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Kim, H
Amaral, L.R
Kazula, M
Abon, J.O
Zuniga-Ramirez, G
Guppy, C.N
Zainal Abidin, M.B
García, C.E
Andretta, I
Gouton, P
Kelley, A
E. Flores, A
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Authors
Amaral, L.R
Portz, G
Rosa, H
Molin, J
Amaral, L.R
Molin, J.P
Taubinger, L
Chung, S
Kim, K
Kim, H
Choi, J
Zhang, Y
Kang, S
Han, K
Hur, S
Cointault, F
Gouton, P
Billiot, B
Zainal Abidin, M.B
Shibusawa, S
Ohaba, M
Li, Q
Kodaira, M
Khalid, M.B
Shibusawa, S
Ohaba, M
Zainal Abidin, M.B
Kodaira, M
Li, Q
Chung, S
Huh, Y
Choi, J
Ryu, D
Kim, K
Kim, H
Kim, H
Amaral, L.R
Molin, J.P
Jasper, J
Portz, G
Trotter, M.G
Lamb, D.W
Hinch, G.N
Guppy, C.N
Sauer, B
Guppy, C.N
Trotter, M.G
Lamb, D.W
Delgado, J.A
Kim, H
Sudduth, K.A
Cho, W
Kim, D
Kang, C
Kim, H
Son, J
Chung, S
Jiang, J
Yun, H
Fontenelli, J.V
Amaral, L.R
Demattê, J.M
Magalhães, P.G
Sanches, G
Sanches, G.M
Amaral, L.R
Pitrat, T
Brasco, T
Magalhaes, P.S
Duft, D.G
Franco, H.C
Michelon, G.K
Sanches, G.M
Valente, I.Q
Bazzi, C.L
de Menezes, P.L
Amaral, L.R
Magalhaes, P.G
Celades, J.A
Caicedo, J.H
García, C.E
Mora, H
Hauschild, L
Kristensen, A.R
Andretta, I
Pomar, C
Remus, A
Pomar, C
Andretta, I
Rivest, J
Hauschild, L
Pomar, J
Pomar, C
Andretta, I
Hauschild, L
Kipper, M
Pires, P.S
Zhou, C
Lee, W
Pourreza, A
Schueller, J.K
Liburd, O.E
Ampatzidis, Y
Zuniga-Ramirez, G
Kazula, M
Wieland, S
Kelley, A
Abon, J.O
Sharda, A
Lacerda, L
Miao, Y
Sharma, V
E. Flores, A
Kechchour, A
Lu, J
Topics
Proximal Sensing in Precision Agriculture
Precision Horticulture
Engineering Technologies and Advances
Modeling and Geo-statistics
Sensor Application in Managing In-season Crop Variability
Precision Livestock Management
Precision Nutrient Management
Precision Nutrient Management
Proximal Sensing in Precision Agriculture
Decision Support Systems
Profitability and Success Stories in Precision Agriculture
Precision Dairy and Livestock Management
Big Data, Data Mining and Deep Learning
Industry Sponsors
Digital Agriculture Solutions for Soil Health and Water Quality
Proximal and Remote Sensing of Soils and Crops (including Phenotyping)
Type
Poster
Oral
Year
2012
2010
2016
2018
2022
2024
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Filter results24 paper(s) found.

1. GNSS Tracking Of Livestock: Towards Variable Fertilizer Strategies For The Grazing Industry

This study reveals the potential for GPS tracking in the grazing industry. By monitoring the locations and movement of livestock, times of peak grazing activity can be identified and these can in turn produce maps of preferred grazing areas, and by examining residency times provide an indication of spatial variability in grazing pressure. A comparison of grazing preference can be made to similarly inferred camping areas to understand the potential redistribution of nutrients within a paddock.... M.G. Trotter, D.W. Lamb, G.N. Hinch, C.N. Guppy

2. Matching Nitrogen To Plant Available Water For Malting Barley On Highly Constrained Vertosol Soil

Crop yield monitoring, high resolution aerial imagery and electromagnetic induction (EMI) soil sensing are three widely used techniques in precision agriculture (PA). Yield maps provide an indication of the crop’s response to a particular management regime in light of spatially-variable constraints. Aerial imagery provides timely and accurate information about photosynthetically-active biomass during crop growth and EMI indicates spatial variability in soil texture, salinity and/or... B. Sauer, C.N. Guppy, M.G. Trotter, D.W. Lamb, J.A. Delgado

3. Laboratory Evaluation Of Ion-selective Electrodes For Simultaneous Analysis Of Macronutrients In Hydroponic Solution

... H. Kim, , , , K.A. Sudduth

4. Use of Active Crop Canopy Reflectance Sensor for Nitrogen Sugarcane Fertilization

Researches about the use of ground-based canopy reflectance sensors aiming the nitrogen management fertilization on variable-rate over the sugarcane crop have been conducted in São Paulo, Brazil since 2007. Sugarcane response to nitrogen is variable, making difficult the development of models to estimate its demands.... L.R. Amaral, G. Portz, H. Rosa, J. Molin

5. Vegetation Indices from Active Crop Canopy Sensor and Their Potential Interference Factors on Sugarcane

Among the inputs usually used in the sugarcane production the nitrogen (N) is the most significant. With the use of ground-based canopy sensors to obtain vegetation indexes (VI), it is possible to obtain recommendations of nutrient supply in... L.R. Amaral, J.P. Molin, L. Taubinger

6. Remote Control System for Greenhouse Environment Using Mobile Devices

Protected crop production facilities such as greenhouse and plant factory have drawn interest and the area is increasing in Korea as well as in other countries in the world. Remote... S. Chung, K. Kim, H. Kim, J. Choi, Y. Zhang, S. Kang, K. han, S. Hur

7. 3D Acquisition System Applied to Agronomic Scenes

To enable a better decision making by the farmer in order to optimize the crop management, it is essential to provide a set of information on basic parameters of the crops. These information are numerous and the image processing is increasingly used for disease detection, weed detection or yield estimation. We will focus initially on assessing the yield of a wheat crop in automatic way. This yield is directly related to the number of ears per square meter for which the counting is currently... F. Cointault, P. Gouton, B. Billiot

8. Transient Water Flow Model in a Soil-Plant System for Subsurface Precision Irrigation

The spatial variability of plant-water characteristic in the soil is still unclear. This limits the attempt to model the soil-plant-atmosphere system with this factor. Understanding the non-steady water flow along the soil-plant component is essential to understand their spatial variability.... M.B. Zainal abidin, S. Shibusawa, M. Ohaba, Q. Li, M. Kodaira, M.B. Khalid

9. Water Distribution Response in a Soil-Root System for Subsurface Precision Irrigation

A subsurface capillary irrigation system with a water source buried in a soil has been developed for precision irrigation. This system has advantages in the efficient irrigation to save much water and the real time measurement of evapotranspiration of plants. Creating this new subsurface capillary... S. Shibusawa, M. Ohaba, M.B. Zainal abidin, M. Kodaira, Q. Li

10. Determination of Sensor Locations for Monitoring of Soil Water Content in Greenhouse

 Monitoring and control of environmental condition is highly important for optimum control of the conditions, especially in greenhouse and plant factor, and the condition... S. Chung, Y. Huh, J. Choi, D. Ryu, K. Kim, H. Kim, H. Kim

11. Optimum Sugarcane Growth Stage for Canopy Reflectance Sensor to Predict Biomass and Nitrogen Uptake

The recent technology of plant canopy reflectance sensors can provide the status of biomass and nitrogen nutrition of sugarcane spatially and in real time, but it is necessary to know the right moment to use this technology aiming the best predictions of the crop parameters... L.R. Amaral, J.P. Molin, J. Jasper, G. Portz

12. Precision Nutrient Management System Based on Ion and Crop Growth Sensing

Automated sensing and variable-rate supply of nutrients in hydroponic solutions according to the status of crop growth would allow more efficient nutrient management for crop growth in closed systems. The Structure from Motion (SfM) method has risen as a new image sensing method to obtain 3D images of plants that can be used to estimate their growth, such as leaf cover area (LCA), plant height, and fresh weight. In this sense, sensor fusion technology combining ion-selective electrodes (ISEs)... W. Cho, D. Kim, C. Kang, H. Kim, J. Son, S. Chung, J. Jiang, H. Yun

13. 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

14. Apparent Electrical Conductivity Sensors and Their Relationship with Soil Properties in Sugarcane Fields

One important tool within the technological precision agriculture (PA) package are the apparent electrical conductivity (ECa) sensors. This kind of sensor shows the ability in mapping soil physicochemical variability quickly, with high resolution and at low cost. However, the adoption of this technology in Brazil is not usual, particularly on sugarcane fields. A major issue for farmers is the applicability of ECa, how to convert ECa data in knowledge that may assist the producer in decision-making... G.M. Sanches, L.R. Amaral, T. Pitrat, T. Brasco, P.S. Magalhaes, D.G. Duft, H.C. Franco

15. Optimized Soil Sampling Location in Management Zones Based on Apparent Electrical Conductivity and Landscape Attributes

One of the limiting factors to characterize the soil spatial variability is the need for a dense soil sampling, which prevents the mapping due to the high demand of time and costs. A technique that minimizes the number of samples needed is the use of maps that have prior information on the spatial variability of the soil, allowing the identification of representative sampling points in the field. Management Zones (MZs), a sub-area delineated in the field, where there is relative homogeneity in... G.K. Michelon, G.M. Sanches, I.Q. Valente, C.L. Bazzi, P.L. De menezes, L.R. Amaral, P.G. Magalhaes

16. Toward a Precision Agricultural Implementation for Sugar Cane Plantations in Southwestern Region of Colombia, South America

The Colombian Sugar Cane Research Center, CENICAÑA, has initiated an ambitious project for the implementation of Precision Agriculture (PA) technologies in the Cauca river valley region, where one of its main objectives is to have the ability to collect large volumes of geospatial data. The main sugarcane growers in the country perform their work in the selected work area, which covers an area of ​​approximately 242,000 ha, characterized by diverse topographic and edaphic conditions.... J.A. Celades, J.H. Caicedo, C.E. García, H. Mora

17. Dynamic Feeding Intake Monitoring in Growing-Finishing Pigs Reared Under Precision Feeding Strategies

Pigs exposed to challenges with no prior experience change their daily feeding intake pattern. A method identifying deviations from normal feeding patterns could be used to develop a model framework to estimate individual nutrient requirements of challenged pigs fed with precision feeding systems. The objective of this study was to develop a tool for early identification of feed intake deviations in precision fed growing-finishing pigs. Feed intake measurements collected during 84 d in 126 growing–finishing... L. Hauschild, A.R. Kristensen, I. Andretta, C. Pomar, A. Remus

18. Precision Feeding Can Significantly Reduce Lysine Intake and Nitrogen Excretion Without Compromising the Performance of Growing Pigs

The impact of using a mathematical model estimating real-time daily lysine requirements in a sustainable precision feeding program for growing pigs was investigated in two performance trials. Three treatments were tested in the first trial (60 pigs of 41.2±0.5 kg): a three-phase feeding program (3P) obtained by blending fixed proportions of feeds A (high nutrient concentration) and B (low nutrient concentration); and two daily-phase feeding programs in which the blended proportions of feeds... C. Pomar, I. Andretta, J. Rivest, L. Hauschild, J. Pomar

19. Environmental Impacts of Precision Feeding Programs Applied in Brazilian Pig Production

This study was undertaken to evaluate the effect that switching from conventional to precision feeding systems during the growing-finishing phase would have on the potential environmental impact of Brazilian pig production. Standard life-cycle assessment procedures were used, with a cradle-to-farm gate boundary. The inputs and outputs of each interface of the life cycle were organized in a model. Grain production was independently characterized in the Central-West and South regions of Brazil,... C. Pomar, I. Andretta, L. Hauschild, M. Kipper, P.S. Pires

20. Strawberry Pest Detection Using Deep Learning and Automatic Imaging System

Strawberry growers need to monitor pests to determine the options for pest management to reduce damage to yield and quality.  However, manually counting strawberry pests using a hand lens is time-consuming and biased by the observer. Therefore, an automated rapid pest scouting method in the strawberry field can save time and improve counting consistency. This study utilized six cameras to take images of the strawberry leaf. Due to the relatively small size of the strawberry pest, six cameras... C. Zhou, W. Lee, A. Pourreza, J.K. Schueller, O.E. Liburd, Y. Ampatzidis, G. Zuniga-ramirez

21. Minnesota Corn Growers Association

With more than 6,500 members, the Minnesota Corn Growers Association is one of the largest grassroots farm organizations in the United States. Working in close partnership with the Minnesota Corn Research & Promotion Council, MCGA identifies and promotes opportunities for Minnesota’s 24,000 corn farmers while building connections with the non-farming public. We accomplish this by investing in third-party research that focuses on water quality and soil health, targeted consumer outreach,... M. Kazula

22. EarthScout, GBC

EarthScout is a precision remote sensor technology that provides farmers and researchers with reliable data in real time, straight from your field to your desktop and mobile devices. In season data allows users to access current conditions for smarter decision making in irrigation and nitrogen management. EarthScout is a crop agnostic tool that is used in any soil type and climate. Our plug and play field sensors need no calibration and set up only takes about 5 minutes. There are no data subscription... S. Wieland, A. Kelley

23. Optimizing Corn Irrigation Strategies: Insights from NDVI Trends, Soil Moisture Dynamics, and Remote Sensing

This comprehensive field experiment systematically examines the impact of varied irrigation rates on corn growth and yield across three treatments: 33%, 67%, and 100% irrigation rates. Utilizing the normalized difference vegetation index (NDVI) as a parameter for vegetation health, distinct patterns emerge throughout key growth stages. The 100% irrigation treatment consistently exhibits superior vegetation health, sustaining higher NDVI values across all stages, while the 33% treatment reveals... J.O. Abon, A. Sharda

24. Estimating Water and Nitrogen Deficiency in Corn Using a Multi-parameter Proximal Sensor

The Crop Circle Phenom (CCP) is an innovative integrated proximal sensor that can be potentially used to perform in-season diagnosis of nitrogen and water status. In addition to measuring spectral reflectance in several bands including the red, red edge, and near-infrared wavelengths, the CCP can also measure canopy and air temperatures and provides several parameters that can be associated with chlorophyll content, crop vigor, and water status. These capabilities differentiate the CCP from other... L. Lacerda, Y. Miao, V. Sharma, A. E. flores, A. Kechchour, J. Lu