Proceedings :: International Society of Precision Agriculture

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Westbrook, J

Kovacs, A

Abbasi, E

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Authors

Zhang, H

Lan, Y

Westbrook, J

Suh, C

Hoffmann, C

Lacey, R

Lan, Y

Hoffmann, W.C

Westbrook, J

Zaller, M

Aliloo, J

Abbasi, E

Karamidehkordi , E

Ghanbari Parmehr, E

Canavari, M

Vitali, G.-

Zsebő, S

Kukorelli, G

Vona, V

Bede, L

Stencinger, D

Kovacs, A

Milics, G

Kulmany, I.M

Horváth, B

Hegedűs, G

Abdinoor, J.A

Topics

Sensor Application in Managing In-season Crop Variability

Modeling and Geo-statistics

Drivers and Barriers to Adoption of Precision Ag Technologies or Digital Agriculture

Scouting and Field Data collection with Unmanned Aerial Systems

Type

Poster

Oral

Year

2010

2024

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Filter results4 paper(s) found.
1. 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 Download paper
2. Development Of A Decision Support System For Precision Areawide Pest Management In Cotton Production Crop models simulate growth and development, and provide relevant information for the routine management of the crop. The use of crop models on large areas for diagnosing crop growing conditions or predicting crop production is hampered by the lack of sufficient spatial information about model inputs. Integrating crop models with other information technologies such as geographic information systems (GIS), variable rate technology, remote sensing, and global positioning... Y. Lan, W.C. Hoffmann, J. Westbrook, M. Zaller
3. Content Analysis of the Challenges of Using Drones in Paddy Fields in the Haraz Plain Watershed, Iran Drone technology has gained popularity in recent years as a sustainable solution to changing agricultural conditions. Using drones in agriculture provides many advantages in farm management. However, the use of drones in paddy fields in Iran is a new phenomenon facing numerous challenges. This study aims to explore the challenges for using drones in paddy fields and provide practical guidelines to solve the challenges facing the their application. This research was conducted with a qualitative... J. Aliloo, E. Abbasi, E. Karamidehkordi , E. Ghanbari parmehr, M. Canavari, G.-. Vitali Download paper
4. Comparison of NDVI Values at Different Phenological Stages of Winter Wheat (Triticum Aestivum L.) The main objective of this study is to monitor, detect and quantify the presence of live green vegetation with the MicaSense RedEdge-MX Dual Camera System (MS) mounted on a DJI Matrice 210 V2 and GreenSeeker HCS 250 (GS) in winter wheat (Triticum aestivum L.) by using Normalized Difference Vegetation Index (NDVI). Surveys were conducted in the North-Western part of Hungary, in Mosonmagyaróvár on six different dates. A small-scale field trial in winter wheat was constructed as a randomized... S. Zsebő, G. Kukorelli, V. Vona, L. Bede, D. Stencinger, A. Kovacs, G. Milics, I.M. Kulmany, B. Horváth, G. Hegedűs, J.A. Abdinoor Download paper

Filter results4 paper(s) found.

1. 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

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2. Development Of A Decision Support System For Precision Areawide Pest Management In Cotton Production

Crop models simulate growth and development, and provide relevant information for the routine management of the crop. The use of crop models on large areas for diagnosing crop growing conditions or predicting crop production is hampered by the lack of sufficient spatial information about model inputs. Integrating crop models with other information technologies such as geographic information systems (GIS), variable rate technology, remote sensing, and global positioning... Y. Lan, W.C. Hoffmann, J. Westbrook, M. Zaller

3. Content Analysis of the Challenges of Using Drones in Paddy Fields in the Haraz Plain Watershed, Iran

Drone technology has gained popularity in recent years as a sustainable solution to changing agricultural conditions. Using drones in agriculture provides many advantages in farm management. However, the use of drones in paddy fields in Iran is a new phenomenon facing numerous challenges. This study aims to explore the challenges for using drones in paddy fields and provide practical guidelines to solve the challenges facing the their application. This research was conducted with a qualitative... J. Aliloo, E. Abbasi, E. Karamidehkordi , E. Ghanbari parmehr, M. Canavari, G.-. Vitali

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4. Comparison of NDVI Values at Different Phenological Stages of Winter Wheat (Triticum Aestivum L.)

The main objective of this study is to monitor, detect and quantify the presence of live green vegetation with the MicaSense RedEdge-MX Dual Camera System (MS) mounted on a DJI Matrice 210 V2 and GreenSeeker HCS 250 (GS) in winter wheat (Triticum aestivum L.) by using Normalized Difference Vegetation Index (NDVI). Surveys were conducted in the North-Western part of Hungary, in Mosonmagyaróvár on six different dates. A small-scale field trial in winter wheat was constructed as a randomized... S. Zsebő, G. Kukorelli, V. Vona, L. Bede, D. Stencinger, A. Kovacs, G. Milics, I.M. Kulmany, B. Horváth, G. Hegedűs, J.A. Abdinoor

Download paper