Intelligent Systems and Their Applications in Agriculture

A special issue of AgriEngineering (ISSN 2624-7402).

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 35687

Special Issue Editors

Faculty of Agriculture, University of South Bohemia in Ceske Budejovice, Studentska 1668, 370 05 Ceske Budejovice, Czech Republic
Interests: intelligent systems in agriculture; smart agriculture; Agriculture 4.0; optimization of technology processes by computer simulations; agricultural automatization and mechanization; best available technologies in agriculture
Faculty of Agriculture, University of South Bohemia in Ceske Budejovice, Studentska 1668, 370 05 Ceske Budejovice, Czech Republic
Interests: livestock breeding; livestock technologies; intelligent systems in agriculture; Agriculture 4.0

Special Issue Information

Dear Colleagues,

At present, we are witnessing a massive increase in the computing power of electronic components, miniaturization of sensors, a reduction in the energy consumption of computing elements, and finally, an expansion of the possibility of interconnecting them into complicated communication networks. Simultaneously, acquisition costs are reduced to a tolerable limit, which positively affects the payback period of financial investments. This has enabled research and development teams to develop new, so-called physical–cybernetic systems, which offer end-users wholly new and hitherto unknown possibilities. These technological advances are so significant that the changes are referred to as the Fourth Industrial Revolution.

The technologies mentioned above have already been successfully introduced in industry, and they have been in use already for a long time. Agriculture is much more conservative when it comes to new technologies because it does not generate such high profits. Therefore, new technologies are usually introduced in agriculture with some delay behind the industry.

Additionally, introducing physical–cybernetic systems into agriculture is significantly more complicated compared to industrial applications. The technologies in use in the industry work almost exclusively with inanimate objects or with well-conditioned data sets, thanks to which it is possible to algorithmize processes easily. In contrast, complex biological and physical input determinants, such as animals, plants, and weather influences, enter decision-making processes in agriculture, putting much higher demands on the developed system. Intelligent systems, which will find use in agriculture, must therefore be at a higher level than industrial ones. They represent an exciting challenge for research and development teams.

Without doubt, agriculture stands on the threshold of a new era of physical–cybernetic systems. Soon, intelligent systems and their applications will significantly change the way we approach growing crops, livestock farming, landscape management, and technologies. They will even affect our society, mainly social relations.

In this topical Special Issue, we would like to collect reviews and original research papers dealing with research and development of intelligent systems and their application in agriculture. The Special Issue covers almost all areas of agriculture, such as livestock farming, growing crops, technologies, landscape management, etc. Moreover, it includes topics in similar fields such as robotics, automatization, electronics, mechatronics, informatics or artificial intelligence. Therefore, papers focused on related methods such as machine learning, neural networks, machine vision, image processing, big data, IoT, remote sensing, sensors, and many others are also welcome.

We would like to invite you to share with the broad audience of the journal AgriEngineering your experience in the research and development of intelligent systems and their testing in laboratories and application directly in farms. Papers presented in this Special Issue can build on the number of other publications published in this field. Your publications will enlarge the knowledge and skills of humankind in this so crucial area of agriculture.

Prof. Dr. Petr Bartoš
Dr. Lubos Smutny
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. AgriEngineering is an international peer-reviewed open access quarterly 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 1600 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

  • Intelligent systems in agriculture
  • Smart agriculture
  • Agriculture 4.0
  • Physical–cybernetic systems
  • Artificial intelligence
  • Neural networks
  • Machine learning
  • Machine vision
  • Livestock farming
  • Growing crops
  • Landscape management
  • IoT
  • Big data
  • Remote sensing
  • Drones
  • Agricultural engineering
  • Technologies
  • Sensors

Published Papers (4 papers)

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Research

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11 pages, 1058 KiB  
Article
Comparing Reference Evapotranspiration Calculated in ETo Calculator (Ukraine) Mobile App with the Estimated by Standard FAO-Based Approach
by Pavlo Lykhovyd
AgriEngineering 2022, 4(3), 747-757; https://0-doi-org.brum.beds.ac.uk/10.3390/agriengineering4030048 - 13 Aug 2022
Cited by 3 | Viewed by 2215
Abstract
Reference evapotranspiration (ETo) is a key agrometeorological index for rational irrigation management. The standard method for ETo estimation, proposed by the FAO, is based on a complicated Penman–Monteith equation and requires many meteorological inputs, making it difficult for practical use by farmers. At [...] Read more.
Reference evapotranspiration (ETo) is a key agrometeorological index for rational irrigation management. The standard method for ETo estimation, proposed by the FAO, is based on a complicated Penman–Monteith equation and requires many meteorological inputs, making it difficult for practical use by farmers. At present, there are many alternative simplified approaches for ETo estimation; most of them are directed at cutting the number of required meteorological inputs for calculation. Among them, special attention should be paid to the various temperature-based methods of ETo assessment. One of the temperature-based models for ETo computation was realized in the free mobile app ETo Calculator (Ukraine). The app gives Ukrainian farmers an opportunity to assess ETo values on a daily or monthly scale using mean air temperature, obtained through free online meteorological forecasts and archive services, as the only input. The objective of the study was to test the app’s accuracy compared to FAO-based calculations in five key regions of Ukraine, each representing a particular climatic zone of the country. It was established that the app provides relatively good accuracy of ETo estimation even in raw (not adjusted to wind speed and relative air humidity) runs. The results of the statistical comparison with the FAO-calculated values on the daily scale are as follows: R2 within 0.82–0.87, RMSE within 0.74–0.81 mm, MAE within 0.60–0.70 mm, MAPE within 18.07–25.50%, depending on the region. The results of the statistical comparison with the FAO-calculated values on the monthly scale are: R2 within 0.88–0.95, RMSE within 0.50–0.72 mm, MAE within 0.33–0.59 mm, MAPE within 8.96–24.08% depending on the region. The ETo Calculator (Ukraine) is a good alternative to the complicated Penman–Monteith method and could be recommended for Ukrainian farmers to be used for irrigation management. Full article
(This article belongs to the Special Issue Intelligent Systems and Their Applications in Agriculture)
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19 pages, 4348 KiB  
Article
Automatic and Reliable Leaf Disease Detection Using Deep Learning Techniques
by Muhammad E. H. Chowdhury, Tawsifur Rahman, Amith Khandakar, Mohamed Arselene Ayari, Aftab Ullah Khan, Muhammad Salman Khan, Nasser Al-Emadi, Mamun Bin Ibne Reaz, Mohammad Tariqul Islam and Sawal Hamid Md Ali
AgriEngineering 2021, 3(2), 294-312; https://0-doi-org.brum.beds.ac.uk/10.3390/agriengineering3020020 - 20 May 2021
Cited by 124 | Viewed by 12941
Abstract
Plants are a major source of food for the world population. Plant diseases contribute to production loss, which can be tackled with continuous monitoring. Manual plant disease monitoring is both laborious and error-prone. Early detection of plant diseases using computer vision and artificial [...] Read more.
Plants are a major source of food for the world population. Plant diseases contribute to production loss, which can be tackled with continuous monitoring. Manual plant disease monitoring is both laborious and error-prone. Early detection of plant diseases using computer vision and artificial intelligence (AI) can help to reduce the adverse effects of diseases and also overcome the shortcomings of continuous human monitoring. In this work, we propose the use of a deep learning architecture based on a recent convolutional neural network called EfficientNet on 18,161 plain and segmented tomato leaf images to classify tomato diseases. The performance of two segmentation models i.e., U-net and Modified U-net, for the segmentation of leaves is reported. The comparative performance of the models for binary classification (healthy and unhealthy leaves), six-class classification (healthy and various groups of diseased leaves), and ten-class classification (healthy and various types of unhealthy leaves) are also reported. The modified U-net segmentation model showed accuracy, IoU, and Dice score of 98.66%, 98.5%, and 98.73%, respectively, for the segmentation of leaf images. EfficientNet-B7 showed superior performance for the binary classification and six-class classification using segmented images with an accuracy of 99.95% and 99.12%, respectively. Finally, EfficientNet-B4 achieved an accuracy of 99.89% for ten-class classification using segmented images. It can be concluded that all the architectures performed better in classifying the diseases when trained with deeper networks on segmented images. The performance of each of the experimental studies reported in this work outperforms the existing literature. Full article
(This article belongs to the Special Issue Intelligent Systems and Their Applications in Agriculture)
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Review

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12 pages, 2982 KiB  
Review
Agricultural Machinery Telemetry: A Bibliometric Analysis
by Leomar Santos Marques, Gabriel Araújo e Silva Ferraz, João Moreira Neto, Ricardo Rodrigues Magalhães, Danilo Alves de Lima, Jefferson Esquina Tsuchida and Diego Cardoso Fuzatto
AgriEngineering 2022, 4(4), 939-950; https://0-doi-org.brum.beds.ac.uk/10.3390/agriengineering4040060 - 17 Oct 2022
Viewed by 2205
Abstract
Agricultural machinery telemetry collects and shares data, which are sent remotely and become precious information. Thus, accurate and instantaneous monitoring can provide an important base of information for adjusting the parameters of the most diverse mechanized agricultural operations, reducing input costs and maintenance [...] Read more.
Agricultural machinery telemetry collects and shares data, which are sent remotely and become precious information. Thus, accurate and instantaneous monitoring can provide an important base of information for adjusting the parameters of the most diverse mechanized agricultural operations, reducing input costs and maintenance expenses. In recent years, this theme has gained more strength and importance for managing rural properties. Therefore, the present study developed a bipartite bibliometric analysis in two lines of research and described the state of the art of this data collection methodology (via telemetry), presenting its technological evolution. The study presents the evolution and connection of telemetry and the processes of robotization of agricultural operations and automation provided by data collection via telemetry in real time. The main countries, keywords, researchers, institutions, and the Dickson quality index indicate a high growth in the last decade. Thus, the present study contributes to decision making regarding research topics, guidance on the state of the art, and contextualization of telemetry’s importance in current research. Full article
(This article belongs to the Special Issue Intelligent Systems and Their Applications in Agriculture)
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34 pages, 3435 KiB  
Review
Precision Irrigation Management Using Machine Learning and Digital Farming Solutions
by Emmanuel Abiodun Abioye, Oliver Hensel, Travis J. Esau, Olakunle Elijah, Mohamad Shukri Zainal Abidin, Ajibade Sylvester Ayobami, Omosun Yerima and Abozar Nasirahmadi
AgriEngineering 2022, 4(1), 70-103; https://0-doi-org.brum.beds.ac.uk/10.3390/agriengineering4010006 - 01 Feb 2022
Cited by 67 | Viewed by 17120
Abstract
Freshwater is essential for irrigation and the supply of nutrients for plant growth, in order to compensate for the inadequacies of rainfall. Agricultural activities utilize around 70% of the available freshwater. This underscores the importance of responsible management, using smart agricultural water technologies. [...] Read more.
Freshwater is essential for irrigation and the supply of nutrients for plant growth, in order to compensate for the inadequacies of rainfall. Agricultural activities utilize around 70% of the available freshwater. This underscores the importance of responsible management, using smart agricultural water technologies. The focus of this paper is to investigate research regarding the integration of different machine learning models that can provide optimal irrigation decision management. This article reviews the research trend and applicability of machine learning techniques, as well as the deployment of developed machine learning models for use by farmers toward sustainable irrigation management. It further discusses how digital farming solutions, such as mobile and web frameworks, can enable the management of smart irrigation processes, with the aim of reducing the stress faced by farmers and researchers due to the opportunity for remote monitoring and control. The challenges, as well as the future direction of research, are also discussed. Full article
(This article belongs to the Special Issue Intelligent Systems and Their Applications in Agriculture)
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