investments in technological research. Evolving technologies such as the Internet of Things, sensors, robotics, Artificial Intelligence, Machine Learning, Big Data, and Cloud Computing are propelling the agricultural sector towards the transformative Agriculture 4.0 paradigm. The present systematic literature review employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to explore the usage of Machine Learning in agriculture. The study investigates the foremost applications of Machine Learning, including crop, water, soil, and animal management, revealing its important role in revolutionising traditional agricultural practices. Furthermore, it assesses the substantial impacts and outcomes of Machine Learning adoption and highlights some challenges associated with its integration in agricultural systems. This review not only provides valuable insights into the current landscape of Machine Learning applications in agriculture, but it also outlines promising directions for future research and innovation in this rapidly evolving field. Full article
(This article belongs to the Special Issue Agricultural Automation and Innovative Agricultural Systems—2nd Edition)
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