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Agriculture
Special Issues
Innovative Methods and Technology for Resilience Agrifood Systems
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Innovative Methods and Technology for Resilience Agrifood Systems

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

Deadline for manuscript submissions: closed (10 May 2023) | Viewed by 3539

Special Issue Editors

Dr. Alfadhl Alkhaled

E-Mail Website
Guest Editor
Department of Horticulture, College of Agricultural & Life Sciences, University of Wisconsin, Madison, WI 53705, USA
Interests: remote sensing; agriculture engineering; hyperspectral imaging system; spectroscopy technology; smart farming; precision agriculture; machine learning; data science; agricultural informatics; instrumentation
Special Issues, Collections and Topics in MDPI journals
Dr. Akinbode A. Adedeji

E-Mail Website
Guest Editor
Department of Biosystems and Agricultural Engineering, University of Kentucky, Lexington, KY 40546, USA
Interests: AI applications in food processing; non-thermal technologies applications food treatment; ultrasound; cold plasma; pulsed UV light; sustainable food processing technology development
Special Issues, Collections and Topics in MDPI journals
Dr. Samsuzana Abd Aziz

E-Mail Website
Guest Editor
Department of Biological and Agricultural Engineering, Faculty of Engineering, University Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia
Interests: agricultural instrumentation; agricultural information technology; agricultural informatics; computational intelligence; precision agriculture

Special Issue Information

Dear Colleagues,

There is no doubt that the world’s growing population urgently needs to develop agricultural and farming techniques to increase productivity to achieve the goal of "Zero Hunger". By November 16, 2022, it is projected that the global population will reach 8 billion, and by 2050, it will increase to about 9.7 billion. The Food and Agriculture Organization (FAO) predicted that worldwide food demand will rise by 50% by 2050. From now till the end of the 21st century, considering the SDG’s targets, substantial effort and innovation will be required to increase agricultural production and food processing sustainably. Consequently, the development and optimization of sustainable modern technologies are indispensable for changing the landscape of how agrifood production and processing are performed and enhancing the operating capabilities in the agriculture sector. Furthermore, there might be a need to revive traditional agricultural methods with advanced technological techniques that could lead to eco-friendly, sustainable, and efficient agrifood systems.

This Special Issue, “Innovative Methods and Technology for Resilience Agrifood Systems”, aims to cover the recent developments and the latest research in resilience farming systems, agricultural productions, and modern techniques in post-harvest food handling and processing, storage, and aggregation. We specifically target the development and application of novel and innovative technologies that provide insight into sustainable and smart agrifood systems. Contributions are expected to cover techniques such as advanced sensing systems, agricultural automation, and robotics, agrifood electronics, crop innovation technology, information and communication technology, data analytics, and computational intelligence. In addition, review papers providing critical overviews of state-of-the-art developments on smart agrifood systems research are welcomed. Topics include, but are not limited to:

  • Smart Farming;
  • Precision Agriculture;
  • Data-driven Agrifood Systems;
  • Crop Innovation Technology;
  • Smart Food Processing and Handling;
  • Nondestructive methods for Agrifood Quality Assessment.

Dr. Alfadhl Alkhaled
Dr. Akinbode A. Adedeji
Dr. Samsuzana Abd Aziz
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

  • smart farming
  • precision agriculture
  • data-driven agrifood systems
  • crop innovation technology
  • smart food processing and handling
  • nondestructive methods for agrifood quality assessment

Published Papers (2 papers)

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Research

17 pages, 6791 KiB  
Article
Electron Beam Irradiation to Control Rhizoctonia solani in Potato
by Natalya Chulikova, Anna Malyuga, Polina Borshchegovskaya, Yana Zubritskaya, Victoria Ipatova, Alexander Chernyaev, Dmitry Yurov, Sergei Zolotov, Alexander Nikitchenko, Ulyana Bliznyuk and Igor Rodin
Agriculture 2023, 13(6), 1221; https://doi.org/10.3390/agriculture13061221 - 09 Jun 2023
Cited by 3 | Viewed by 1236
Abstract
This study focuses on the influence of pre-planting irradiation on the development, health, and yield of seed potatoes infected with Rhizoctonia solani. The research was prompted by the need to ensure crop security and sustainability in the modern-day environment, which calls into [...] Read more.
This study focuses on the influence of pre-planting irradiation on the development, health, and yield of seed potatoes infected with Rhizoctonia solani. The research was prompted by the need to ensure crop security and sustainability in the modern-day environment, which calls into question the future sufficiency of crop yields. Considering that the focus has shifted to non-chemical methods of crop treatment at all plant development stages in response to more stringent regulations governing potato production, it is particularly important to refine physics-based methods to suppress fungal diseases caused by Rhizoctonia solani. Irradiation of tubers with 20–150 Gy inhibited the potato development phases and the doses exceeding 150 Gy completely suppressed the potato sprouting. Doses ranging from 20 Gy to 100 Gy decreased the quantity of large tubers by 10–20% on average while the number of medium and small tubers increased by 5–15% and 3–10%, respectively. Irradiation of seed potatoes also decreased the sclerotia and non-sclerotia forms of diseases caused by Rhizoctonia solani in the harvested tubers. It was found that 1 MeV electron irradiation with doses ranging from 20 Gy to 30 Gy is the most efficient for the pre-planting treatment of seed potatoes since the penetration of low-energy accelerated electrons into the upper layers of potato tubers ensures the suppression of diseases caused by Rhizoctonia solani by at least 10% from the value of non-irradiated samples and prevents the reduction of total yield allowing for a maximum of 25% loss. Full article
(This article belongs to the Special Issue Innovative Methods and Technology for Resilience Agrifood Systems)
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15 pages, 1302 KiB  
Article
Application of Computational Intelligence in Describing Dust Emissions in Different Soil Tillage Applications in Middle Anatolia
by Kazım Çarman, Alper Taner, Fariz Mikailsoy, Kemal Çağatay Selvi, Nicoleta Ungureanu and Nicolae-Valentin Vlăduț
Agriculture 2023, 13(5), 1011; https://doi.org/10.3390/agriculture13051011 - 04 May 2023
Cited by 1 | Viewed by 1054
Abstract
Soil degradation is an increasing problem in Turkey, especially in the Middle Anatolia region where the annual precipitation is approximately 300 mm, resulting from conventional farming methods. To address this issue, the artificial neural networks (ANNs) are used, as they are flexible mathematical [...] Read more.
Soil degradation is an increasing problem in Turkey, especially in the Middle Anatolia region where the annual precipitation is approximately 300 mm, resulting from conventional farming methods. To address this issue, the artificial neural networks (ANNs) are used, as they are flexible mathematical tools that capture data. This study aims to investigate the relationships between dust emission (PM10) and the mean weight diameter, shear stress, and stubble amount of the soil, which were measured in eight different tillage practices (conventional tillage, six types of reduced tillage, and direct seeding). The results show that the mean weight diameter, shear stress, and stubble amount of the soil varied between 4.89 and 14.17 mm, 0.40–1.23 N·cm−2, and 30.5–158 g·m−2, respectively, depending on the type of tillage works. Additionally, dust emissions generated during different tillage applications ranged from 27.73 to 153.45 mg·m−3. The horizontal shaft rototiller produced the highest dust emission, approximately 150% higher than those of disc harrow and winged chisel plows. The impact of tillage practices on dust emission was statistically significant (p < 0.01). A sophisticated 3-(7-7)-1 ANNs model using a backpropagation learning algorithm was developed to predict the concentration of dust, which outperformed the traditional statistical models. The model was based on the values of mean weight diameter, shear stress, and stubble amount of the soil after tillage. The best result was obtained from the ANN model among the polynomial and ANN models. In the ANN model, the coefficient of determination, root mean square error, and mean error were found to be 0.98, 6.70, and 6.11%, respectively. This study demonstrated the effectiveness of ANNs in predicting the levels of dust concentration based on soil tillage data, and it highlighted the importance of adopting alternative tillage practices to reduce soil degradation and dust emissions. Full article
(This article belongs to the Special Issue Innovative Methods and Technology for Resilience Agrifood Systems)
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