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Agronomy
Special Issues
Innovations in Agriculture for Sustainable Agro-Systems
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Innovations in Agriculture for Sustainable Agro-Systems

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: 30 April 2025 | Viewed by 13455

Special Issue Editors

Dr. Ioannis Vagelas

E-Mail Website
Guest Editor
Department of Agriculture Crop Production and Rural Environment, University of Thessaly, 382 21 Volos, Greece
Interests: plant pathology; biological control; applied statistics
Special Issues, Collections and Topics in MDPI journals
Dr. Christos Lykas

E-Mail Website
Guest Editor
Department of Agriculture Crop Production and Rural Environment, University of Thessaly, 382 21 Volos, Greece
Interests: plant growth models; soilless culture; biodiversity; biostimulants
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Agriculture has changed dramatically and has been improved due to new technologies. Smart technologies, such as artificial intelligence, robotics, and the Internet of Things, play an important role in achieving enhanced productivity. However, their implications on the ecosystem are unknown or underestimated.

In addition to favoring production, innovations in agriculture may have many positive environmental impacts such as reductions in agrochemicals application, saving water and energy, waste reduction, and preventing water, soil, and air pollution.

Undoubtedly, there are no shortages of uses for these technologies; multispectral cameras, sensors, and drones are combined with appropriate software and robotic or conventional systems to remove weeds or for the precise application of herbicides and fertilizers. Smart agriculture approaches already include disease prediction models to adjust the greenhouse environment or reduce infections to aid growers in early disease detection.

However, some of the smart technologies that are already in use may have undesirable impacts on the environment, as well as on wider society. For this, a responsible innovation should be further developed in order provide the most benefits in agriculture, while at the same time, it should be environmentally friendly. In light of this method of development, the possibilities and limitations of innovations should be explored.

This Special Issue aims to highlight responsible innovation and contribute to the further development of new ideas for the establishment of sustainable agro-systems.

Dr. Ioannis Vagelas
Dr. Christos Lykas
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

  • precision and smart agriculture
  • environmental impact
  • process-based crop model
  • climate change
  • open and big data
  • predictive and decision support systems
  • IoT for biodiversity and sustainability
  • Internet of Things
  • robot–plant interaction
  • remote sensing
  • monitoring and control of diseases, pests and weeds
  • plant detection and monitoring

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (5 papers)

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Editorial

Jump to: Research, Review

7 pages, 1987 KiB  
Editorial
Innovations in Agriculture for Sustainable Agro-Systems
by Christos Lykas and Ioannis Vagelas
Agronomy 2023, 13(9), 2309; https://doi.org/10.3390/agronomy13092309 - 1 Sep 2023
Cited by 2 | Viewed by 1473
Abstract
Agriculture has changed dramatically and has been improved due to new technologies [...] Full article
(This article belongs to the Special Issue Innovations in Agriculture for Sustainable Agro-Systems)
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Research

Jump to: Editorial, Review

22 pages, 8136 KiB  
Article
Application of Drone Surveillance for Advance Agriculture Monitoring by Android Application Using Convolution Neural Network
by Sabab Ali Shah, Ghulam Mustafa Lakho, Hareef Ahmed Keerio, Muhammad Nouman Sattar, Gulzar Hussain, Mujahid Mehdi, Rahim Bux Vistro, Eman A. Mahmoud and Hosam O. Elansary
Agronomy 2023, 13(7), 1764; https://doi.org/10.3390/agronomy13071764 - 29 Jun 2023
Cited by 26 | Viewed by 5428
Abstract
Plant diseases are a significant threat to global food security, impacting crop yields and economic growth. Accurate identification of plant diseases is crucial to minimize crop loses and optimize plant health. Traditionally, plant classification is performed manually, relying on the expertise of the [...] Read more.
Plant diseases are a significant threat to global food security, impacting crop yields and economic growth. Accurate identification of plant diseases is crucial to minimize crop loses and optimize plant health. Traditionally, plant classification is performed manually, relying on the expertise of the classifier. However, recent advancements in deep learning techniques have enabled the creation of efficient crop classification systems using computer technology. In this context, this paper proposes an automatic plant identification process based on a synthetic neural network with the ability to detect images of plant leaves. The trained model EfficientNet-B3 was used to achieve a high success rate of 98.80% in identifying the corresponding combination of plant and disease. To make the system user-friendly, an Android application and website were developed, which allowed farmers and users to easily detect diseases from the leaves. In addition, the paper discusses the transfer method for studying various plant diseases, and images were captured using a drone or a smartphone camera. The ultimate goal is to create a user-friendly leaf disease product that can work with mobile and drone cameras. The proposed system provides a powerful tool for rapid and efficient plant disease identification, which can aid farmers of all levels of experience in making informed decisions about the use of chemical pesticides and optimizing plant health. Full article
(This article belongs to the Special Issue Innovations in Agriculture for Sustainable Agro-Systems)
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13 pages, 9110 KiB  
Article
Monitoring Patch Expansion Amends to Evaluate the Effects of Non-Chemical Control on the Creeping Perennial Cirsium arvense (L.) Scop. in a Spring Wheat Crop
by Marian Malte Weigel, Sabine Andert and Bärbel Gerowitt
Agronomy 2023, 13(6), 1474; https://doi.org/10.3390/agronomy13061474 - 26 May 2023
Cited by 4 | Viewed by 1608
Abstract
The creeping perennial weed species Cirsium arvense (L.) Scop. occurs in patches. Expanding creeping roots allow these patches to increase their covered area. This characteristic has rarely been addressed when investigating the effects of control options in arable fields. We designed a three-year [...] Read more.
The creeping perennial weed species Cirsium arvense (L.) Scop. occurs in patches. Expanding creeping roots allow these patches to increase their covered area. This characteristic has rarely been addressed when investigating the effects of control options in arable fields. We designed a three-year field experiment (2019–2021) in north-eastern Germany, accounting for existing patch patterns. The experimental setup included an untreated control, a competition treatment (cover crop, CC), two disturbance treatments by mouldboard ploughing (PL), root cutting (RC), and four combined applications (RC + CC, PL + CC, PL + RC, PL + RC + CC). Root cutting was performed by a prototype tillage machine produced by “Kverneland”. Plots were defined by the species growth pattern and mapped by GPS and UAV. The experiment investigates the thistle response variables: “Expansion”, “Density”, “Coverage”, and “Height”. Treatments including disturbance by ploughing (PL, PL + CC, PL + RC, PL + RC + CC) reduced “Density” by the factor 0.15 and “Expansion” by 0.25, while those without ploughing (CC, RC, RC + CC) only reduced “Density” by the factor 0.68 and “Expansion” by 0.71. Adding root cuttings or cover crops did not further increase the reduction effect of ploughing. Treatments with competition by cover crops impacted “Expansion” more clearly than “Density”. When cover crops were combined with root cutting (RC + CC), “Expansion” was almost additively reduced, resulting in a reduction comparable to that of ploughing. The “Height” of the shoots was significantly reduced in four treatments (PL, RC + CC, PL + RC, PL + RC + CC), while “Coverage” did not change significantly. UAV patch monitoring proved to be accurate enough for thistle “Expansion” but not for thistle “Density” within the patch. The results of this study demand innovative research when controlling patch-forming creeping perennial weeds. The need for patches will limit small-scale experimental set ups. Full article
(This article belongs to the Special Issue Innovations in Agriculture for Sustainable Agro-Systems)
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Review

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26 pages, 2925 KiB  
Review
Crops Disease Detection, from Leaves to Field: What We Can Expect from Artificial Intelligence
by Youssef Lebrini and Alicia Ayerdi Gotor
Agronomy 2024, 14(11), 2719; https://doi.org/10.3390/agronomy14112719 - 18 Nov 2024
Viewed by 514
Abstract
Agriculture is dealing with numerous challenges of increasing production while decreasing the amount of chemicals and fertilizers used. The intensification of agricultural systems has been linked to the use of these inputs which nevertheless have negative consequences for the environment. With new technologies, [...] Read more.
Agriculture is dealing with numerous challenges of increasing production while decreasing the amount of chemicals and fertilizers used. The intensification of agricultural systems has been linked to the use of these inputs which nevertheless have negative consequences for the environment. With new technologies, and progress in precision agriculture associated with decision support systems for farmers, the objective is to optimize their use. This review focused on the progress made in utilizing machine learning and remote sensing to detect and identify crop diseases that may help farmers to (i) choose the right treatment, the most adapted to a particular disease, (ii) treat diseases at early stages of contamination, and (iii) maybe in the future treat only where it is necessary or economically profitable. The state of the art has shown significant progress in the detection and identification of disease at the leaf scale in most of the cultivated species, but less progress is done in the detection of diseases at the field scale where the environment is complex and applied only in some field crops. Full article
(This article belongs to the Special Issue Innovations in Agriculture for Sustainable Agro-Systems)
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27 pages, 2820 KiB  
Review
Agroclimatic and Phytosanitary Events and Emerging Technologies for Their Identification in Avocado Crops: A Systematic Literature Review
by Tomas Ramirez-Guerrero, Maria Isabel Hernandez-Perez, Marta S. Tabares, Alejandro Marulanda-Tobon, Eduart Villanueva and Alejandro Peña
Agronomy 2023, 13(8), 1976; https://doi.org/10.3390/agronomy13081976 - 26 Jul 2023
Cited by 3 | Viewed by 3011
Abstract
Avocado is one of the most commercialized and profitable fruits in the international market. Its cultivation and production are centered in countries characterized by tropical and subtropical climatic conditions, many of them with emerging economies. Moreover, the use of technology is key to [...] Read more.
Avocado is one of the most commercialized and profitable fruits in the international market. Its cultivation and production are centered in countries characterized by tropical and subtropical climatic conditions, many of them with emerging economies. Moreover, the use of technology is key to agricultural production improvement strategies. Using avocado crop data to forecast the potential impacts of biotic and abiotic factors, combined with smart farming technologies, growers can apply measures during a single production phase to reduce the risks caused by pests and weather variations. Therefore, this paper aims to distinguish the most relevant variables related to agroclimatic and phytosanitary events in avocado crops, their incidence on production and risk management, as well as the emerging technologies used for the identification and analysis of pests and diseases in avocados. A scientific literature search was performed, and the first search found 608 studies, and once the screening process was applied, 37 papers were included in this review. In the results, three research questions were answered that described the pests and diseases with high impact on avocado production, along with the data sources and the principal enabling technologies used in the identification of agroclimatic and phytosanitary events in avocados. Some challenges and trends in the parameterization of the technology in field conditions for data collection are also highlighted. Full article
(This article belongs to the Special Issue Innovations in Agriculture for Sustainable Agro-Systems)
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