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Smart Decision Systems for Digital Farming: 2nd Edition
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Smart Decision Systems for Digital Farming: 2nd Edition

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Smart Agriculture".

Deadline for manuscript submissions: 20 April 2025 | Viewed by 2757

Special Issue Editors

Prof. Dr. Yongwha Chung

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Guest Editor
Department of Computer Convergence Software, Sejong Campus, Korea University, Sejong City 30019, Republic of Korea
Interests: image processing; computer vision; deep learning; smart agriculture; livestock monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. Sungju Lee

E-Mail
Guest Editor
Department of Software, Sangmyung University, Cheonan 31066, Republic of Korea
Interests: image processing; computer vision; meta learning; smart agriculture; fault diagnosis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, agriculture has adopted digital farming with artificial intelligence, which aims to improve the productivity, convenience, and quality of classical farming, which relies on the intuition and experience of farmers. Digital farming technologies enable data-based smart decisions in all fields of agriculture, such as production, distribution, and consumption, to solve agricultural problems faced by rural aging, labor shortages, and climate change and to achieve sustainable agriculture. In the agricultural sector, the term 'Agriculture 5.0' refers to digital farming based on artificial intelligence and the Internet of Things.

This Special Issue welcomes the contribution of studies focusing on the use of recent techniques, including artificial intelligence and the Internet of Things, with the aim of obtaining information related to digital farming. Topics of interest include, but are not limited to, the following:

  • Decision support systems for crop management.
  • Decision support systems for livestock management.
  • Monitoring systems for crop management.
  • Monitoring systems for livestock management.

Prof. Dr. Yongwha Chung
Dr. Sungju Lee
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. Sensors is an international peer-reviewed open access semimonthly 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

  • digital farming, agriculture 5.0
  • crop management, livestock management
  • decision support systems, monitoring systems
  • image processing, signal processing
  • artificial intelligence, Internet of Things

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Related Special Issue

Published Papers (2 papers)

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Research

23 pages, 12985 KiB  
Article
Discrete Time Series Forecasting of Hive Weight, In-Hive Temperature, and Hive Entrance Traffic in Non-Invasive Monitoring of Managed Honey Bee Colonies: Part I
by Vladimir A. Kulyukin, Daniel Coster, Aleksey V. Kulyukin, William Meikle and Milagra Weiss
Sensors 2024, 24(19), 6433; https://doi.org/10.3390/s24196433 - 4 Oct 2024
Viewed by 1010
Abstract
From June to October, 2022, we recorded the weight, the internal temperature, and the hive entrance video traffic of ten managed honey bee (Apis mellifera) colonies at a research apiary of the Carl Hayden Bee Research Center in Tucson, AZ, USA. [...] Read more.
From June to October, 2022, we recorded the weight, the internal temperature, and the hive entrance video traffic of ten managed honey bee (Apis mellifera) colonies at a research apiary of the Carl Hayden Bee Research Center in Tucson, AZ, USA. The weight and temperature were recorded every five minutes around the clock. The 30 s videos were recorded every five minutes daily from 7:00 to 20:55. We curated the collected data into a dataset of 758,703 records (280,760–weight; 322,570–temperature; 155,373–video). A principal objective of Part I of our investigation was to use the curated dataset to investigate the discrete univariate time series forecasting of hive weight, in-hive temperature, and hive entrance traffic with shallow artificial, convolutional, and long short-term memory networks and to compare their predictive performance with traditional autoregressive integrated moving average models. We trained and tested all models with a 70/30 train/test split. We varied the intake and the predicted horizon of each model from 6 to 24 hourly means. Each artificial, convolutional, and long short-term memory network was trained for 500 epochs. We evaluated 24,840 trained models on the test data with the mean squared error. The autoregressive integrated moving average models performed on par with their machine learning counterparts, and all model types were able to predict falling, rising, and unchanging trends over all predicted horizons. We made the curated dataset public for replication. Full article
(This article belongs to the Special Issue Smart Decision Systems for Digital Farming: 2nd Edition)
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26 pages, 3492 KiB  
Article
Image Processing for Smart Agriculture Applications Using Cloud-Fog Computing
by Dušan Marković, Zoran Stamenković, Borislav Đorđević and Siniša Ranđić
Sensors 2024, 24(18), 5965; https://doi.org/10.3390/s24185965 - 14 Sep 2024
Viewed by 1284
Abstract
The widespread use of IoT devices has led to the generation of a huge amount of data and driven the need for analytical solutions in many areas of human activities, such as the field of smart agriculture. Continuous monitoring of crop growth stages [...] Read more.
The widespread use of IoT devices has led to the generation of a huge amount of data and driven the need for analytical solutions in many areas of human activities, such as the field of smart agriculture. Continuous monitoring of crop growth stages enables timely interventions, such as control of weeds and plant diseases, as well as pest control, ensuring optimal development. Decision-making systems in smart agriculture involve image analysis with the potential to increase productivity, efficiency and sustainability. By applying Convolutional Neural Networks (CNNs), state recognition and classification can be performed based on images from specific locations. Thus, we have developed a solution for early problem detection and resource management optimization. The main concept of the proposed solution relies on a direct connection between Cloud and Edge devices, which is achieved through Fog computing. The goal of our work is creation of a deep learning model for image classification that can be optimized and adapted for implementation on devices with limited hardware resources at the level of Fog computing. This could increase the importance of image processing in the reduction of agricultural operating costs and manual labor. As a result of the off-load data processing at Edge and Fog devices, the system responsiveness can be improved, the costs associated with data transmission and storage can be reduced, and the overall system reliability and security can be increased. The proposed solution can choose classification algorithms to find a trade-off between size and accuracy of the model optimized for devices with limited hardware resources. After testing our model for tomato disease classification compiled for execution on FPGA, it was found that the decrease in test accuracy is as small as 0.83% (from 96.29% to 95.46%). Full article
(This article belongs to the Special Issue Smart Decision Systems for Digital Farming: 2nd Edition)
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