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Bae, I
Nuyttens, D
Walsh, O.S
Bochtis, D
Ishii, K
Wyatt, B
Nyéki, A
Delgadillo, C.A
Green, O
Best, S
Bouroubi, Y
Dabbelt, D
Gamble, A
Wang, Z
Ghosheh, H
Holmes, A
Randriamanga, D
Diaz, O.A
Bertani, T.D
Huang, H
Demattê, J.M
Ninomiya, K
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Authors
Borchert, A
Recke, G
Dabbelt, D
Trautz, D
Olfs, H
Han-ya, I
Ishii, K
Noguchi, N
Rasooli Sharabian, V
Shibusawa, S
Ninomiya, K
Kodaira, M
Mochizuki, R
Han-ya, I
Noguchi, N
Su, B
Ishii, K
Pantoja, J.L
Daroub, S.H
Diaz, O.A
Ghosheh, H
Kodaira, M
Shibusawa, S
Ninomiya, K
Fountas, S
Bochtis, D
Sorensen, C
Green, O
J, R
Bartzanas, T
Walsh, O.S
Pandey, A
Christiaens, R
Meng, Z
Wang, Z
Wu, G
Fu, W
An, X
Kovács, A.J
Nyéki, A
Milics, G
Neményi, M
Walsh, O.S
Samborski, S.M
Stępień, M
Gozdowski, D
Lamb, D.W
Gacek, E.S
Drzazga, T
Walsh, O.S
Samborski, S.M
Gozdowski, D
Stępień, M
Leszczyńska, E
Walsh, O.S
Belmont, K
McClintick-Chess, J
Fontenelli, J.V
Amaral, L.R
Demattê, J.M
Magalhães, P.G
Sanches, G
Archer, J.K
Delgadillo, C.A
Shen, F
Delauré, B
Baeck, P
Blommaert, J
Delalieux, S
Livens, S
Sima, A
Boonen, M
Goffart, J
Jacquemin, G
Nuyttens, D
Keresztes, B
Da Costa, J
Randriamanga, D
Germain, C
Abdelghafour, F
Ekanayake, D.C
Owens, J
Werner, A
Holmes, A
Seo, Y
Lee, W
Kim, Y
Chung, S
Jang, S
Bae, I
Larsen, D
Skovsen, S
Steen, K.A
Grooters, K
Green, O
Jørgensen, R.N
Eriksen, J
de Souza, M.R
Bertani, T.D
Parraga, A
Bredemeier, C
Trentin, C
Doering, D
Susin, A
Negreiros, M
Bouroubi, Y
Bugnet, P
Nguyen-Xuan, T
Bélec, C
Longchamps, L
Vigneault, P
Gosselin, C
Saifuzzaman, M
Adamchuk, V.I
Huang, H
Ji, W
Rabe, N
Biswas, A
Shinde, S
Adamchuk, V
Lacroix, R
Tremblay, N
Bouroubi, Y
Huang, H
Adamchuk, V
Biswas, A
Ji, W
Lauzon, S
Straw, C
Wyatt, B
Smith, A.P
Watkins, K
Hong, S
Floyd, W
Williams, D
Garza, C
Jansky, T
Ortiz, B.V
Lena, B.P
Morlin , F
Morata, G
Duarte de Val, M
Prasad, R
Gamble, A
Balboa, G
Puntel, L
Melchiori, R
Ortega, R
Tiscornia, G
Bolfe, E
Roel, A
Scaramuzza, F
Best, S
Berger, A
Hansel, D
Palacios, D
Topics
Precision Nutrient Management
Remote Sensing Applications in Precision Agriculture
Proximal Sensing in Precision Agriculture
Spatial Variability in Crop, Soil and Natural Resources
Global Proliferation of Precision Agriculture and its Applications
Spatial Variability in Crop, Soil and Natural Resources
Precision Nutrient Management
Spatial Variability in Crop, Soil and Natural Resources
Emerging Issues in Precision Agriculture (Energy, Biofuels, Climate Change, Standards)
Precision Nutrient Management
Sensor Application in Managing In-season Crop Variability
Proximal Sensing in Precision Agriculture
Standards & Data Stewardship
Unmanned Aerial Systems
Proximal and Remote Sensing of Soil and Crop (including Phenotyping)
On Farm Experimentation with Site-Specific Technologies
Precision Horticulture
Applications of Unmanned Aerial Systems
Big Data, Data Mining and Deep Learning
Decision Support Systems
Geospatial Data
Geospatial Data
Drainage Optimization and Variable Rate Irrigation
ISPA Community: Latin America
Type
Poster
Oral
Year
2012
2010
2014
2016
2018
2022
Home » Authors » Results

Authors

Filter results29 paper(s) found.

1. Precision Weed Management Research Advancement In The Near East

  Precision weed control research received considerable attention since the introduction of global positioning systems (GPS). GPS and geographic information systems (GIS) technologies may assist with field monitoring, particularly; in deciding what weed species to monitor? What weed densities are bypassing critical thresholds? and where?  While advancements in precision agricultural research could be detected through the intensive publications in the developed world,... H. Ghosheh

2. Dozen Parameters Soil Mapping Using The Real-time Soil Sensor

 A Real-time soil sensor (RTSS) can be predicted soil parameters using near-infrared underground soil reflectance sensor in commercial farms. ... M. Kodaira, S. Shibusawa, K. Ninomiya

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. Economic Evaluation of a Variable Lime Application Strategy Based on Soil pH Maps Derived from On-The-Go pH-Measurements under German Conditions

... A. Borchert, G. Recke, D. Dabbelt, D. Trautz, H. Olfs

5. Appropriate Wavelengths for Winter Wheat Growth Status Based On Multi-Spectral Crop Reflectance Data

One of the applications of remote sensing in agriculture is to obtain crop status for estimation and management of variable rate of inputs in the crop production. In order to select the appropriate wavelengths related... I. Han-ya, K. Ishii, N. Noguchi, V. Rasooli sharabian

6. Nineteen-Soil-Parameter Calibration Models and Mapping for Upland Fields Using the Real-Time Soil Sensor

In precision agriculture, rapid, non-destructive, cost-effective and convenient soil analysis techniques are needed for soil management, crop quality control using fertilizer, manure and compost, and variable-rate input for soil... S. Shibusawa, K. Ninomiya, M. Kodaira

7. Remote Sensing Imagery Based Agricultural Land Pattern Extraction around Miyajimanuma Wetland

This research aimed to extract agricultural land use pattern around the Miyajimanuma wetland, Hokkaido, Japan. By combining the image segmentation technology - watershed transform and image classification technology- particle swarm optimization (PSO)-k-means based minimum distance classifier, a new method for extracting the agricultural land use information based... R. Mochizuki, I. Han-ya, N. Noguchi, B. Su, K. Ishii

8. Soil Spatial Variability in the Everglades Agricultural Area in South Florida

The Everglades agricultural area is composed by histosols laying on hard limestone bedrock in south Florida. Despite the common assumption of homogeneity of these soils, agricultural practices could result in the increase of soil variability. Therefore, soil spatial variability was studied on three fields (5.5 ha each) at the Everglades Research and Education Center to compare the changes... J.L. Pantoja, S.H. Daroub, O.A. Diaz

9. Precision Sensors For Improved Nitrogen Recommendations In Wheat

Crop sensor-based systems with developed algorithms for making mid-season fertilizer nitrogen (N) recommendations are commercially available to producers in some parts of the world. Although there is growing interest in these technologies by grain producers in Montana, use is limited by the lack of local research under Montana’s semiarid conditions. A field study was carried out at two locations in 2011, three locations in 2012, and two locations in 2013 in North West Montana:... O.S. Walsh, A. Pandey, R. Christiaens

10. The Spatial And Temporal Variability Analysis Of Wheat Yield in suburban of Beijing

  Abstract: The yield map is the basis of the fertilization maps and plant maps. In order to diagnose the cause of variation accurately, not only the spatial variation of annual yield data, but also the successive annual yield data of temporal variability should be understood.The introduction of yield monitor system, global positioning system (GPS), and geographic information system have provided new methods to obtain wheat yield in precision agriculture.... Z. Meng, Z. Wang, G. Wu, W. Fu, X. An

11. Climate Change And Sustainable Precision Crop Production With Regard To Maize (Zea Mays L.)

Precision crop production research activities were started during the mid-‘90s at the Institute of Biosystems Engineering, Faculty of Agricultural and Food Sciences, University of West Hungary. On the basis of the experiences with DSSAT (Decision Support System for Agrotechnology Transfer) the impact of climate change on maize yield (three soil types) was investigated until 2100. DSSAT crop growth model is used worldwide. The coupled model intercomparison project... A.J. Kovács, A. Nyéki, G. Milics, M. Neményi

12. Winter Wheat Genotype Effect on Canopy Reflectance: Implications for Using NDVI for In-season Nitrogen Topdressing Recommendations

Active optical sensors (AOSs) measure crop reflectance at specific wavelengths and calculate vegetation indices (VIs) that are used to prescribe variable N fertilization. Visual observations of winter wheat (Triticum aestivum L.) plant greenness and density suggest that VI values may be genotype specific. Some sensor systems use correction coefficients to eliminate the effect of genotype on VI values. This study was conducted to assess the effects of winter wheat cultivars and growing conditions... O.S. Walsh, S.M. Samborski, M. Stępień, D. Gozdowski, D.W. Lamb, E.S. gacek, T. Drzazga

13. On-Farm Evaluation of an Active Optical Sensor Performance for Variable Nitrogen Application in Winter Wheat

Winter wheat (Triticum aestivum L.) represents almost 50% of total cereal production in the European Union, accounting for approximately 25% of total mineral nitrogen (N) fertilizer applied to all crops. Currently, several active optical sensor (AOS) based systems for optimizing variable N fertilization are commercially available for a variety of crops, including wheat. To ensure successful adoption of these systems, definitive measurable benefits must be demonstrated. Nitrogen management strategies... O.S. Walsh, S.M. Samborski, D. Gozdowski, M. Stępień, E. Leszczyńska

14. Sensor-based Technologies for Improving Water and Nitrogen Use Efficiency

 Limited reports exist on identifying the empirical relationships between plant nitrogen and water status with hyperspectral reflectance. This project is aiming to develop effective system for nitrogen and water management in wheat. Specifically: 1) To evaluate the effects of nitrogen rates and irrigation treatments on wheat plant growth and yield; 2) To develop methods to predict yield and grain protein content in varying nitrogen and water environments, and to determine the minimum nitrogen... O.S. Walsh, K. Belmont, J. Mcclintick-chess

15. Soil Attributes Estimation Based on Diffuse Reflectance Spectroscopy and Topographic Variability

The local management of crop areas, which is the basic concept of precision agriculture, is essential for increasing crop yield. In this context, diffuse reflectance spectroscopy (DRS) and digital elevation modelling (DEM) appears as an important technique for determining soil properties, on an adequate scale to agricultural management, enabling faster and less costly evaluations in soil studies. The objective of this work was to evaluate the use of DRS together with topographic parameters for... J.V. fontenelli, L.R. Amaral, J.M. Demattê, P.G. Magalhães, G. Sanches

16. Key Data Ownership, Privacy and Protection Issues and Strategies for the International Precision Agriculture Industry

Precision agriculture companies seek to leverage technology to process greater volumes of data, greater varieties of data, and at a velocity unfathomable to most. The promises of boundless benefits are coupled with risks associated with data ownership, stewardship and privacy. This paper presents some risks related to the management of farm data, in general, as well as those unique to operating in the international arena.  Examples of U.S. and international laws related to data protection... J.K. Archer, C.A. Delgadillo, F. Shen

17. High Resolution Vegetation Mapping with a Novel Compact Hyperspectral Camera System

The COSI-system is a novel compact hyperspectral imaging solution designed for small remotely piloted aircraft systems (RPAS). It is designed to supply accurate action and information maps related to the crop status and health for precision agricultural applications. The COSI-Cam makes use of a thin film hyperspectral filter technology which is deposited onto an image sensor chip resulting in a compact and lightweight instrument design. This paper reports on the agricultural monitoring... B. Delauré, P. Baeck, J. Blommaert, S. Delalieux, S. Livens, A. Sima, M. Boonen, J. Goffart, G. Jacquemin, D. Nuyttens

18. Real-Time Fruit Detection Using Deep Neural Networks

Proximal imaging using tractor-mounted cameras is a simple and cost-effective method to acquire large quantities of data in orchards and vineyards. It can be used for the monitoring of vegetation and for the management of field operations such as the guidance of smart spraying systems for instance. One of the most prolific research subjects in arboriculture is fruit detection during the growing season. Estimations of fruit-load can be used for early yield assessments and for the monitoring of... B. Keresztes, J. Da costa, D. Randriamanga, C. Germain, F. Abdelghafour

19. Delineation of 'Management Classes' Within Non-Irrigated Maize Fields Using Readily Available Reflectance Data and Their Correspondence to Spatial Yield Variation

Maize is grown predominantly for silage or gain in North Island, New Zealand. Precision agriculture allows management of spatially variable paddocks by variably applying crop inputs tailored to distinctive potential-yield limiting areas of the paddock, known as management zones. However, uptake of precision agriculture among in New Zealand maize growers is slow and limited, largely due to lack of data, technical expertise and evidence of financial benefits. Reflectance data of satellite and areal... D.C. Ekanayake, J. Owens, A. Werner, A. Holmes

20. Variability Analysis of Temperature and Humidity for Control Optimization of a Hybrid Dehumidifier with a Heating Module for Greenhouses

Protected horticulture using greenhouses and also recently plant factories is becoming more popular, especially for high-value crops such as paprika, tomato, strawberry, due to year-round production of high yield and better quality crops under controlled environment. Temperature and humidity are most important ambient environmental factors for not only optimum crop growth but also disease control. This study was conducted to analyze vertical and spatial variability of temperature and humidity... Y. Seo, W. Lee, Y. Kim, S. Chung, S. Jang, I. Bae

21. Autonomous Mapping of Grass-Clover Ratio Based on Unmanned Aerial Vehicles and Convolutional Neural Networks

This paper presents a method which can provide support in determining the grass-clover ratio, in grass-clover fields, based on images from an unmanned aerial vehicle. Automated estimation of the grass-clover ratio can serve as a tool for optimizing fertilization of grass-clover fields. A higher clover content gives a higher performance of the cows, when the harvested material is used for fodder, and thereby this has a direct impact on the dairy industry. An android application... D. Larsen, S. Skovsen, K.A. Steen, K. Grooters, O. Green, R.N. Jørgensen, J. Eriksen

22. 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

23. Pest Detection on UAV Imagery Using a Deep Convolutional Neural Network

Presently, precision agriculture uses remote sensing for the mapping of crop biophysical parameters with vegetation indices in order to detect problematic areas, and then send a human specialist for a targeted field investigation. The same principle is applied for the use of UAVs in precision agriculture, but with finer spatial resolutions. Vegetation mapping with UAVs requires the mosaicking of several images, which results in significant geometric and radiometric problems. Furthermore, even... Y. Bouroubi, P. Bugnet, T. Nguyen-xuan, C. Bélec, L. Longchamps, P. Vigneault, C. Gosselin

24. Data Clustering Tools for Understanding Spatial Heterogeneity in Crop Production by Integrating Proximal Soil Sensing and Remote Sensing Data

Remote sensing (RS) and proximal soil sensing (PSS) technologies offer an advanced array of methods for obtaining soil property information and determining soil variability for precision agriculture. A large amount of data collected using these sensors may provide essential information for precision or site-specific management in a production field. In this paper, we introduced a new clustering technique was introduced and compared with existing clustering tools for determining relatively homogeneous... M. Saifuzzaman, V.I. Adamchuk, H. Huang, W. Ji, N. Rabe, A. Biswas

25. Development of an Online Decision-Support Infrastructure for Optimized Fertilizer Management

Determination of an optimum fertilizer application rate involves various influential factors, such as past management, soil characteristics, weather, commodity prices, cost of input materials and risk preference. Spatial and temporal variations in these factors constitute sources of uncertainties in selecting the most profitableapplication rate. Therefore, a decision support system (DSS) that could help to minimize production risks in the context of uncertain crop performance is needed. This... S. Shinde, V. Adamchuk, R. Lacroix, N. Tremblay, Y. Bouroubi

26. Analysis of Soil Properties Predictability Using Different On-the-Go Soil Mapping Systems

Understanding the spatial variability of soil chemical and physical attributes allows for the optimization of the profitability of nutrient and water management for crop development. Considering the advantages and accessibility of various types of multi-sensor platforms capable of acquiring large sensing data pertaining to soil information across a landscape, this study compares data obtained using four common soil mapping systems: 1) topography obtained using a real-time kinematic (RTK) global... H. Huang, V. Adamchuk, A. Biswas, W. Ji, S. Lauzon

27. Investigating Spatial Relationship of Apparent Electrical Conductivity with Turfgrass and Soil Characteristics in Sand-capped Golf Course Fairways

Turfgrass quality decreases when grown on fine textured soils that are irrigated with poor quality water. As a result, sand-capping (i.e., a sand layer above existing native soil) is now considered during golf course fairway renovation and construction. Mapping spatial variability of soil apparent electrical conductivity (ECa) has recently been suggested to have applications for precision turfgrass management (PTM) in native soil fairways, but sand-capped fairways have received less... C. Straw, B. Wyatt, A.P. Smith, K. Watkins, S. Hong, W. Floyd, D. Williams, C. Garza, T. Jansky

28. Can Topographic Indices Be Used for Irrigation Management Zone Delineation

Soil water movement is affected by soil physical properties and field terrain changes. The identification of within-field areas prone to excess or deficit of soil moisture could support the implementation of variable rate irrigation and adoption of irrigation scheduling strategies. This study evaluated the use of the topographic wetness index (TWI) and topographic position index (TPI) to understand and explain within-field soil moisture variability. Volumetric water content (VWC) collected in... B.V. Ortiz, B.P. Lena, F. morlin , G. Morata, M. Duarte de val, R. Prasad, A. Gamble

29. 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