Proceedings :: International Society of Precision Agriculture

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Arnold, S

Anup, A

Almallahi, A

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Authors

Abukmeil, R

Almallahi, A

Javed, B

Cambouris, A

Duchemin, M

Longchamps, L

Basran, P.S

Arnold, S

Fenech, A

Karam, A

Waltz, L

Khanal, S

Katari, S

Hong, C

Anup, A

Colbert, J

Potlapally, A

Dill, T

Porter, C

Engle, J

Stewart, C

Subramoni, H

Machiraju, R

Ortez, O

Lindsey, L

Nandi, A

Topics

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

Precision Agriculture and Global Food Security

Artificial Intelligence (AI) in Agriculture

Type

Oral

Year

2022

2024

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Filter results3 paper(s) found.
1. Developing Empirical Method to Estimate Phosphorous in Potato Plants Using Spectroscopy-based Approach Application of non-destructive sensors opens a promising opportunity to provide efficient information on nutrient contents based on leaf or canopy reflectance in different crops. In potatoes, nutrient levels are estimated by conducting chemical tests for the petioles. In thinking of deploying sensors for potato nutrient estimation, it is necessary to study the spectrum based on petiole chemical testing rather than leaf chemical testing. Thus, this study aimed to investigate whether there is a... R. Abukmeil, A. Almallahi Download paper
2. In-season Nitrogen Prediction Evaluation Using Airborne Imagery with AI Techniques in Commercial Potato Production In modern agriculture, timely and precise nitrogen (N) monitoring is essential to optimize resource management and improve trade benefits. Potato (Solanum tuberosum L.) is a staple food in many regions of the world, and improving its production is inevitable to ensure food security and promote related industries. Traditional methods of assessing nitrogen are labour-intensive, time-consuming, and require subjective observations. To address these limitations, a combination of multispectral... B. Javed, A. Cambouris, M. Duchemin, L. Longchamps, P.S. Basran, S. Arnold, A. Fenech, A. Karam Download paper
3. Cyberinfrastructure for Machine Learning Applications in Agriculture: Experiences, Analysis, and Vision Advancements in machine learning algorithms and GPU computational speeds over the last decade have led to remarkable progress in the capabilities of machine learning. This progress has been so much that, in many domains, including agriculture, access to sufficiently diverse and high-quality datasets has become a limiting factor. While many agricultural use cases appear feasible with current compute resources and machine learning algorithms, the lack of software infrastructure for collecting,... L. Waltz, S. Khanal, S. Katari, C. Hong, A. Anup, J. Colbert, A. Potlapally, T. Dill, C. Porter, J. Engle, C. Stewart, H. Subramoni, R. Machiraju, O. Ortez, L. Lindsey, A. Nandi Download paper

Filter results3 paper(s) found.

1. Developing Empirical Method to Estimate Phosphorous in Potato Plants Using Spectroscopy-based Approach

Application of non-destructive sensors opens a promising opportunity to provide efficient information on nutrient contents based on leaf or canopy reflectance in different crops. In potatoes, nutrient levels are estimated by conducting chemical tests for the petioles. In thinking of deploying sensors for potato nutrient estimation, it is necessary to study the spectrum based on petiole chemical testing rather than leaf chemical testing. Thus, this study aimed to investigate whether there is a... R. Abukmeil, A. Almallahi

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2. In-season Nitrogen Prediction Evaluation Using Airborne Imagery with AI Techniques in Commercial Potato Production

In modern agriculture, timely and precise nitrogen (N) monitoring is essential to optimize resource management and improve trade benefits. Potato (Solanum tuberosum L.) is a staple food in many regions of the world, and improving its production is inevitable to ensure food security and promote related industries. Traditional methods of assessing nitrogen are labour-intensive, time-consuming, and require subjective observations. To address these limitations, a combination of multispectral... B. Javed, A. Cambouris, M. Duchemin, L. Longchamps, P.S. Basran, S. Arnold, A. Fenech, A. Karam

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3. Cyberinfrastructure for Machine Learning Applications in Agriculture: Experiences, Analysis, and Vision

Advancements in machine learning algorithms and GPU computational speeds over the last decade have led to remarkable progress in the capabilities of machine learning. This progress has been so much that, in many domains, including agriculture, access to sufficiently diverse and high-quality datasets has become a limiting factor. While many agricultural use cases appear feasible with current compute resources and machine learning algorithms, the lack of software infrastructure for collecting,... L. Waltz, S. Khanal, S. Katari, C. Hong, A. Anup, J. Colbert, A. Potlapally, T. Dill, C. Porter, J. Engle, C. Stewart, H. Subramoni, R. Machiraju, O. Ortez, L. Lindsey, A. Nandi

Download paper