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Velasquez, A.E
Patto Pacheco, E
Rahman, M.M
Taylor, J
Korsaeth, A
Yadav, P.K
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Authors
Isaksson, T
Korsaeth, A
Øvergaard, S
Stanley, J.S
Lamb, D.W
Trotter, M.G
Rahman, M.M
Patto Pacheco, E
Liu, J
Longchamps, L
Khosla, R
Becker, M
Velasquez, A.E
Guerrero, H.B
HIguti, V.A
Milori, D.M
Magalhães, D.V
Taylor, J
Shahar, Y
James, P
Blacker, C
Leese, S
Sanderson, R
Kavanagh, R
Taylor, J
Fassinou Hotegni, N
Karangwa, A
Manyatsi, A
Frimpong, K.A
Amri, M
Cammarano, D
Lesueur, C
Taylor, J
Phillips, S
Achigan-Dako, E
Kemeshi, J.O
Chang, Y
Yadav, P.K
Alahe, M
Frederick, Q
Burks, T
Yadav, P.K
Dewdney, M
Qin, J
Kim, M
Topics
Remote Sensing Applications in Precision Agriculture
Remote Sensing Applications in Precision Agriculture
Remote Sensing Applications in Precision Agriculture
Engineering Technologies and Advances
Geospatial Data
Industry Sponsors
Country Representative Report
Drivers and Barriers to Adoption of Precision Ag Technologies or Digital Agriculture
Artificial Intelligence (AI) in Agriculture
Type
Poster
Oral
Year
2012
2014
2016
2018
2022
2024
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Filter results9 paper(s) found.

1. Remote NIR-Sensor Fusion with Weather Data for Improved Prediction of Wheat Yield Models

Prediction models for grain yield based on remote sensing data are commonly shown to perform reasonably well for one single cropping season. The model performances often drop, however, when data from more years is included. This may be caused by biased data, resulting from diverging growth conditions from year to year, which affects... T. Isaksson, A. Korsaeth, S. Øvergaard

2. NDVI 'Depression' In Pastures Following Grazing

Pasture biomass estimation from normalized difference vegetation index (NDVI) using ground, air or space borne sensors is becoming more widely used in precision agriculture. Proximal active optical sensors (AOS) have the potential to eliminate the confounding effects of path radiance and target illumination conditions typically encountered using passive sensors. Any algorithm that infers the green fraction of pasture from NDVI must factor in plant morphology and live/dead plant ratio, irrespective... J.S. Stanley, D.W. Lamb, M.G. Trotter, M.M. Rahman

3. Detecting Nitrogen Variability at Early Growth Stages of Wheat by Active Fluorescence and NDVI

Low efficiency in the use of nitrogen fertilizer, has been reported around the world which often times result in high production costs and environmental damage. Today, unmanned aerial vehicles (UAV) cameras are being used to obtain conditions of crops, and can cover large areas in a short time. The objectives of this study were (i) to investigate N-variability in wheat at early growth stages using induced fluorescence indices, NDVI measured by active sensor and NDVI obtained by digital imagery;... E. Patto pacheco, J. Liu, L. Longchamps, R. Khosla

4. 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

5. Experiences in the Development of Commercial Web-Based Data Engines to Support UK Growers Within an Industry-Academic Partnership

The lifecycle of Precision Agriculture data begins the moment that the measurement is taken, after which it may pass through each multiple data processes until finally arriving as an output employed back in the production system. This flow can be hindered by the fact that many farm datasets have different spatial resolutions. This makes the process to aggregate or analyse multiple Precision Agriculture layers arduous and time consuming.  Precision Decisions Ltd located in Yorkshire,... J. Taylor, Y. Shahar, P. James, C. Blacker, S. Leese, R. Sanderson, R. Kavanagh

6. #DigitAg France

#DigitAg, the Digital Agriculture Convergence Laboratory, is one of 10 French Convergence Institutes financed by the Investissements d'Avenir (Investment for the Future) program. #DigitAg conducts interdisciplinary research between agronomic sciences, engineering sciences (computer science, mathematics, electronics, physics, etc.) and social and management sciences (economics, sociology, business management), bringing together more than 700 experts in these fields to produce the scientific... J. Taylor

7. Capacity Building of African Young Scientists in Precision Agriculture Through Cross-regional Academic Mobility for Enhanced Climate-smart Agri-food System: an Intra Africa Mobility Project on Precision Agriculture

Climate change is one of the main problems affecting food and nutrition globally, particularly in sub-Saharan Africa. Adapting to and/or mitigating climate change in the agri-food sector requires merging information technologies, genetic innovations, and sustainable farming practices to empower the agricultural youth sector to create effective and locally adapted solutions. Precision Agriculture applied to crops (PAAC), has been advocated as a strategic solution to mitigate/adapt agriculture at... N. Fassinou hotegni, A. Karangwa, A. Manyatsi, K.A. Frimpong, M. Amri, D. Cammarano, C. Lesueur, J. Taylor, S. Phillips, E. Achigan-dako

8. Comparing Global Shutter and Rolling Shutter Cameras for Image Data Collection in Motion on a UGV

In a bid to drive the adoption of precision farming (PF) technology by reducing the cost of developing an Unmanned Ground Vehicle (UGV), during the Reduction-To-Below-Two grand (R2B2) project we compared Arducam’s AR0234, a global shutter camera (GSC) to their IMX462, a rolling shutter camera (RSC). Since the cost of the AR0234 is approximately three times the price of the IMX462, the comparison was done to determine the possibility of using the latter for image data collection in place... J.O. Kemeshi, Y. Chang, P.K. Yadav, M. Alahe

9. Supervised Hyperspectral Band Selection Using Texture Features for Classification of Citrus Leaf Diseases with YOLOv8

Citrus greening disease (HLB), a disease caused by bacteria of the Candidatus Liberibacter group, is characterized by blotchy leaves and smaller fruits. Causing both premature fruit drop and eventual tree death, HLB is a novel and significant threat to the Florida citrus industry.  Citrus canker is another serious disease caused by the bacterium Xanthomonas citri subsp. citri (syn. X. axonopodis pv. citri) and causes economic losses for growers from fruit drops and blemishes. Citrus canker... Q. Frederick, T. Burks, P.K. Yadav, M. Dewdney, J. Qin, M. Kim