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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: closed (25 November 2025) | Viewed by 10505

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 250 words) can be sent to the Editorial Office for assessment.

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 (6 papers)

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Research

23 pages, 4782 KB  
Article
Cattle Farming Activity Monitoring Using Advanced Deep Learning Approach
by Muhammad Asim, Bareera Anam, Muhammad Nadeem Ali and Byung-Seo Kim
Sensors 2026, 26(3), 785; https://doi.org/10.3390/s26030785 (registering DOI) - 24 Jan 2026
Abstract
Technological advancements have significantly improved cattle farming, particularly in sensor-based activity monitoring for health management, estrus detection, and overall herd supervision. However, such a sensor-based monitoring framework often illustrates several issues, such as high cost, animal discomfort, and susceptibility to false measurement. This [...] Read more.
Technological advancements have significantly improved cattle farming, particularly in sensor-based activity monitoring for health management, estrus detection, and overall herd supervision. However, such a sensor-based monitoring framework often illustrates several issues, such as high cost, animal discomfort, and susceptibility to false measurement. This study introduces a vision-based cattle activity monitoring approach deployed in a commercial Nestlé dairy farm, specifically one that is estrus-focused, where overhead cameras capture unconstrained herd behavior under variable lighting, occlusions, and crowding. A custom dataset of 2956 Images are collected and then annotated into four fine-grained behaviors—standing, lying, grazing, and estrus—enabling detailed analysis beyond coarse activity categories commonly used in prior livestock monitoring studies. Furthermore, computer vision-based deep learning algorithms are deployed on this dataset to classify the aforementioned classes. A comparative analysis of YOLOv8 and YOLOv9 is provided, which clearly illustrates that YOLOv8-L achieved a mAP of 91.11%, whereas YOLOv9-E achieved a mAP of 90.23%. Full article
(This article belongs to the Special Issue Smart Decision Systems for Digital Farming: 2nd Edition)
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29 pages, 5866 KB  
Article
StaticPigDetv2: Performance Improvement of Unseen Pig Monitoring Environment Using Depth-Based Background and Facility Information
by Seungwook Son, Munki Park, Sejun Lee, Jongwoong Seo, Seunghyun Yu, Daihee Park and Yongwha Chung
Sensors 2026, 26(2), 621; https://doi.org/10.3390/s26020621 - 16 Jan 2026
Viewed by 191
Abstract
Standard Deep Learning-based detectors generally face a trade-off between accuracy and latency, as well as a significant performance degradation when applied to unseen environments. To address these challenges, this study proposes a method that enhances both accuracy and latency by leveraging the static [...] Read more.
Standard Deep Learning-based detectors generally face a trade-off between accuracy and latency, as well as a significant performance degradation when applied to unseen environments. To address these challenges, this study proposes a method that enhances both accuracy and latency by leveraging the static characteristics of fixed-camera pig pen monitoring. Specifically, we utilize background and infrastructure information obtained through a one-time preprocessing step upon camera installation. By integrating this information, we introduce three distinct modules, Background-suppressed Image Generator (BIG), Facility Image Generator (FIG), and Background Suppression Integration (BSI), that improve detection accuracy and operational efficiency without the need for model retraining. BIG creates background-suppressed images that integrate foreground and background information. FIG creates facility mask images that can be used to identify pigs that are occluded by facilities, enabling more efficient learning in unseen environments. BSI leverages both the input image and the background-suppressed image generated by BIG, feeding them into a 3D convolution layer for efficient feature fusion. This difference-aware fusion helps the model focus on foreground information and gradually reduce the domain gap. After training on the German pig dataset and testing on the unseen Korean Hadong pig dataset, the proposed method could improve AP50 accuracy (from 75% to 86%) and Jetson Orin Nano latency (from 67 ms to 41 ms) compared to the baseline model YOLOV12m. Full article
(This article belongs to the Special Issue Smart Decision Systems for Digital Farming: 2nd Edition)
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24 pages, 37157 KB  
Article
Smart Irrigation with Fuzzy Decision Support Systems in Trentino Vineyards
by Romeo Silvestri, Massimo Vecchio, Miguel Pincheira and Fabio Antonelli
Sensors 2025, 25(23), 7188; https://doi.org/10.3390/s25237188 - 25 Nov 2025
Viewed by 542
Abstract
Efficient water management is a critical challenge for modern agriculture, particularly in the context of increasing climate variability and limited freshwater resources. This study presents a comparative field-based evaluation of two fuzzy-logic-based irrigation decision support systems for vineyard management: a Mamdani-type controller with [...] Read more.
Efficient water management is a critical challenge for modern agriculture, particularly in the context of increasing climate variability and limited freshwater resources. This study presents a comparative field-based evaluation of two fuzzy-logic-based irrigation decision support systems for vineyard management: a Mamdani-type controller with expert-defined rules and a Takagi–Sugeno system designed to enable automated learning from ultra-local historical field data. Both systems integrate soil moisture sensing, short-term forecasting, and weather predictions to provide optimized irrigation recommendations. The evaluation combines counterfactual simulations with a bootstrap-based statistical analysis to assess water use efficiency, soil moisture control, and robustness to environmental variability. The comparison highlights distinct strengths of the two approaches, revealing trade-offs between water conservation and crop stress mitigation, and offering practical insights for the design and deployment of intelligent irrigation management solutions. Full article
(This article belongs to the Special Issue Smart Decision Systems for Digital Farming: 2nd Edition)
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27 pages, 10832 KB  
Article
Discrete Time Series Forecasting in Non-Invasive Monitoring of Managed Honey Bee Colonies: Part II: Are Hive Weight and In-Hive Temperature Seasonal and Colony-Specific?
by Vladimir A. Kulyukin, Aleksey V. Kulyukin and William G. Meikle
Sensors 2025, 25(14), 4319; https://doi.org/10.3390/s25144319 - 10 Jul 2025
Viewed by 1025
Abstract
We explored the stationarity, trend, and seasonality of the hive weight and in-hive temperature of ten managed honey bee (Apis mellifera) colonies at a research apiary of the Carl Hayden Bee Research Center in Tucson, Arizona, USA. The hives were monitored [...] Read more.
We explored the stationarity, trend, and seasonality of the hive weight and in-hive temperature of ten managed honey bee (Apis mellifera) colonies at a research apiary of the Carl Hayden Bee Research Center in Tucson, Arizona, USA. The hives were monitored with electronic scales and in-hive temperature sensors from June to October 2022. The weight and temperature were recorded every five minutes around the clock. The collected data were curated into 2160 timestamped weight and 2160 timestamped temperature observations. We performed a systematic autoregressive integrated moving average (ARIMA) time series analysis to answer three fundamental questions: (a) Does seasonality matter in the ARIMA forecasting of hive weight and in-hive temperature? (b) To what extent do the best forecasters of one hive generalize to other hives? and (c) Which time series type (i.e., hive weight or in-hive temperature) is better predictable? Our principal findings were as follows: (1) The hive weight and in-hive temperature series were not white noise, were not normally distributed, and, for most hives, were not difference- or trend-stationary; (2) Seasonality matters, in that seasonal ARIMA (SARIMA) forecasters outperformed their ARIMA counterparts on the curated dataset; (3) The best hive weight and in-hive temperature forecasters of the ten monitored colonies appeared to be colony-specific; (4) The accuracy of the hive weight forecasts was consistently higher than that of the in-hive temperature forecasts; (5) The weight and temperature forecasts exhibited common qualitative patterns. Full article
(This article belongs to the Special Issue Smart Decision Systems for Digital Farming: 2nd Edition)
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23 pages, 12985 KB  
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
Cited by 9 | Viewed by 3567
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 KB  
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
Cited by 6 | Viewed by 4015
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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