Proceedings :: International Society of Precision Agriculture

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Gupta, M

Antunes, J.F

Dhiman, V

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Authors

Gupta, M

Khosla, R

Pereira, F.R

Dos Reis, A.A

Freitas, R.G

Oliveira, S.R

Amaral, L.R

Figueiredo, G.K

Antunes, J.F

Lamparelli, R.A

Moro, E

Pereira, N.D

Magalhães, P.S

Barai, K

Ewanik, C

Dhiman, V

Zhang, Y

Hodeghatta, U.R

Zhang, Y

Hodeghatta, U.R

Dhiman, V

Barai, K

Trang, T

Topics

Precision Nutrient Management

Big Data, Data Mining and Deep Learning

Proximal and Remote Sensing of Soils and Crops (including Phenotyping)

Data Analytics for Production Ag

Type

Poster

Oral

Year

2012

2022

2024

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Filter results4 paper(s) found.
1. Precision Nitrogen Management and Global Nitrogen Use Efficiency Traditionally, nitrogen (N) fertilizers have been applied uniformly across entire field while ignoring inherent spatial variation in crop N needs across crop fields. This results in either too little or too much application of N in various parts of the fields.... M. Gupta, R. Khosla Download paper
2. A Framework for Imputation of Missing Parts in UAV Orthomosaics Using Planetscope and Sentinel-2 Data In recent years, the emergence of Unmanned Aerial Vehicles (UAV), also known as drones, with high spatial resolution, has broadened the application of remote sensing in agriculture. However, UAV images commonly have specific problems with missing areas due to drone flight restrictions. Data mining techniques for imputing missing data is an activity often demanded in several fields of science. In this context, this research used the same approach to predict missing parts on orthomosaics obtained... F.R. Pereira, A.A. Dos reis, R.G. Freitas, S.R. Oliveira, L.R. Amaral, G.K. Figueiredo, J.F. Antunes, R.A. Lamparelli, E. Moro, N.D. Pereira, P.S. Magalhães
3. Airborne Spectral Detection of Leaf Chlorophyll Concentration in Wild Blueberries Leaf chlorophyll concentration (LCC) detection is crucial for monitoring crop physiological status, assessing the overall health of crops, and estimating their photosynthetic potential. Fast, non-destructive, and spatially extensive monitoring of LCC in crops is critical for accurately diagnosing and assessing crop health in large commercial fields. Advancements in hyperspectral remote sensing offer non-destructive and spatially extensive alternatives for monitoring plant parameters such as LCC.... K. Barai, C. Ewanik, V. Dhiman, Y. Zhang, U.R. Hodeghatta Download paper
4. Predicting Water Potentials of Wild Blueberries During Drought Treatment Using Hyperspectral Sensor and Machine Learning Detecting water stress on crops early and accurately is crucial to minimize its impact. This study aims to measure water stress in wild blueberry crops non-destructively by analyzing proximal hyperspectral data. The data collection took place in the summer growing season of 2022. A drought experiment was conducted on wild blueberries in the randomized block design in the greenhouse, incorporating various genotypes and irrigation treatments. Hyperspectral data ( spectral range: 400-1000 nm) using... Y. Zhang, U.R. Hodeghatta, V. Dhiman, K. Barai, T. Trang Download paper

Filter results4 paper(s) found.

1. Precision Nitrogen Management and Global Nitrogen Use Efficiency

Traditionally, nitrogen (N) fertilizers have been applied uniformly across entire field while ignoring inherent spatial variation in crop N needs across crop fields. This results in either too little or too much application of N in various parts of the fields.... M. Gupta, R. Khosla

Download paper

2. A Framework for Imputation of Missing Parts in UAV Orthomosaics Using Planetscope and Sentinel-2 Data

In recent years, the emergence of Unmanned Aerial Vehicles (UAV), also known as drones, with high spatial resolution, has broadened the application of remote sensing in agriculture. However, UAV images commonly have specific problems with missing areas due to drone flight restrictions. Data mining techniques for imputing missing data is an activity often demanded in several fields of science. In this context, this research used the same approach to predict missing parts on orthomosaics obtained... F.R. Pereira, A.A. Dos reis, R.G. Freitas, S.R. Oliveira, L.R. Amaral, G.K. Figueiredo, J.F. Antunes, R.A. Lamparelli, E. Moro, N.D. Pereira, P.S. Magalhães

3. Airborne Spectral Detection of Leaf Chlorophyll Concentration in Wild Blueberries

Leaf chlorophyll concentration (LCC) detection is crucial for monitoring crop physiological status, assessing the overall health of crops, and estimating their photosynthetic potential. Fast, non-destructive, and spatially extensive monitoring of LCC in crops is critical for accurately diagnosing and assessing crop health in large commercial fields. Advancements in hyperspectral remote sensing offer non-destructive and spatially extensive alternatives for monitoring plant parameters such as LCC.... K. Barai, C. Ewanik, V. Dhiman, Y. Zhang, U.R. Hodeghatta

Download paper

4. Predicting Water Potentials of Wild Blueberries During Drought Treatment Using Hyperspectral Sensor and Machine Learning

Detecting water stress on crops early and accurately is crucial to minimize its impact. This study aims to measure water stress in wild blueberry crops non-destructively by analyzing proximal hyperspectral data. The data collection took place in the summer growing season of 2022. A drought experiment was conducted on wild blueberries in the randomized block design in the greenhouse, incorporating various genotypes and irrigation treatments. Hyperspectral data ( spectral range: 400-1000 nm) using... Y. Zhang, U.R. Hodeghatta, V. Dhiman, K. Barai, T. Trang

Download paper