Proceedings :: International Society of Precision Agriculture

Proceedings

Find matching any: Reset

Rasooli Sharabian, V

Ranieri, E

» Add more authors

Add filter to result:

Authors

Han-ya, I

Ishii, K

Noguchi, N

Rasooli Sharabian, V

Karampoiki, M

Todman, L

Mahmood, S

Murdoch, A

Paraforos, D

Hammond, J

Ranieri, E

Topics

Remote Sensing Applications in Precision Agriculture

Big Data, Data Mining and Deep Learning

Type

Poster

Oral

Year

2012

2022

Home » Authors » Results

Authors

Filter results2 paper(s) found.
1. Appropriate Wavelengths for Winter Wheat Growth Status Based On Multi-Spectral Crop Reflectance Data One of the applications of remote sensing in agriculture is to obtain crop status for estimation and management of variable rate of inputs in the crop production. In order to select the appropriate wavelengths related... I. Han-ya, K. Ishii, N. Noguchi, V. Rasooli sharabian Download paper
2. A Bayesian Network Approach to Wheat Yield Prediction Using Topographic, Soil and Historical Data Bayesian Network (BN) is the most popular approach for modeling in the agricultural domain. Many successful applications have been reported for crop yield prediction, weed infestation, and crop diseases. BN uses probabilistic relationships between variables of interest and in combination with statistical techniques the data modeling has many advantages. The main advantages are that the relationships between variables can be learned using the model as well as the potential to deal with missing... M. Karampoiki, L. Todman, S. Mahmood, A. Murdoch, D. Paraforos, J. Hammond, E. Ranieri Download paper

Filter results2 paper(s) found.

1. Appropriate Wavelengths for Winter Wheat Growth Status Based On Multi-Spectral Crop Reflectance Data

One of the applications of remote sensing in agriculture is to obtain crop status for estimation and management of variable rate of inputs in the crop production. In order to select the appropriate wavelengths related... I. Han-ya, K. Ishii, N. Noguchi, V. Rasooli sharabian

Download paper

2. A Bayesian Network Approach to Wheat Yield Prediction Using Topographic, Soil and Historical Data

Bayesian Network (BN) is the most popular approach for modeling in the agricultural domain. Many successful applications have been reported for crop yield prediction, weed infestation, and crop diseases. BN uses probabilistic relationships between variables of interest and in combination with statistical techniques the data modeling has many advantages. The main advantages are that the relationships between variables can be learned using the model as well as the potential to deal with missing... M. Karampoiki, L. Todman, S. Mahmood, A. Murdoch, D. Paraforos, J. Hammond, E. Ranieri

Download paper