Tag Archives: precision agriculture

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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122-133 E. Kokin, M. Pennar, V. Palge and K. Jürjenson
Strawberry leaf surface temperature dynamics measured by thermal camera in night frost conditions
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Strawberry leaf surface temperature dynamics measured by thermal camera in night frost conditions

E. Kokin*, M. Pennar, V. Palge and K. Jürjenson

Estonian University of Life Sciences, Institute of Technology, Department of Energy Application, Kreutzwaldi 56, EE51014 Tartu, Estonia
*Correspondence: eugen.kokin@emu.ee

Abstract:

The aim of the study was to define the strawberry leaf surface and ambient air temperature differences in night frost conditions. The study was carried out at the commercial strawberry field in late autumn at a specific natural climatic situation, corresponding to night frost conditions. Thermal camera FLIR P660 was used for obtaining thermal images and corresponding visual colour images of the strawberry leaves. The images were taken at ten-minute interval. The ambient air temperature, relative humidity, dew point, solar radiation and wind speed data were obtained by Davis Vantage Pro2 weather station. It was estimated that the surface temperature of the specific leaf is comparatively similar at different parts of the specimen and changes noticeably with the variation of solar radiation intensity. The speed of temperature changes was also analysed. During all the measurement period, the considerable difference between the temperature of the leaf and the ambient air temperature was established, especially in absence of solar radiation. The difference of the leaf surface and ambient air temperature reached 8 °C. The study showed that in night frost conditions the plants might be endangered by low temperatures even at the air temperatures above 0 °C due to intensive energy loss by long wave radiation to the sky. It is suggested that the thermal imaging or infrared radiation measurement should be used simultaneously with air temperature measurements for more exact timing of night frost prevention measures at strawberry cultivation.

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959–966 K.E. Temizel
Mapping of some soil properties due to precision irrigation in agriculture
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Mapping of some soil properties due to precision irrigation in agriculture

K.E. Temizel

University of Ondokuz Mayıs, Faculty of Agriculture, Department of Agricultural Structures and Irrigation, Samsun, Turkey; e-mail: ersint@omu.edu.tr

Abstract:

Precision Agriculture (PA) is a whole-farm management approach using information technology, satellite positioning (GNSS) data, remote sensing and proximal data gathering. These technologies have the goal of optimizing returns on inputs whilst potentially reducing environmental impacts. This study was conducted out to determine the acidity, salinity, field capacity, permanent wilting point and water holding capacity in precision agriculture by analyzing soil samples taken from the field in 32 points. Maps were drawn by obtaining data from the field. The purpose of this research is to use the geographic information system for comparing the obtained data from soil more quickly and easily than before and also the water amount in order to make precise decisions for agriculture progress and applying the appropriate inputs which is related to water. The present results also indicated that water holding capacity maps. These maps are usage for the irrigation management and the information from different points of the field. These data obtained the field has an important role in the management of precision agriculture.

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307-314 D. Rössel, H. Ortiz-Laurel, N. Kanswohl and M. Schlegel
Mathematical modelling for precisely improving inputs supply for crop production
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Mathematical modelling for precisely improving inputs supply for crop production

D. Rössel¹, H. Ortiz-Laurel², N. Kanswohl³ and M. Schlegel³

¹Campus San Luis Potosí, Colegio de Postgraduados, Iturbide No. 73, Salinas de Hgo., S.L.P.,C.P. 78600. México. e-mail: edietmar@colpos.mx
²Campus Cordoba, Colegio de Postgraduados, km 348, Carr. Fed. Córdoba-Veracruz, Córdoba,Veracruz, C.P. 94500, México. hlaurel@colpos.mx
³Institute for Farm Animals Sciences and Technology. University of Rostock, Justus-von-LiebigWeg 8, 18059 Rostock, Germany; e-mail: norbert.kanswohl@uni-rostock.de

Abstract:

Although farm size may make a difference in access to all precision agriculture techniques, farms including small-scale traditional crop cultivation will likely have access to some of them in the long term. For this farm sector, a mathematical model is being developed to assist decision-making for improved dosage of nutrients and pesticides for crops or feed for animals. The objective was to find out the maximum allowed permissible deficiencies in dosing of inputs compared with the number of repetitions for improving precision dosage each time it is spread to the field. The model is based on a number of specified repetitions and it calculates the amount of deficiency to be obtained. It is possible to find that, depending on the rate of application, there is a wide range of choices among different fertilizer formulae and their concentration of available nutrients. The higher the number of applications, the more precision could be achieved. This will make it possible to arrive at optimum application rates for each field point or for supplying a more precise rate of feed to the animals.

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