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Ben-Gal, A
Bedwell, E
Baumbauer, C
Berger, A
Balzarini, M
Hoffmann, C
Robbins, J
Nnadi, F
DUMONT, B
Naor, A
Novais, W
Rudnick, D
Baffaut, C
D.C, H
Yin, X
Yang, Q
Holmes, A.W
Becker, M
Rahe, F
Wijewardane, N
Ragán, P
Yao, Y
Yang, C
Gailums, A
Gobezie, T.B
Nef, B.K
Rocha, D
Bredemeier, C
Bartzanas, T
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Authors
Asiabaka, C.C
Adesope, M.O
Ifeanyi- Obi, C.C
Nwakwasi, R.N
Nnadi, F
Matthews- Njoku, E.C
Chikaire, J
Pentjuðs, A
Gailums, A
Shaver, T
Schmer, M
Irmak, S
Van Donk, S
Wienhold, B
Jin, V
Bereuter, A
Francis, D
Rudnick, D
Ward, N
Hendrickson, L
Ferguson, R.B
Adamchuk, V.I
Yao, Y
Miao, Y
Huang, S
Gnyp, M.L
Khosla, R
Jiang, R
Bareth, G
Yao, Y
Miao, Y
Huang, S
Gnyp, M.L
Jiang, R
Chen, X
Bareth, G
Zhang, H
Lan, Y
Westbrook, J
Suh, C
Hoffmann, C
Lacey, R
Fountas, S
Bochtis, D
Sorensen, C
Green, O
J, R
Bartzanas, T
Ruckelshausen, A
Alheit, K.V
Busemeyer, L
Klose, R
Linz, A
Moeller, K
Rahe, F
Thiel, M
Trautz, D
Weiss, U
She, Y
Ehsani, R
Robbins, J
Owen, J
Leiva, J.N
Lee, W
Ehsani, R
Roka, F
Choi, D
Yang, C
Souza, E
Schenatto, K
Rodrigues, F
Rocha, D
Bazzi, C.L
Baffaut, C
Sudduth, K
Sadler, J
Kremer, R
Lerch, R
Kitchen, N
Veum, K
Anselmi, A.A
Federizzi , L.C
Bredemeier, C
Molin, J.P
Alchanatis, V
Cohen, Y
Sprinstin, M
Cohen, A
Zipori, I
Dag, A
Naor, A
Huang, S
Miao, Y
Yuan, F
Gnyp, M.L
Yao, Y
Cao, Q
Lenz-Wiedemann, V
Bareth, G
Wijewardane, N
Ge, Y
Becker, M
Velasquez, A.E
Guerrero, H.B
HIguti, V.A
Milori, D.M
Magalhães, D.V
Archila-Diaz, J.F
Becker, M
Gailums, A
Dr., N
T, S
giriyappa, M
D.C, H
PATIL, B
Prabhudeva, D
Kombali, G
Noorasma, S
Thimmegowda, M
Larson, J.A
Stefanini, M
Lambert, D.M
Yin, X
Boyer, C.N
Varco, J.J
Scharf , P.C
Tubaña, B.S
Dunn, D
Savoy, H.J
Buschermohle, M.J
Tyler, D.D
Ferreyra, R
Applegate, D.B
Berger, A.W
Berne, D.T
Craker, B.E
Daggett, D.G
Gowler, A
Bullock, R.J
Haringx, S.C
Hillyer, C
Howatt, T
Nef, B.K
Rhea, S.T
Russo, J.M
Nieman, S.T
Sanders, P
Wilson, J.A
Wilson, J.W
Tevis, J.W
Stelford, M.W
Shearouse, T.W
Schultz, E.D
Reddy, L
Holmes, A.W
Jiang, G
Dong, J
Meng, Z
Cong, Y
Zhang, A
Fu, W
Pan, R
Yang, Q
Shang, Y
Nigon, T
Mulla, D
Yang, C
Villalobos, J.E
Perret, J.S
Abdalla, K
Fuentes, C.L
Rodriguez, J.C
Novais, W
Laacouri, A
Nigon, T
Mulla, D
Yang, C
Rátonyi, T
Ragán, P
Sulyok, D
Nagy, J
Harsányi, E
Vántus, A
Csatári, N
Ragán, P
Harsányi, E
Nagy, J
Ágnes, T
Rátonyi, T
Vántus, A
Csatári, N
Nándor, C
Rátonyi, T
Harsányi, E
Ragán, P
Hagymássy, Z
Nagy, J
Vántus, A
de Souza, M.R
Bertani, T.D
Parraga, A
Bredemeier, C
Trentin, C
Doering, D
Susin, A
Negreiros, M
Goodrich, P.J
Baumbauer, C
Arias, A.C
LENOIR, A
VANDOORNE, B
DUMONT, B
Baumbauer, C
Goodrich, P
Arias, A
Katz, L
Ben-Gal, A
Litaor, I
Naor, A
Peeters, A
Goldshtein, E
Alchanatis, V
Cohen, Y
Balboa, G
Puntel, L
Melchiori, R
Ortega, R
Tiscornia, G
Bolfe, E
Roel, A
Scaramuzza, F
Best, S
Berger, A
Hansel, D
Palacios, D
Saurette, D
Biswas, A
Gobezie, T.B
Balboa, G
Degioanni, A
Bongiovanni, R
Melchiori, R
Cerliani, C
Scaramuzza, F
Bongiovanni, M
Gonzalez, J
Balzarini, M
Videla, H
Amin, S
Esposito, G
Bedwell, E
Topics
Food Security and Precision Agriculture
Guidance, Robotics, Automation, and GPS Systems
Spatial Variability in Crop, Soil and Natural Resources
Sensor Application in Managing In-season Crop Variability
Sensor Application in Managing In-season Crop Variability
Spatial Variability in Crop, Soil and Natural Resources
Precision Horticulture
Engineering Technologies and Advances
Precision Conservation Management
Profitability, Sustainability and Adoption
Remote Sensing Applications in Precision Agriculture
Remote Sensing Applications in Precision Agriculture
Proximal Sensing in Precision Agriculture
Engineering Technologies and Advances
Precision Nutrient Management
Profitability, Sustainability and Adoption
Standards & Data Stewardship
Site-Specific Nutrient, Lime and Seed Management
Precision Agriculture and Global Food Security
In-Season Nitrogen Management
Proximal and Remote Sensing of Soil and Crop (including Phenotyping)
Big Data, Data Mining and Deep Learning
On Farm Experimentation with Site-Specific Technologies
Precision Dairy and Livestock Management
Applications of Unmanned Aerial Systems
Wireless Sensor Networks
Site-Specific Nutrient, Lime and Seed Management
Proximal and Remote Sensing of Soil and Crop (including Phenotyping)
ISPA Community: Latin America
Precision Horticulture
Education and Outreach in Precision Agriculture
Big Data, Data Mining and Deep Learning
Type
Poster
Oral
Year
2012
2010
2014
2016
2018
2022
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Authors

Filter results39 paper(s) found.

1. Developing An Active Crop Sensor-based In-season Nitrogen Management Strategy For Rice In Northeast China

  Crop sensor-based in-season N management strategies have been successfully developed and evaluated for winter wheat around the world, but little has been reported for rice. The objective of this study was to develop an active crop sensor-based in-season N management strategy for upland rice in Northeast... Y. Yao, Y. Miao, S. Huang, M.L. Gnyp, R. Jiang, X. Chen, G. Bareth

2. Investigation Of Crop Varieties At Different Growth Stages Using Optical Sensor Data

Cotton, soybean and sorghum are economically important crops in Texas. Knowing the growing status of crops at different stages of growth is crucial to apply site-specific management and increase crop yield for farmers. Field experiments were initiated to measure cotton, soybean and sorghum plants growth status and spatial variability through the whole growing cycle. A ground-based active optical sensor, Greenseeker®, was used to collect the Normalized Difference Vegetation Index (NDVI) data... H. Zhang, Y. Lan, J. Westbrook, C. Suh, C. Hoffmann, R. Lacey

3. Spatial-temporal Management Zones For Biomass Moisture

 Biomass handling operations (harvesting, raking, collection, and transportation) are critical operations within the agricultural production system since they constitute the first link in the biomass supply chain, a fact of substantial importance considering the increasingly involvement of biomass in bio-refinery and bio-energy procedures. Nevertheless, the inherent uncertainty, imposed by the interaction between environmental, biological, and machinery factors, makes the available scheduling... S. Fountas, D. Bochtis, C. Sorensen, O. Green, R. J, T. Bartzanas

4. Sensor And System Technology For Individual Plant Crop Scouting

Sensor and system technologies are key components for automatic treatment of individual plants as well as for plant phenotyping in field trials. Based on experiences in research and application of sensors in agriculture the authors have developed phenotyping platforms for field applications including sensors, system and software development and application-specific mountings.   Sensor and data fusion have a high potential by compensating varying selectivities... A. Ruckelshausen, K.V. Alheit, L. Busemeyer, R. Klose, A. Linz, K. Moeller, F. Rahe, M. Thiel, D. Trautz, U. Weiss

5. Enhancing Farmers' Indigenous Knowledge Management in Cassava Varietal Trial Using Agro Ecosystem Analysis, Farmers' Drama Group and Animations in Eastern part of Nigeria.

Researchers continue to come up with new varieties but farmer perspectives and preferences are very important factors for new varieties to spread in farmers’ communities. Researcher priorities alone are not enough. A variety may be ‘scientifically perfect... C.C. Asiabaka, M.O. Adesope, C.C. Ifeanyi- obi, R.N. Nwakwasi, F. Nnadi, E.C. Matthews- njoku, J. Chikaire

6. Improvement Precision Agricultural Communication Schema agroXML Based on Multi-Agents System's Deliberation and Decision Making Processes

... A. Pentjuðs, A. Gailums

7. Landscape Influences on Soil Nitrogen Supply and Water Holding Capacity for Irrigated Corn

... T. Shaver, M. Schmer, S. Irmak, S. Van donk, B. Wienhold, V. Jin, A. Bereuter, D. Francis, D. Rudnick, N. Ward, L. Hendrickson, R. Ferguson, V.I. Adamchuk

8. In-season Diagnosis of Rice Nitrogen Status Using an Active Canopy Sensor

... Y. Yao, Y. Miao, S. Huang, M. Gnyp, R. Khosla, R. Jiang, G. Bareth

9. Applications Of Small UAV Systems For Tree And Nursery Inventory Management

Unmanned aerial vehicles (UAV) systems could provide low-cost and high spatial resolution aerial images. These features and ease of operation make it a practical tool for applications in precision agriculture and horticulture. This paper highlights the application of UAV systems in tree counting, which is vital for tree inventory management and yield estimation. In this paper, two types of trees were discussed. One type is with non-uniform canopy area (e.g. container plants and citrus... Y. She, R. Ehsani, J. Robbins, J. Owen, J.N. Leiva

10. Post-Harvest Quality Evaluation System On Conveyor Belt For Mechanically Harvested Citrus

Recently, a machine vision technology has shown its popularity for automating visual inspection. Many studies proved that the machine vision system can successfully estimate external qualities of fruit as good as manual inspection. However, introducing mechanical harvesters to citrus industry caused the following year’s yield loss due to the loss of immature young citrus. In this study, a machine vision system on a conveyor belt was developed to inspect mechanically... W. Lee, R. Ehsani, F. Roka, D. Choi, C. Yang

11. Comparison Of Management Zones Generated By The K-Means And Fuzzy C-Means Methods

The generation of Management Zones (MZ) is an economic alternative to make viable the precision agriculture (RODRIGUES & ZIMBACK, 2002) because they work as operation units for the inputs localized application and as soil and culture sample indicators. For the field division in... E. Souza, K. Schenatto, F. Rodrigues, D. Rocha, C. Bazzi

12. Production And Conservation Results From A Decade-Long Field-Scale Precision Agriculture System

Research is needed that simultaneously evaluates production and conservation outcomes of precision agriculture practices.  From over a decade (1993-2003) of yield and soil mapping and water quality assessment, a multi-faceted, “precision agriculture system” (PAS) was developed and initiated in 2004 on a 36-ha field in Central Missouri. The PAS assessment was accomplished by comparing it to the previous decade of conventional corn-soybean... C. Baffaut, K. Sudduth, J. Sadler, R. Kremer, R. Lerch, N. Kitchen, K. Veum

13. Factors Related To Adoption Of Precision Agriculture Technologies In Southern Brazil

The adoption of technologies which allow the increase of food production with improving quality in addition to reduce the foot prints in the environment is important for agribusiness development. Precision Agriculture (PA) stands out as an option to aid the achievement of these goals. Brazil plays an important role to supply agricultural products and to demand technologies. However, research has focused on technical and economic implementation of PA technologies. Therefore, more information... A.A. Anselmi, L.C. Federizzi , C. Bredemeier, J.P. Molin

14. Automatic Detection And Mapping Of Irrigation System Failures Using Remotely Sensed Canopy Temperature And Image Processing

Today there is no systematic way to identify and locate failures of irrigation systems mainly because of the labor costs associated with locating the failures. The general aim of this study was to develop an airborne thermal imaging system for semi - automatic monitoring and mapping of irrigation system failures, specifically, of leaks and clogs. Initially, leaks and clogs were simulated by setting controlled trials in table grapes vineyards and olive groves. Airborne thermal... V. Alchanatis, Y. Cohen, M. Sprinstin, A. Cohen, I. Zipori, A. Dag, A. Naor

15. Potential Improvement in Rice Nitrogen Status Monitoring Using Rapideye and Worldview-2 Satellite Remote Sensing

For in-season site-specific nitrogen (N) management of rice to be successful, it is crucially important to diagnose rice N status efficiently across large area in a timely fashion. Satellite remote sensing provides a promising technology for crop growth monitoring and precision management over large areas. The FORMOSAT-2 satellite remote sensing imageries with 4 wavebands have been used to estimate rice N status. The objective of this study was to evaluate the potential of using high spatial resolution... S. Huang, Y. Miao, F. Yuan, M.L. Gnyp, Y. Yao, Q. Cao, V. Lenz-wiedemann, G. Bareth

16. Laboratory Evaluation of Two VNIR Optical Sensor Designs for Vertical Soil Sensing

Visible and near infrared reflectance spectroscopy (VNIR) is becoming an extensively researched technology to predict soil properties such as soil organic carbon, inorganic carbon, total nitrogen, moisture  for precision agriculture. Due to its rapid, non-destructive nature and ability to infer multiple soil properties simultaneously, engineers have been trying to develop proximal sensors based on the VNIR technology to enable horizontal soil sensing and mapping. Since the vertical variation... N. Wijewardane, Y. Ge

17. Helvis - a Small-scale Agricultural Mobile Robot Prototype for Precision Agriculture

The use of agricultural robots is emerging in a complex scenario where it is necessary to produce more food to feed a crescent population, decrease production costs, fight plagues and diseases, and preserve nature. Around the world, there are many research institutes and companies trying to apply mobile robotics techniques in agricultural fields. Mostly, large prototypes are being used and their shapes and dimensions are very similar to tractors and trucks. In the present study, a small-scale... M. Becker, A.E. Velasquez, H.B. Guerrero, V.A. Higuti, D.M. Milori, D.V. Magalhães

18. Simulation of Curiosity and Exo Mars Rovers on Agriculture Terrain

Improving agricultural productivity is one of the biggest challenges Agriculture and Engineering face. A possible solution is the creation of soil databases and/or maps to apply precision agriculture techniques, aiming to produce more in the same land, using less agricultural supplies. This practice may be developed with the help of rovers applied to e.g. agricultural data collect, mapping, scouting and supply tasks. However, the rover needs to move and adapt to the terrain to obtain a real appropriate... J.F. Archila-diaz, M. Becker

19. The Methods and Applications of Artificial Intelligence Used in the Technologies of Precision Agriculture

The methods and applications of artificial intelligence more and more are linking with technologies of precision agriculture. The classical and modern approaches to artificial intelligence used for problem solving in the technologies of precision agriculture. Searching methods include uninformed and informed search methods which is better way to achieve optimality. Expert systems are typical classical approaches to artificial intelligence and they can be applied for problem solutions. Decision... A. Gailums

20. Precision Nutrient Management Through Drip Irrigation in Aerobic Rice

A field experiment was conducted during kharif 2015 to asses the spatial variability and precision nutrient management through drip irrigation in aerobic rice at ZARS, GKVK, Bangalore. The experimental field has been delineated into 48 grids of 4.5 m x 4.5 m using geospatial technology. Soil samples from 0-15 cm depth were collected and analysed. There was spatial variability for available nitrogen (154 to 277 kg ha-1), phosphorous (45 to 152 kg ha-1) and potassium... N. Dr., S. T, M. Giriyappa, H. D.c, B. Patil, D. Prabhudeva, G. Kombali, S. Noorasma, M. Thimmegowda

21. Net Returns and Production Use Efficiency for Optical Sensing and Variable Rate Nitrogen Technologies in Cotton Production

This research evaluated the profitability and N use efficiency of real time on-the-go optical sensing measurements (OPM) and variable-rate technologies (VRT) to manage spatial variability in cotton production in the Mississippi River Basin states of Louisiana, Mississippi, Missouri, and Tennessee. Two forms of OPM and VRT and the existing farmer practice (FP) were used to determine N fertilizer rates applied to cotton on farm fields in the four states. Changes in yields and N rates due to OPM... J.A. Larson, M. Stefanini, D.M. Lambert, X. Yin, C.N. Boyer, J.J. Varco, P.C. Scharf , B.S. Tubaña, D. Dunn, H.J. Savoy, M.J. Buschermohle, D.D. Tyler

22. Toward Geopolitical-Context-Enabled Interoperability in Precision Agriculture: AgGateway's SPADE, PAIL, WAVE, CART and ADAPT

AgGateway is a nonprofit consortium of 240+ businesses working to promote, enable and expand eAgriculture. It provides a non-competitive collaborative environment, transparent funding and governance models, and anti-trust and intellectual property policies that guide and protect members’ contributions and implementations. AgGateway primarily focuses on implementing existing standards and collaborating with other organizations to extend them when necessary. In 2010 AgGateway identified... R. Ferreyra, D.B. Applegate, A.W. Berger, D.T. Berne, B.E. Craker, D.G. Daggett, A. Gowler, R.J. Bullock, S.C. Haringx, C. Hillyer, T. Howatt, B.K. Nef, S.T. Rhea, J.M. Russo, S.T. Nieman, P. Sanders, J.A. Wilson, J.W. Wilson, J.W. Tevis, M.W. Stelford, T.W. Shearouse, E.D. Schultz, L. Reddy

23. Increasing Profitability & Sustainability of Maize Using Site-Specific Crop Management in New Zealand

Precision agriculture (PA) tools and techniques have been used in New Zealand (NZ) since the early 1990's. There has been wide-scale uptake of some PA tools such as autosteer; planter and sprayer section control; and variable-rate irrigation. However, there has been a limited uptake of Site-Specific Crop Management (SSCM) using variable-rate seeding, nutrient and lime applications to different Management Zones (MZ). This paper outlines examples of the use of SSCM on maize crops,... A.W. Holmes, G. Jiang

24. An Automatic Control Method Research for 9YG-1.2 Large Round Baler

When manual or semi-automatic round baler working, the tractor driver have to frequently manual the machine according to the bale process at the same time of driving. The driver easily feel fatigue in this operating mode for a long time, so the consistency of the bale’s density can not be guaranteed. And there may be wrong operation. In this article, we use the model 9YG-1.2 large round baler as a research prototype. We study the information collection and processing of the baler’s... J. Dong, Z. Meng, Y. Cong, A. Zhang, W. Fu, R. Pan, Q. Yang, Y. Shang

25. Utilization of Spatially Precise Measurements to Autocalibrate the EPIC Agroecosystem Model

Corn nitrogen recommendations for individual fields must improve to minimize the negative influence that agriculture has on the environment and society. Two adaptive N management approaches for making in-season N fertilizer recommendations are remote sensing and crop systems modeling. Remote sensing has the advantage of characterizing the spatial variability at a high spatial resolution, and crop models are prognostic and can assess expected additions and losses that are not yet reflected by the... T. Nigon, D. Mulla, C. Yang

26. Delineation of Site-Specific Nutrient Management Zones to Optimize Rice Production Using Proximal Soil Sensing and Multispectral Imaging

Evaluating nutrient uptake and site-specific nutrient management zones in rice in Costa Rica from plant tissue and soil sampling is expensive because of the time and labor involved.  In this project, a range of measurement techniques were implemented at different vintage points (soil, plant and UAVs) in order to generate and compare nutrient management information.  More precisely, delineation of site-specific nutrient management zones were determined using 1) georeferenced soil/tissue... J.E. Villalobos, J.S. Perret, K. Abdalla, C.L. Fuentes, J.C. Rodriguez, W. Novais

27. A Case Study Comparing Machine Learning and Vegetation Indices for Assessing Corn Nitrogen Status in an Agricultural Field in Minnesota

Compact hyperspectral sensors compatible with UAV platforms are becoming more readily available. These sensors provide reflectance in narrow spectral bands while covering a wide range of the electromagnetic spectrum. However, because of the narrow spectral bands and wide spectral range, hyperspectral data analysis can benefit greatly from data mining and machine learning techniques to leverage its power. In this study, rainfed corn was grown during the 2017 growing season using four nitrogen treatments... A. Laacouri, T. Nigon, D. Mulla, C. Yang

28. Evaluation of Strip Tillage Systems in Maize Production in Hungary

Strip tillage is a form of conservation tillage system. It combines the benefits of conventional tillage systems with the soil-protecting advantages of no-tillage. The tillage zone is typically 0.25 to 0.3 m wide and 0.25 to 0.30 m deep. The soil surface between these strips is left undisturbed and the residue from the previous crop remain on the soil surface. The residue-covered area reaches 60-70%. Keeping residue on the surface helps prevent soil structure and reduce water loss from the soil.... T. Rátonyi, P. Ragán, D. Sulyok, J. Nagy, E. Harsányi, A. Vántus, N. Csatári

29. Examining the Relationship Between SPAD, LAI and NDVI Values in a Maize Long-Term Experiment

In Hungary, the preconditions for the use of precision crop production have undergone enormous development over the last five years. RTK coverage is complete in crop production areas. Consultants are increasingly using the vegetation index maps from Landsat and Sentinel satellite data, but measurements with on-site proximal plant sensors are also needed to exclude the influence of the atmosphere. The aim of our studies was to compare the values measured by proximal plant sensors in the... P. Ragán, E. Harsányi, J. Nagy, T. Ágnes, T. Rátonyi, A. Vántus, N. Csatári

30. The Spread of Precision Livestock Farming Technology at Dairy Farms in East Hungary

During the survey, 25 dairy farms were examined in East Hungary in Hajdú-Bihar (H-B) County between 2017 and 2018 by methodical observation and oral interviews with the farm managers, about the spread of Precision Livestock Farming (PLF) technologies. Among Holstein Friesian dairy farms in the County 60% were questioned, and the representativity was above 47 percent ins each size category. Nine precision farming equipment were examined on the farms: milking robot or robotic carousel milking... C. Nándor, T. Rátonyi, E. Harsányi, P. Ragán, Z. Hagymássy, J. Nagy, A. Vántus

31. Wheat Biomass Estimation Using Visible Aerial Images and Artificial Neural Network

In this study, visible RGB-based vegetation indices (VIs) from UAV high spatial resolution (1.9 cm) remote sensing images were used for modeling shoot biomass of two Brazilian wheat varieties (TBIO Toruk and BRS Parrudo). The approach consists of a combination of Artificial Neural Network (ANN) with several Vegetation Indices to model the measured crop biomass at different growth stages. Several vegetation indices were implemented: NGRDI (Normalized Green-Red Difference Index), CIVE (Color Index... M.R. De souza, T.D. Bertani, A. Parraga, C. Bredemeier, C. Trentin, D. Doering, A. Susin, M. Negreiros

32. A Passive-RFID Wireless Sensor Node for Precision Agriculture

Accurate soil data is crucial for precision agriculture.  While existing optical methods can correlate soil health to the gasses emitted from the field, in-soil electronic sensors enable real-time measurements of soil conditions at the effective root zone of a crop. Unfortunately, modern soil sensor systems are limited in what signals they can measure and are generally too expensive to reasonably distribute the sensors in the density required for spatially accurate feedback.  In this... P.J. Goodrich, C. Baumbauer, A.C. Arias

33. A Low-tech Approach to Manage Within Field Variability – Toward a Territorial Scale Application

Managing within field variability is promising to achieve European objectives of sustainability in crop production. Technological development has allowed to precisely characterize fields heterogeneity in space and time. However, learnings from low adoption of yield maps in west-European context have highlighted the importance of reliable methods to support decisions. Blackmore et al. designed a delineation method considering yield as an integrative variable that reflects spatial and temporal... A. Lenoir, B. Vandoorne, B. Dumont

34. Printed Nitrate Sensors for In-soil Measurements

Managing nitrate is a central concert for precision agriculture, from delineating management zones, to optimizing nitrogen use efficiency through in-season applications, to minimizing leaching and greenhouse gas emissions. However, measurement methods for in-soil nitrate are limiting. State-of-the-art soil nitrate analysis requires taking soil or liquid samples to laboratories for chemical or spectrographic analysis. These methods are accurate, but costly, labor intensive, and cover limited geographic... C. Baumbauer, P. Goodrich, A. Arias

35. Comparison of Canopy Extraction Methods from UAV Thermal Images for Temperature Mapping: a Case Study from a Peach Orchard

Canopy extraction using thermal images significantly affects temperature mapping and crop water status estimation. This study aimed to compare several canopy extraction methodologies by utilizing a large database of UAV thermal images from a precision irrigation trial in a peach orchard. Canopy extraction using thermal images can be attained by purely statistical analysis (S), a combination of statistical and spatial analyses (SS), or by synchronizing thermal and RGB images, following RGB statistical... L. Katz, A. Ben-gal, I. Litaor, A. Naor, A. Peeters, E. Goldshtein, V. Alchanatis, Y. Cohen

36. How Digital is Agriculture in South America? Adoption and Limitations

A rapidly growing population in a context of land and water scarcity, and climate change has driven an increase in healthy, nutritious, and affordable food demand while maintaining the current cropping area. Digital agriculture (DA) can contribute solutions to meet the demands in an efficient and sustainable way. South America (SA) is one of the main grain and protein producers in the world but the status of DA in the region is unknown. This article presents the results from a systematic review... G. Balboa, L. Puntel, R. Melchiori, R. Ortega, G. Tiscornia, E. Bolfe, A. Roel, F. Scaramuzza, S. Best, A. Berger, D. Hansel, D. Palacios

37. Digital Soil Sensing and Mapping for Crop Suitability

Soil, central to any land-based production system, determines the success of any crops. While soil for a farm or field is fixed, the crops can be selected to best fit the soil’s capability and production. Traditionally crops are selected based on farm history, knowledge, and years of trial and error to tailor the right crop to the right soil. Inherent challenges associated with this make the whole process unsustainable. Due to the consistent nature of the information collected, soil sensors... D. Saurette, A. Biswas, T.B. Gobezie

38. Overcoming Educational Barriers for Precision Agriculture Adoption: a University Diploma in Precision Agriculture in Argentina

The lack of educational programs in Precision Agriculture (PA) has been reported as one of the barriers for adoption. Our goal was to improve professional competence in PA through education in crop variability, management, and effective practices of PA in real cases. In the last 20 years different efforts has been made in Argentina to increase adoption of PA. The Universidad Nacional de Rio Cuarto (UNRC) launched in 2021 the first University Diploma in PA, a 9-month program to train agronomist... G. Balboa, A. Degioanni, R. Bongiovanni, R. Melchiori, C. Cerliani, F. Scaramuzza, M. Bongiovanni, J. Gonzalez, M. Balzarini, H. Videla, S. Amin, G. Esposito

39. Increasing Precision Irrigation Efficacy for Row Crop Agriculture Through the Use of Artificial Intelligence

The agricultural sector is the largest consumer of the world’s available fresh water resources. With fresh water scarcity increasing worldwide, more efficient use for irrigation water is necessary. Precision irrigation is described as the application of water to meet crop needs of a specific area, at the right amount and at the time that is optimum for crop health and management objectives. Irrigation becomes increasingly efficient through the use of precision irrigation tools. However,... E. Bedwell