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Emerging Technologies for Precision 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: closed (20 October 2025) | Viewed by 3662

Special Issue Editor


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Guest Editor
Department of Biosystems Engineering and Precision Technology, Albert Kázmér Mosonmagyaróvár Faculty of Agricultural and Food Sciences, Széchenyi István University, H-9200 Mosonmagyaróvár, Hungary
Interests: sustainable agriculture; crop production; plant nutrition; crop management; precision agriculture; IoT and AI in crop management
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Special Issue Information

Dear Colleagues,

Currently, the pace of increase in environmental pollution caused by food production is more rapid than the formulation of answers by science. The two fundamental tasks are as follows: 1. to meet sustainability criteria within the production units and 2. to continuously expand the technical information systems (IoT, WSN, drone monitoring) to enhance the synergy of natural and agricultural areas. The intensive development of IoT is increasingly leading us to talk about IoE (Internet of Everything). Through IoT, we can also examine the relationship network of events that are spatially and temporally distant from each other. One example is when pests (insects) appear up to 40–50 km away from our crop area, and the IoT system provides detailed information about the characteristics of the insects and makes recommendations for pest control. There is also continuous signaling of the direction and speed at which insect swarms are moving. In this area, M2M, decision making without human intervention, can also appear. The two major areas of sensor development that are outstanding are as follows: 1. Lab2Field, where in the laboratory, in addition to fixed environmental characteristics, precisely functioning instruments are adapted to field conditions, such as the instrument (artificial tongue, nose, and ear) and 2. Chipless, i.e., wireless sensors that degrade in the soil or in human and animal bodies. The latter are significant for frozen foods. Nanotechnology opens new windows in the development of agricultural sensors. All the above contribute to Hands Free Hectar (Harper Adams University, UK), unmanned cultivation technology, which can reduce environmental pollution without significantly affecting yields. On the other hand, there is a growing demand for reducing soil compaction. Seeder robots operating in rows, small smart data-gathering robots that can also perform actuator tasks, such as sampling and causing minor soil compaction, contribute to the per plant, ultra-precise platform.

Prof. Dr. Miklós Neményi
Guest Editor

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Keywords

  • IoT (internet of things)
  • WSN (wireless sensor network)
  • F2F (from farm to fork)
  • M2M (machine to machine)
  • Lab2Field (from laboratory to field)
  • small smart robots to reduce the soil compaction

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Published Papers (2 papers)

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Research

16 pages, 2307 KB  
Article
Daily Light Integral (DLI) Mapping Challenges in a Central European Country (Slovakia)
by Anusha Kundathil, Zsófia Varga, Kornél Szalay, László Sipos and András Jung
Appl. Sci. 2025, 15(22), 12254; https://doi.org/10.3390/app152212254 - 18 Nov 2025
Cited by 2 | Viewed by 1085
Abstract
The role of customized DLI maps in optimizing lighting strategies for controlled and open field crop production is gradually increasing, resulting in the creation of specialized DLI maps for more countries. Daily Light Integral (DLI) [mol·m−2·d−1] is an accumulation [...] Read more.
The role of customized DLI maps in optimizing lighting strategies for controlled and open field crop production is gradually increasing, resulting in the creation of specialized DLI maps for more countries. Daily Light Integral (DLI) [mol·m−2·d−1] is an accumulation or integration of quantum flux measurements per second over one day (24 h), its spatial distribution will be visualized on maps. Our research objectives are: (1) to create 1 mol·m−2·d−1 resolution Slovakia DLI map and explore the seasonal and regional characteristics, (2) to create 2 and 5 mol·m−2·d−1 resolution DLI maps to show how the spatial resolution capabilities change in a local (country) and regional (Europe) context, (3) to summarize and compare the seasonal patterns for mountainous and lowland areas with characteristic DLI values (minimum, maximum, average, range). The current study shows how much light was available at different times of the year using monthly DLI threshold maps for 1 mol·m−2·d−1, 2 mol·m−2·d−1, and 5 mol·m−2·d−1. The data present a clear seasonal and regional pattern. In the seasons, the monthly total DLI maximum and minimum differences reached: 21 DLI units (38–17 mol·m−2·d−1) in spring, 17 DLI units (46–17 mol·m−2·d−1) in summer, 20 DLI units (26–6 mol·m−2·d−1) in autumn, 9 DLI units (13–4 mol·m−2·d−1) in winter. Slovakia is an East–West oriented country, which explains the use of the 1 mol·m−2·d−1 DLI map. DLI maps are of particular importance for plant cultivation technologies that are sensitive to the amount of light and its temporal and spatial distribution, such as greenhouse vegetables or certain fruit species. Spatial DLI data support lighting strategy and design, supplemented by lighting, shading management, and photosynthetically active radiation (PAR) availability and efficient use. Full article
(This article belongs to the Special Issue Emerging Technologies for Precision Agriculture)
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13 pages, 1827 KB  
Article
Soil Moisture Content Prediction Using Gradient Boosting Regressor (GBR) Model: Soil-Specific Modeling with Five Depths
by Tarek Alahmad, Miklós Neményi and Anikó Nyéki
Appl. Sci. 2025, 15(11), 5889; https://doi.org/10.3390/app15115889 - 23 May 2025
Cited by 8 | Viewed by 1857
Abstract
Monitoring soil moisture content (SMC) remains challenging due to its spatial and temporal variability. Accurate SMC prediction is essential for optimizing irrigation and enhancing water use efficiency. In this research, a Gradient Boosting Regressor (GBR) model was developed and validated to predict SMC [...] Read more.
Monitoring soil moisture content (SMC) remains challenging due to its spatial and temporal variability. Accurate SMC prediction is essential for optimizing irrigation and enhancing water use efficiency. In this research, a Gradient Boosting Regressor (GBR) model was developed and validated to predict SMC in two soil textures, loam and silt loam, using meteorological data from Internet of Things (IoT) sensors and gravimetric SMC field measurements collected from five different depths. The statistical analysis revealed significant variation in SMC across depths in loam soil (p < 0.05), while silt loam exhibited more stable moisture distribution. The GBR model demonstrated high performance in both soil textures, achieving R2 values of 0.98 and 0.94 for silt loam and loam soils, respectively, with low prediction errors (RMSE 0.85 and 0.97, respectively). Feature importance analysis showed that precipitation and humidity were the most influential features in loam soil, while solar radiation had the highest impact on prediction in silt loam soil. Soil depth also showed a significant contribution to SMC prediction in both soils. These results highlight the necessity for soil-specific modeling to enhance SMC prediction accuracy, optimize irrigation systems, and support water resources management approaches aligning with SDG6 objectives. Full article
(This article belongs to the Special Issue Emerging Technologies for Precision Agriculture)
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