Tag Archives: precision agriculture

1060-1073 T. Fedoniuk, P. Pyvovar, P. Topolnytskyi, I. Bezvershuk, V. Tereshchuk and I. Puleko
Estimation of temporal and spatial characteristics of oat development parameters using Sentinel-1 backscatter data
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Estimation of temporal and spatial characteristics of oat development parameters using Sentinel-1 backscatter data

T. Fedoniuk¹*, P. Pyvovar¹, P. Topolnytskyi¹, I. Bezvershuk¹, V. Tereshchuk¹ and I. Puleko²

¹Polissia National University, Staryi Blvd., 7, UA10008 Zhytomyr, Ukraine
²Zhytomyr Military Institute named after S.P. Korolyov, Myru Avenue, 22, UA10004 Zhytomyr, Ukraine
*Correspondence: tanyavasiluk2015@gmail.com

Abstract:

The implementation of precision agriculture is an urgent priority for Ukraine’s agricultural sector under climate change and restricted use of unmanned aerial vehicles in border regions. This study aims to clearly define and evaluate the potential of Sentinel-1 radar data in identifying temporal and spatial variations in oat crop density and structure during the growing season under field conditions in Ukraine.  The technique encompassed the acquisition of Sentinel-1 satellite images in VV and VH polarizations, data processing by SNAP, field assessments of height, plant density, and phenological development, along with statistical analysis of the association between satellite data and land observations. The study demonstrated that the reflectance coefficient values in VV and VH polarizations fluctuate according to the oat development phase: a reduction in backscattering was noted at the onset of the growing season, followed by an increase during the stem formation and earing phases. The VH/VV ratio is responsive to variations in moisture, plant biomass, and stress conditions. The modelling demonstrated a substantial correlation among planting rate, herbicide application, and polarization markers. The findings validate the efficacy of Sentinel-1 for monitoring crop structure irrespective of weather conditions. This method enables farmers to obtain dependable information for making decisions regarding crop management, timely fertilizer application, or harvesting. The regression model demonstrated a consistent association with a R²=0.61, suggesting the potential for further research utilizing multi-year data to develop integrated yield forecasting models.

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1572-1582 P. Linna and H. Haapala
Advancing precision agriculture: a case study of open source autosteering with AgOpenGPS and RTKbase
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Advancing precision agriculture: a case study of open source autosteering with AgOpenGPS and RTKbase

P. Linna* and H. Haapala

Jamk University of Applied Sciences, Jamk Institute of Bioeconomy,
Tuumalantie 17, FI 43130 Tarvaala, Finland
*Correspondence: petri.linna@jamk.fi

Abstract:

Precision agriculture increasingly relies on Real-Time Kinematic (RTK) services to perform highly accurate tasks in the field. Robotics are gradually entering farming, demanding precise and reliable correction signals. However, before widespread adoption of autonomous field robots becomes a reality, automated tractors will remain in use for a significant period, becoming progressively more advanced.

The market is currently filled with various manufacturers offering aftermarket autosteering systems, which incrementally bring farmers closer to the functionality of fully autonomous field robots. This study explores open-source solutions for cost-effective autosteering systems and RTK base stations. The project involved retrofitting a single farmer’s tractor with an autosteering system and establishing an RTK base station.

As the pilot progressed, word of the implementation spread, leading to the creation of a dedicated communication channel for interested farmers. This platform has facilitated knowledge sharing and further adoption. Information about the project also reached other regions, inspiring similar initiatives that have significantly increased the number of RTK base stations in just two years.

The results of this project demonstrate a strong demand for alternative solutions. Many farmers lack the financial resources to invest in expensive, proprietary systems or are unwilling to commit to recurring subscription fees. The goal remains the same, regardless of the implementation method, agriculture is moving steadily toward smarter, more precise practices and the eventual adoption of field robotics.

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393-400 H. Haapala, K. Sarvela, J. Kalmari, I. Appelgrén and P. Linna
Evaluating the efficiency, environmental impact, and operator benefits of GPS guidance and autosteer technologies in agricultural field operations
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Evaluating the efficiency, environmental impact, and operator benefits of GPS guidance and autosteer technologies in agricultural field operations

H. Haapala*, K. Sarvela, J. Kalmari, I. Appelgrén and P. Linna

Jamk University of Applied Sciences, Department of Engineering, Institute of Bioeconomy, Tuumalantie 17, FI43130 Tarvaala, Finland
*Correspondence: hannu.haapala@jamk.fi

Abstract:

This study evaluated the benefits of GPS guidance and autosteer technologies in agricultural operations through a three-year field experiment conducted at the Smart Bioeconomy Testbed in Central Finland. Adjacent fields were sown either with or without the use of GPS guidance and autosteer, while all other variables were standardized to isolate the impact of the technologies. The movement of the tractor–seeder combination was precisely tracked using RTK GPS with centimetre-level accuracy, and operational parameters were recorded via ISOBUS, supplemented by external measurements of environmental and agronomic factors.

Key findings demonstrated that GPS-guided autosteer operations reduced total work time by 9.7% (p < 0.01), primarily due to a 21% (p < 0.01) decrease in overlap and unnecessary movement. This operational efficiency translated into a 20% (p < 0.01) reduction in fuel consumption and a corresponding decrease in CO₂ emissions per hectare. Moreover, GPS-based automation produced more uniform traffic patterns, mitigating localized soil compaction. Operator well-being also improved, with a 10% (p < 0.01) reduction in average heart rate, suggesting reduced physical strain. These benefits were particularly significant in small, irregular fields typical of Finnish agriculture.

In conclusion, GPS guidance and autosteer technologies significantly enhance operational efficiency by reducing fuel use, field time, and emissions. These benefits are particularly pronounced in smaller fields, such as those typical in Finland, where improved manoeuvrability yields greater returns. While the technologies contribute positively to operator well-being, individual responses may vary. Further research is needed to assess long-term impacts, explore integration with advanced technologies such as robotics and AI-driven decision support systems, and address the challenges associated with broader adoption.

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435-447 M. Matvieiev, A. Getya, M. Nehrey, T. Yakubets, S. Ruban, O. Nazarko, O.O. Borshch, I. Lastovska, V. Baban and Yu. Mashkin
Optimisation of dairy farming in Ukraine: Integrating modern information technologies for genetic improvement and sustainable herd management
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Optimisation of dairy farming in Ukraine: Integrating modern information technologies for genetic improvement and sustainable herd management

M. Matvieiev¹*, A. Getya², M. Nehrey³, T. Yakubets², S. Ruban², O. Nazarko⁴, O.O. Borshch⁵, I. Lastovska⁵, V. Baban⁵ and Yu. Mashkin⁵

¹National University of Life and Environmental Science of Ukraine, Department of Dairy and Beef Production Technology, 12b Heroiv Oborony Str., UA03041 Kyiv, Ukraine
²National University of Life and Environmental Science of Ukraine, Department of Genetics, Breeding and Biotechnology of Animals, 19 Horikhuvatskyi shliakh Str., UA03041 Kyiv, Ukraine
³National University of Life and Environmental Science of Ukraine, Department of Economic Cybernetics, 16a Heroyiv Oborony Str., UA03041 Kyiv, Ukraine
⁴Global Agro Finance, 50 Tsentralna Str., UA20300, Uman, Ukraine
⁵Bila Tserkva National Agrarian University, Department of Technology Milk and Meat Production, 8/1 Soborna pl., UA09117 Bila Tserkva, Ukraine
*Correspondence: matvieiev_mykhailo@nubip.edu.ua

Abstract:

The dynamic nature of the Ukrainian dairy sector requires the integration of modern information technology solutions for the judicious selection of economically viable animals, with a focus on genetic improvement through a comprehensive breeding index. However, the absence of a centralized data repository makes it impossible to calculate the breeding value of animals, does not contribute to making appropriate management decisions and thus does not help to improve the economic well-being of the farm. Farm software plays a key role in filling such a database. In Ukraine, there is a large number of software programs of various producers, including Ukrainian ones, which allow farms to organize correct recording and ensure the filling of the database. However, this diversity is often accompanied by the incompatibility of programs and the inability to combine the data registered by different programs. The study underlines the need for comprehensive improvements in the system of cow breeding using data from dairy farm software, especially in response to the growing trend towards automation.

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595–600 O. Shchuklina, R. Afanasiev, I. Voronchikhina, I. Klimenkova and A. Komkova
Differentiated application of nitrogen fertilizers based on optical sensor readings
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Differentiated application of nitrogen fertilizers based on optical sensor readings

O. Shchuklina¹*, R. Afanasiev², I. Voronchikhina¹, I. Klimenkova¹ and A. Komkova¹

¹Federal State Budgetary Institution of Sciences Tsitsin Main Botanical Garden of the Russian Academy of Sciences, Department of Distant hybridization, Botanic street 4, RU127276 Moscow, Russia
²All-Russian Research Institute of Agrochemistry named after D.N Pryanishnikov, Pryanishnikova street 31A, RU127434, Moscow, Russia
*Correspondence: oashuklina@gmail.com

Abstract:

The article considers the method of variable rate application of top dressing with nitrogen fertilizers in spring barley crops in the system of precise agriculture. The principle of is based on the in-process diagnosis of plants state in key phases of development and the introduction of necessary dose of top dressing in specific field areas. To assess the plants state, a GreenSeeker optical sensor, which measures the NDVI (Normalized Difference Vegetation Index). The tailored application of top dressing increases the yield of spring barley grain by 14.2% compared to the application of fertilizers with one calculated rate for the entire plot or field (Skudra, 2017, Hamann, 2020).

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1138-1146 A. Avotins, K. Kviesis, J. Bicans, I. Alsina and L. Dubova
Experimental Analysis of IoT Based Camera SI-NDVI Values for Tomato Plant Health Monitoring Application
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Experimental Analysis of IoT Based Camera SI-NDVI Values for Tomato Plant Health Monitoring Application

A. Avotins¹*, K. Kviesis¹, J. Bicans¹, I. Alsina² and L. Dubova²

¹Riga Technical University, Faculty of Power and Electrical Engineering, Institute of Industrial Electronics and Electrical Engineering, Azenes street 12/1, LV-1048, Riga, Latvia
²Latvia University of Life Sciences and Technologies, Faculty of Agriculture, Institute of Plant and Soil Science, Liela Street 2, LV-3001, Jelgava, Latvia
*Correspondence: ansis.avotins@rtu.lv

Abstract:

This paper reveals an IoT based camera design to capture SI-NDVI parameters and describes first obtained data analysis regarding luminary spectrum impact on readings in real greenhouse application. For experimental comparison, measurements of Encore, Strabena, Audiance, Bolzano, Forticia and Chocomate tomato plants, both for the ‘best’ and the ‘weakest’ plant sample, using IoT based camera solution and portable leaf spectrometer. First experimental results show that this approach can be applied for tomato plant monitoring, and reveals some ideas about possible precision improvements.

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1300–1306 K. Grausa, V. Komasilovs, L. Brossard, N. Quiniou, M. Marcon, M. Querne, A. Kviesis, N. Bumanis and A. Zacepins
Usability improvements of the Thermipig model for precision pig farming
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Usability improvements of the Thermipig model for precision pig farming

K. Grausa¹, V. Komasilovs¹, L. Brossard², N. Quiniou³, M. Marcon³, M. Querne³, A. Kviesis¹, N. Bumanis¹ and A. Zacepins¹*

¹University of Life Sciences and Technologies, Faculty of Information Technologies, Department of Computer Systems, Liela iela 2, LV-3001 Jelgava, Latvia
²PEGASE, INRAE, Agrocampus Ouest, 35590 Saint-Gilles, France
³IFIP-Institut du Porc, BP35104, 35651 Le Rheu cedex, France
Correspondence: aleksejs.zacepins@llu.lv

Abstract:

Pig livestock farming systems encounter several economic and environmental challenges, connected with meat price decrease, sanitary norms, emissions etc. To deal with these issues, methods and models to assess the performance of a pig production system have been developed. For instance, Thermipig model represents the pig fattening room and simulates performances of pigs at the batch level, taking into account interactions between the individual variability of pigs, farmer’s practices, room characteristics and outdoor climate conditions. The model requires some static basic inputs fulfilled in several spreadsheets (such as rooms, pigs, and dietary characteristics) but also data files for voluminous variable inputs (such as outdoor temperature or climate control box parameters) for further modelling and outcome producing. This leads to challenges in data providing by the farmers and have to be improved. This paper deals with the implementation of the separate modules of the developed data warehouse system for usability improvements of the Thermipig model. The idea is to substitute input from the data files with online data input and automated variable processing by the model using the python script for connection to the remote data warehouse. The data warehouse system is extended with ‘Property Sets’ section dealing with all the operations that can be performed to a set of input variables. This approach demonstrates the ability of the data warehouse to act as data supplier for the remote model. As well the outcome of the model is also transferable back to the data warehouse for evaluation. This work is done within the Era-Net SuSan PigSys project – Improving pig system performance through a whole system approach.

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418–429 G.A.S. Ferraz, P.F.P. Ferraz, F.B. Martins, F.M. Silva, F.A. Damasceno and M. Barbari
Principal components in the study of soil and plant properties in precision coffee farming
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Principal components in the study of soil and plant properties in precision coffee farming

G.A.S. Ferraz¹*, P.F.P. Ferraz¹, F.B. Martins², F.M. Silva¹, F.A. Damasceno¹ and M. Barbari³

¹Federal University of Lavras – UFLA, Departament of Agricultural Engineering, University Campus, BR37200-000 Lavras-MG, Brazil
²Rural Federal University of Rio de Janeiro – UFRRJ, BR-465, Km 7, BR 23.897-000 Seropédica- RJ, Brazil
³Department of Agriculture, Food, Environment and Forestry (DAGRI), Università degli Studi di Firenze, Via San Bonaventura, 13, IT50145 Firenze, Itália
*Correspondence: gabriel.ferraz@ufla.br

Abstract:

In this work, a principal component analysis was performed to evaluate the possibility of discarding obsolete soil and plant variables in a coffee field to eliminate redundant and difficult-to-measure information in precision coffee farming. This work was conducted at Brejão Farm in Três Pontas, Minas Gerais, Brazil, in a coffee field planted with 22 ha of Topázio cultivar. The evaluated variables were the yield, plant height, crown diameter, fruit maturation index, degree of fruit maturation, leafing, soil pH, available phosphorus (P), remaining phosphorus (Prem), available potassium (K), exchangeable calcium (Ca2+), exchangeable magnesium (Mg2+), exchangeable acidity (Al3+), potential acidity (H + Al), aluminium saturation (N(Al)), potential CEC (CECp), actual CEC (CECa), sum of bases (SB), base saturation (BS) and organic matter (OM). The data were evaluated by a principal component analysis, which generated 20 components. Of these, 7 representing 88.98% of the data variation were chosen. The variables were discarded based on the preservation of the variables with the greatest coefficients in absolute values corresponding to the first component, followed by the variable with the second highest absolute value corresponding to the second principal component. Based on the results, the variables V, OM, fruit maturity index, plant height, yield, leafing and P were selected. The other variables were discarded.

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784-793 K. Křížová, J. Haberle, M. Kroulík, J. Kumhálová and J. Lukáš
Assessment of soil electrical conductivity using remotely sensed thermal data
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Assessment of soil electrical conductivity using remotely sensed thermal data

K. Křížová¹²*, J. Haberle³, M. Kroulík¹, J. Kumhálová⁴ and J. Lukáš²

¹Czech University of Life Sciences Prague, Faculty of Engineering, Department of Agricultural Machines, Kamýcká 129, CZ16500 Prague, Czech Republic
²Crop Research Institute, Division of Crop Protection and Plant Health, Drnovská 507/73, CZ 16106 Prague, Czech Republic
³Crop Research Institute, Division of Crop Management Systems, Drnovská 507/73, CZ16106 Prague, Czech Republic
⁴Czech University of Life Sciences Prague, Faculty of Engineering, Department of Machinery Utilization, Kamýcká 129, CZ16500 Prague, Czech Republic
*Correspondence: krizovak@tf.czu.cz

Abstract:

Detection of heterogeneity (crop, soil, etc.) gained a lot of importance in the field of site-specific farming in recent years and became possible to be measured by different sensors. The thermal spectrum of electromagnetic radiation has a great potential today and experiments focused on describing a relation between canopy temperature and various vegetation characteristics are conducted. This paper was aimed to examine the relation between canopy temperature and electrical conductivity as one of staple soil characteristics. The related experiment was undertaken in Sojovice, Czech Republic, within an agricultural plot where winter wheat was grown in 2017 growing season. The examined plot was composed of three sub plots and 35 control points were selected within this area which the data were related to. A canopy was sensed by UAV (eBee carrying thermoMAP (FLIR TAU2) camera). Soil conductivity data were collected by terrestrial sampling using EM38-MK2 Ground Conductivity Meter in 1 m depth and 2 m sampling point distance. This dataset was later interpolated using the kriging method. The correlation analysis results showed a strong negative correlation between conductivity and thermal data (-0.82; p < 0.001). When comparing conductivity with NDVI representing the aboveground biomass, there was an opposite trend but also strong result (0.86; p < 0.001). Correlation coefficient of thermal data and NDVI comparison was -0.86; (p < 0.001). These preliminary results have a potential for further research in terms of soil characteristics studies.

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464-473 V. Komasilovs, A. Zacepins, A. Kviesis, A. Nasirahmadi and B. Sturm
Solution for remote real-time visual expertise of agricultural objects
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Solution for remote real-time visual expertise of agricultural objects

V. Komasilovs¹, A. Zacepins¹, A. Kviesis¹, A. Nasirahmadi² and B. Sturm²

¹University of Life Sciences and Technologies, Faculty of Information Technologies, Department of Computer Systems, Liela iela 2, LV-3001 Jelgava, Latvia
²University of Kassel, Process and Systems Engineering in Agriculture Group, Department of Agricultural and Biosystems Engineering, Nordbahnhofstrasse 1a, D-37213, Witzenhausen, Germany.
*Correspondence: aleksejs.zacepins@llu.lv

Abstract:

In recent years automated image and video analyses of plants and animals have become important techniques in Precision Agriculture for the detection of anomalies in development. Unlikely, machine learning (i.e., artificial neural networks, support vector machine, and other relevant techniques) are not always able to support decision making. Nevertheless, experts can use these techniques for developing more precise solutions and analysis approaches. It is labour-intensive and time-consuming for the experts to continuously visit the production sites to make direct on-site observations. Therefore, videos from the site need to be made available for remote viewing and analysis. In some cases it is also essential to monitor different parts of objects in agriculture and animal farming (e.g., bottom of the plants, stomach of the animal, etc.) which are difficult to access in standard recording procedures. One possible solution for the farmer is the use of a portable camera with real-streaming option rather than a stationary camera.
The aim of this paper is the proposition of a solution for real-time video streaming of agricultural objects (plants and/or animals) for remote expert evaluation and diagnosis. The proposed system is based on a Raspberry Pi 3, which is used to transfer the video from the attached camera to the YouTube streaming service. Users will be able to watch the video stream from the YouTube service on any device that has a web browser. Several cameras (USB, and Raspberry Pi camera) and video resolutions (from 480p till 1,080p) are compared and analysed, to find the best option, taking into account video quality, frame rates, and latency. Energy consumption of the whole system is evaluated and for the chosen solution it is 645 mA.

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