Proceedings :: International Society of Precision Agriculture

Proceedings

Find matching any: Reset

Mouazen, A.M

Mathew, J.J

» Add more authors

Add filter to result:

Authors

Nawar, S.M

Mouazen, A.M

George, D

Manfield , A

Mathew, J.J

Flores, P.J

Stenger, J

Miranda, C

Zhang, Z

Das, A.K

Topics

Proximal Sensing in Precision Agriculture

Precision Agriculture and Global Food Security

Type

Oral

Year

2016

2022

Home » Authors » Results

Authors

Filter results2 paper(s) found.
1. Comparing Predictive Performance of Near Infrared Spectroscopy at a Field, Regional, National and Continental Scales by Using Spiking and Data Mining Techniques The development of accurate visible and near infrared (vis-NIR) spectroscopy calibration models for selected soil properties is a crucial step for variable rate application in precision agriculture. The objective of the present study was to compare the prediction performance of vis-NIR spectroscopy at local, regional, national and continental scales using data mining techniques including spiking. Fresh soil samples collected from farms in the UK, Czech Republic, Germany, Denmark and the Netherlands... S.M. Nawar, A.M. Mouazen, D. George, A. Manfield Download paper
2. Comparative Analysis of Light-weight Deep Learning Architectures for Soybean Yield Estimation Based on Pod Count from Proximal Sensing Data for Mobile and Embedded Vision Applications Crop yield prediction is an important aspect of farming and food-production. Therefore, estimating yield is important for crop breeders, seed-companies, and farmers to make informed real-time financial decisions. In-field soybean (Glycine max L.(Merr.)) yield estimation can be of great value to plant breeders as they screen thousands of plots to identify better yielding genotypes that ultimately will strengthen national food security. Existing soybean yield estimation tools,... J.J. Mathew, P.J. Flores, J. Stenger, C. Miranda, Z. Zhang, A.K. Das Download paper

Filter results2 paper(s) found.

1. Comparing Predictive Performance of Near Infrared Spectroscopy at a Field, Regional, National and Continental Scales by Using Spiking and Data Mining Techniques

The development of accurate visible and near infrared (vis-NIR) spectroscopy calibration models for selected soil properties is a crucial step for variable rate application in precision agriculture. The objective of the present study was to compare the prediction performance of vis-NIR spectroscopy at local, regional, national and continental scales using data mining techniques including spiking. Fresh soil samples collected from farms in the UK, Czech Republic, Germany, Denmark and the Netherlands... S.M. Nawar, A.M. Mouazen, D. George, A. Manfield

Download paper

2. Comparative Analysis of Light-weight Deep Learning Architectures for Soybean Yield Estimation Based on Pod Count from Proximal Sensing Data for Mobile and Embedded Vision Applications

Crop yield prediction is an important aspect of farming and food-production. Therefore, estimating yield is important for crop breeders, seed-companies, and farmers to make informed real-time financial decisions. In-field soybean (Glycine max L.(Merr.)) yield estimation can be of great value to plant breeders as they screen thousands of plots to identify better yielding genotypes that ultimately will strengthen national food security. Existing soybean yield estimation tools,... J.J. Mathew, P.J. Flores, J. Stenger, C. Miranda, Z. Zhang, A.K. Das

Download paper