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Duff, H.D
López, J.D
Leithold, P
D.C, H
Landivar, J
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Authors
Leithold, P
Volk, T
Dammer, K
Dr., N
T, S
giriyappa, M
D.C, H
PATIL, B
Prabhudeva, D
Kombali, G
Noorasma, S
Thimmegowda, M
Lan, Y
Huang, Y
Martin, D.E
Hoffmann, W.C
Fritz, B.K
López, J.D
Maxwell, B.D
Hegedus, P.D
Loewen, S.D
Duff, H.D
Sheppard, J.W
Peerlinck, A.D
Morales, G.L
Bekkerman, A
Bhandari, M
Landivar, J
Ghansah, B
Zhao, L
Landivar, J
Pal, P
Fernandez, O
Bhandari, M
Landivar-Scoot, J.L
Eldefrawy, M
Zhao, L
Landivar, J
Topics
Precision Crop Protection
Precision Nutrient Management
Remote Sensing Application / Sensor Technology
Decision Support Systems
Artificial Intelligence (AI) in Agriculture
Data Analytics for Production Ag
Type
Oral
Poster
Year
2014
2016
2008
2022
2024
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Authors

Filter results6 paper(s) found.

1. Fungiprecise - A German Project For Precise Real-Time Fungicide Application In Winter Wheat

Regarding to real-time or online technologies in recent years, new technologies has been introduced into practical farming especially in the field of nitrogen application. These technologies are based on sensors mainly detecting the canopy reflectance. In the field of plant protection, although few sensor-based real-time technologies in weed control and growth regulator application are marked available, solutions for fungicide application are mostly missing currently. Amongst others... P. Leithold, T. Volk, K. Dammer

2. 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

3. Development of an Airborne Remote Sensing System for Aerial Applicators

An airborne remote sensing system was developed and tested for recording aerial images of field crops, which were analyzed for variations of crop health or pest infestation. The multicomponent system consists of a multi-spectral camera system, a camera control system, and a radiometer for normalizing images. To overcome the difficulties currently associated with correlating imagery data with what is actually occurring on the ground (a process known as ground truthing); a hyperspectral reflectance... Y. Lan, Y. Huang, D.E. Martin, W.C. Hoffmann, B.K. Fritz, J.D. López

4. Decision Support from On-field Precision Experiments

Empirically driven adaptive management in large-scale commodity crop production has become possible with spatially controlled application and sub-field scale crop monitoring technology. Site-specific experimentation is fundamental to an agroecosystem adaptive management (AAM) framework that results in information for growers to make informed decisions about their practices. Crop production and quality response data from combine harvester mounted sensors and internet available remote sensing data... B.D. Maxwell, P.D. Hegedus, S.D. Loewen, H.D. Duff, J.W. Sheppard, A.D. Peerlinck, G.L. Morales, A. Bekkerman

5. Cotton Yield Estimation Using High-resolution Satellite Imagery Obtained from Planet SkySat

Satellite images have been used to monitor and estimate crop yield. Over the years, significant improvements on spatial resolution have been made where ortho images can be generated at 30-centimeter resolution. In this study, we wanted to explore the potential use of Planet SKYSAT satellite system for cotton yield predictions. This system provided imagery data at 50 centimeters resolution, and we collected data 14 times during the season. The data were collected from two different cotton... M. Bhandari

6. Ground-based Imagery Data Collection of Cotton Using a Robotic Platform

In modern agriculture, technological advancements are pivotal in optimizing crop production and resource management. Integrating robotics and image processing techniques allows the efficient collection, analysis, and storage of high-resolution images crucial for monitoring crop health, identifying pest infestations, assessing growth stages, making precise management decisions and predicting yield potential. The objective of this project is to utilize the Farm-NG Amiga robot to develop an image... O. Fernandez, M. Bhandari, J.L. Landivar-scoot, M. Eldefrawy, L. Zhao, J. Landivar