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Jia, M

Zaman, Q

Znoj, E

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Authors

Zaman, Q

Schumann, A.W

Percival, D.C

Esau, T.J

Read, S.M

Farooque, A.A

Zaman, Q

Schumann, A.W

Percival, D.C

Esau, T.J

Stauffer, T

Kross, A

Kaur, G

Znoj, E

Callegari, D

Sunohara, M

McNairn, H

Lapen, D

Rudy, H

van Vliet, L

Lu, J

Chen, Z

Miao, Y

Li, Y

Zhang, Y

Zhao, X

Jia, M

Topics

Engineering Technologies and Advances

Spatial Variability in Crop, Soil and Natural Resources

Proximal and Remote Sensing of Soil and Crop (including Phenotyping)

In-Season Nitrogen Management

Type

Oral

Poster

Year

2010

2018

2022

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Filter results4 paper(s) found.
1. Performance Evaluation Of A Prototype Variable Rate Sprayer For Spot- Application Of Agrochemicals In Wild Blueberry Fields Wild blueberry yields are highly dependent on agrochemicals for adequate weed control. The excessive use of agrochemicals with uniform application in significant bare spots and plant areas has resulted in increased cost of production. A cost-effective automated prototype variable rate (VR) sprayer was developed for spot-application (SA) of agrochemicals in a specific section of the sprayer boom where the weeds have been detected. The weed patches were mapped with an RTK-GPS... Q. Zaman, A.W. Schumann, D.C. Percival, T.J. Esau, S.M. Read Download paper
2. Estimating Soil Moisture And Organic Matter Content Variabality Using Electromagnatic Induction Metod Abstract: Electromagnetic induction (EMI) methods are gaining popularity due to their non-destructive nature, rapid response and ease of integration into mobile platforms for assessment of the soil moisture content, water table depth, and salinity etc. The objective of this study was to estimate and map soil moisture content and organic matter content using DualEM.... A. Farooque, Q. Zaman, A.W. Schumann, D.C. Percival, T.J. Esau, T. Stauffer
3. Evaluation of an Artificial Neural Network Approach for Prediction of Corn and Soybean Yield The ability to predict crop yield during the growing season is important for crop income, insurance projections and for evaluating food security. Yet, modeling crop yield is challenging because of the complexity of the relationships between crop growth and the interrelated predictor variables. Artificial neural networks (ANNs) are useful for such complex systems as they can capture non-linear relationships of data without explicitly knowing the underlying processes. In this study, an ANN-based... A. Kross, G. Kaur, E. Znoj, D. Callegari, M. Sunohara, H. Mcnairn, D. Lapen, H. Rudy, L. Van vliet Download paper
4. In-season Diagnosis of Winter Wheat Nitrogen Status Based on Rapidscan Sensor Using Machine Learning Coupled with Weather Data Nitrogen nutrient index (NNI) is widely used as a good indicator to evaluate the N status of crops in precision farming. However, interannual variation in weather may affect vegetation indices from sensors used to estimate NNI and reduce the accuracy of N diagnostic models. Machine learning has been applied to precision N management with unique advantages in various variables analysis and processing. The objective of this study is to improve the N status diagnostic model for winter wheat by combining... J. Lu, Z. Chen, Y. Miao, Y. Li, Y. Zhang, X. Zhao, M. Jia Download paper

Filter results4 paper(s) found.

1. Performance Evaluation Of A Prototype Variable Rate Sprayer For Spot- Application Of Agrochemicals In Wild Blueberry Fields

Wild blueberry yields are highly dependent on agrochemicals for adequate weed control. The excessive use of agrochemicals with uniform application in significant bare spots and plant areas has resulted in increased cost of production. A cost-effective automated prototype variable rate (VR) sprayer was developed for spot-application (SA) of agrochemicals in a specific section of the sprayer boom where the weeds have been detected. The weed patches were mapped with an RTK-GPS... Q. Zaman, A.W. Schumann, D.C. Percival, T.J. Esau, S.M. Read

Download paper

2. Estimating Soil Moisture And Organic Matter Content Variabality Using Electromagnatic Induction Metod

Abstract: Electromagnetic induction (EMI) methods are gaining popularity due to their non-destructive nature, rapid response and ease of integration into mobile platforms for assessment of the soil moisture content, water table depth, and salinity etc. The objective of this study was to estimate and map soil moisture content and organic matter content using DualEM.... A. Farooque, Q. Zaman, A.W. Schumann, D.C. Percival, T.J. Esau, T. Stauffer

3. Evaluation of an Artificial Neural Network Approach for Prediction of Corn and Soybean Yield

The ability to predict crop yield during the growing season is important for crop income, insurance projections and for evaluating food security. Yet, modeling crop yield is challenging because of the complexity of the relationships between crop growth and the interrelated predictor variables. Artificial neural networks (ANNs) are useful for such complex systems as they can capture non-linear relationships of data without explicitly knowing the underlying processes. In this study, an ANN-based... A. Kross, G. Kaur, E. Znoj, D. Callegari, M. Sunohara, H. Mcnairn, D. Lapen, H. Rudy, L. Van vliet

Download paper

4. In-season Diagnosis of Winter Wheat Nitrogen Status Based on Rapidscan Sensor Using Machine Learning Coupled with Weather Data

Nitrogen nutrient index (NNI) is widely used as a good indicator to evaluate the N status of crops in precision farming. However, interannual variation in weather may affect vegetation indices from sensors used to estimate NNI and reduce the accuracy of N diagnostic models. Machine learning has been applied to precision N management with unique advantages in various variables analysis and processing. The objective of this study is to improve the N status diagnostic model for winter wheat by combining... J. Lu, Z. Chen, Y. Miao, Y. Li, Y. Zhang, X. Zhao, M. Jia

Download paper