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Digital Technologies in Smart Agriculture

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Agricultural Science and Technology".

Deadline for manuscript submissions: 20 August 2026 | Viewed by 1287

Special Issue Editors


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Guest Editor
Department of Biosystems Engineering, Poznań University of Life Sciences, ul. Wojska Polskiego 50, 60-627 Poznań, Poland
Interests: modern agricultural equipment; use of agricultural machinery; digital-smart agriculture; engineering of crop production processes; postharvest technologies and process engineering; sustainable agriculture; biosystems engineering
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Biosystems Engineering, Faculty of Environmental Engineering and Mechanical Engineering, Poznan University of Life Sciences, Poznan, Poland
Interests: modern agricultural equipment; use of agricultural machinery; postharvest technologies and process engineering; biomass energy; biosystems engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sustainable agriculture supports the conservation of natural resources, halts biodiversity loss, and reduces greenhouse gas emissions. Sustainable agriculture is a combination of the best conventional technologies and practices, offering precision farming concepts and digital technologies. The aim of digitalization is to optimize and increase the quality of production and reduce human labor, industrial inputs, and environmental pressures. Today, in the era of Agriculture 4.0, the latest digital technology tools are being employed, namely, the Internet of Things (IoT), Big Data analytics, artificial intelligence, machine learning, satellite technologies, cloud computing, etc. New technologies are key to enabling the development of sustainable agriculture. As such, one of the most important technologies is the Internet of Things (IoT). This concept of connecting devices and collecting and processing the Big Data received from them allows for the continuous creation of streams of interconnected data as well as the creation of new information.

Smart agriculture emphasizes information and communication technologies in machines, equipment, and sensors. This not only allows for high-tech farm monitoring and the automation of processes but also provides a possibility for remotely controlling both processes and work. Currently, these solutions are being used, among others, to monitor changes in soil characteristics, climatic factors, and humidity.

Innovative digital technologies, i.e., satellite technologies, cloud computing, or artificial intelligence, are expected to contribute to agricultural development, greater production efficiency, and resource savings, as well as promote food security and reduce climate change. Digitization and the implementation of new technologies seems to be a natural progression for agricultural development. However, the application of new technologies also raises certain concerns and poses new challenges for farmers.

Therefore, this Special Issue will review a wide range of theoretical and experimental research related to agricultural production processes that implement innovative digital technologies, the possibilities of their application, and the assessment of their effectiveness, as well as the anticipated challenges in combining innovative technologies with conventional agricultural activities.

This Special Issue welcomes all types of articles and is intended for a broad and multidisciplinary audience. 

Prof. Dr. Jacek Przybył
Dr. Dawid Wojcieszak
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. Applied Sciences 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 2400 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

  • sustainable agriculture
  • smart agriculture
  • data storage and processing
  • Internet of Things (IoT)
  • big data analytics
  • artificial intelligence
  • machine learning
  • cloud computing
  • satellite technologies
  • predictive models
  • remote control of processes and works
  • automation and autonomy
  • field management
  • reduction in fertilizer and pesticide use
  • reduction in energy inputs
  • increased crop production
  • increase in production efficiency
  • reduction in climate change

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Published Papers (2 papers)

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Research

26 pages, 54986 KB  
Article
Use of Sentinel-2 Images to Elaborate a VRT Sensor-Based and Map-Based Nitrogen Fertilization in Wheat and Barley Crops
by Patricia Arizo-García, Sergio Castiñeira-Ibáñez, Daniel Tarrazó-Serrano, Belén Franch, Constanza Rubio and Alberto San Bautista
Appl. Sci. 2025, 15(21), 11646; https://doi.org/10.3390/app152111646 - 31 Oct 2025
Viewed by 362
Abstract
Precision agriculture can determine the amount of nitrogen (N) required in each area to optimize yield and nitrogen use efficiency (NUE). The use of variable rate technology (VRT) for planning N fertilization has often relied on techniques that are unfeasible for farmers with [...] Read more.
Precision agriculture can determine the amount of nitrogen (N) required in each area to optimize yield and nitrogen use efficiency (NUE). The use of variable rate technology (VRT) for planning N fertilization has often relied on techniques that are unfeasible for farmers with limited resources. This study aims to present a variable fertilization plan for wheat and barley, along with a protocol to determine the optimal timing for the second nitrogen (N) application, thereby minimizing the need for in situ crop monitoring. Two approaches are studied: a more straightforward sensor-based method and a map-based method. The sensor-based approach involved modeling the maximum NDVI based on the observed value at the time of application and the required N level, achieving an R2 of 0.55 ± 0.06 and 0.72 ± 0.04, an MAE of 0.025 ± 0.002 and 0.039 ± 0.002, and an RMSE of 0.049 ± 0.007 and 0.055 ± 0.004 for wheat and barley, respectively. The map-based approach relied on training models to estimate the nitrogen dose to be applied based on the target yield and reflectance data from Sentinel-2 at the time of application. Using random forest algorithms, an R2 of 0.97 ± 0.01 and 0.96 ± 0.02, an MAE of 3.33 ± 0.20 kg N ha−1 and 2.01 ± 0.13 kg N ha−1, and an RMSE of 4.79 ± 0.31 kg N ha−1 and 3.27 ± 0.58 kg N ha−1 for wheat and barley, respectively. Full article
(This article belongs to the Special Issue Digital Technologies in Smart Agriculture)
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21 pages, 1456 KB  
Article
PSCDR-BMPNet: A Point-Supervised Contrastive Deep Regression Network for Point Cloud Biomass Prediction
by Yi Wang, Hao Peng, Cheng Ouyang, Ruofan Zhang, Mingyu Tan, Wenwu Hu and Pin Jiang
Appl. Sci. 2025, 15(14), 7671; https://doi.org/10.3390/app15147671 - 9 Jul 2025
Viewed by 670
Abstract
Accurate assessment of above-ground biomass (AGB) is essential for optimizing crop growth and enhancing agricultural efficiency. However, predicting above-ground biomass (AGB) presents significant challenges. Traditional point cloud networks often struggle with processing crop structures and data characteristics, hindering their ability to predict biomass [...] Read more.
Accurate assessment of above-ground biomass (AGB) is essential for optimizing crop growth and enhancing agricultural efficiency. However, predicting above-ground biomass (AGB) presents significant challenges. Traditional point cloud networks often struggle with processing crop structures and data characteristics, hindering their ability to predict biomass accurately. To address these limitations, we propose a point-supervised contrastive deep regression method (PSCDR) and a novel network, BMP_Net (BioMixerPoint_Net). The PSCDR method leverages the benefits of deep contrastive regression while accounting for the modal differences between point cloud and 2D image data. By incorporating Chamfer distance to measure point cloud similarity, it improves the model’s adaptability to point cloud features. Experimental results on the SGCBP public dataset show that PSCDR significantly reduces prediction errors compared to seven other point cloud models. Furthermore, the BMP_Net network, which integrates the novel PFMixer module and a point cloud downsampling module, effectively captures the relationship between point cloud structure, density, and biomass. The model achieved test results with RMSE, MAE, and MAPE values of 75.92, 63.19, and 0.115, respectively, outperforming PointMixer by 37.94, 30.07, and 0.079. This method provides an efficient biomass monitoring tool for precision agriculture. Full article
(This article belongs to the Special Issue Digital Technologies in Smart Agriculture)
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