Proceedings :: International Society of Precision Agriculture

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Abney, M

Amin, S

Gardezi, M

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Authors

Balboa, G

Degioanni, A

Bongiovanni, R

Melchiori, R

Cerliani, C

Scaramuzza, F

Bongiovanni, M

Gonzalez, J

Balzarini, M

Videla, H

Amin, S

Esposito, G

Vellidis, G

Abney, M

Burlai, T

Fountain, J

Kemerait, R.C

Kukal, S

Lacerda, L

Maktabi, S

Peduzzi, A

Pilcon, C

Sysskind, M

Kumari, S

Rathore, J

Mitra, S

Gardezi, M

Walsh, O

Gardezi, M

Walsh, O

Joshi, D

Kumari, S

Clay, D.E

Rathore, J

Topics

Education and Outreach in Precision Agriculture

Decision Support Systems

Site-Specific Nutrient, Lime and Seed Management

Artificial Intelligence (AI) in Agriculture

Type

Oral

Poster

Year

2022

2024

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Filter results4 paper(s) found.
1. Overcoming Educational Barriers for Precision Agriculture Adoption: a University Diploma in Precision Agriculture in Argentina The lack of educational programs in Precision Agriculture (PA) has been reported as one of the barriers for adoption. Our goal was to improve professional competence in PA through education in crop variability, management, and effective practices of PA in real cases. In the last 20 years different efforts has been made in Argentina to increase adoption of PA. The Universidad Nacional de Rio Cuarto (UNRC) launched in 2021 the first University Diploma in PA, a 9-month program to train agronomist... G. Balboa, A. Degioanni, R. Bongiovanni, R. Melchiori, C. Cerliani, F. Scaramuzza, M. Bongiovanni, J. Gonzalez, M. Balzarini, H. Videla, S. Amin, G. Esposito Download paper
2. Decision Support Tools for Developing Aflatoxin Risk Maps in Peanut Fields Aspergillus flavus and Aspergillus parasiticus hereafter referred to jointly as A. flavus, are soil fungi that infect and contaminate preharvest and postharvest peanuts with the carcinogenic secondary metabolite aflatoxin. A. flavus can cause extensive economic losses to peanut growers and shellers by contaminating peanut kernels with aflatoxins. In the southeastern U.S., contamination from aflatoxin continues to be a major threat to the peanut industry and... G. Vellidis, M. Abney, T. Burlai, J. Fountain, R.C. Kemerait, S. Kukal, L. Lacerda, S. Maktabi, A. Peduzzi, C. Pilcon, M. Sysskind Download paper
3. Optimizing Soil Nutrient Management: Agricultural Policy/environmental Extender (APEX) Model Simulation for Field Scale Phosphorous Loss Reduction in Virginia Managing soil nutrients is crucial for enhancing crop productivity and meeting consumptions demands while minimizing environmental impacts. Sustainable agriculture relies on well-planned soil nutrient management strategies. Phosphorous (P) stands out among the 16 essential soil nutrients, particularly in Virginia, where natural P levels are typically low. Adequate amount of P is necessary for the early root formation and plant growth. However, excess amount of P in the soil leads to increase the... S. Kumari, J. Rathore, S. Mitra, M. Gardezi, O. Walsh Download paper
4. Predicting Soybean Yield Using Remote Sensing and a Machine Learning Model Soybean (Glycine max L.), a nutrient-rich legume crop, is an important resource for both livestock feed and human dietary needs. Accurate preharvest yield prediction of soybeans can help optimize harvesting strategies, enhance profitability, and improve sustainability. Soybean yield estimation is inherently complex because yield is influenced by many factors including growth patterns, varying crop physiological traits, soil properties, within-field variability, and weather conditions. The objective... M. Gardezi, O. Walsh, D. Joshi, S. Kumari, D.E. Clay, J. Rathore Download paper

Filter results4 paper(s) found.

1. Overcoming Educational Barriers for Precision Agriculture Adoption: a University Diploma in Precision Agriculture in Argentina

The lack of educational programs in Precision Agriculture (PA) has been reported as one of the barriers for adoption. Our goal was to improve professional competence in PA through education in crop variability, management, and effective practices of PA in real cases. In the last 20 years different efforts has been made in Argentina to increase adoption of PA. The Universidad Nacional de Rio Cuarto (UNRC) launched in 2021 the first University Diploma in PA, a 9-month program to train agronomist... G. Balboa, A. Degioanni, R. Bongiovanni, R. Melchiori, C. Cerliani, F. Scaramuzza, M. Bongiovanni, J. Gonzalez, M. Balzarini, H. Videla, S. Amin, G. Esposito

Download paper

2. Decision Support Tools for Developing Aflatoxin Risk Maps in Peanut Fields

Aspergillus flavus and Aspergillus parasiticus hereafter referred to jointly as A. flavus, are soil fungi that infect and contaminate preharvest and postharvest peanuts with the carcinogenic secondary metabolite aflatoxin. A. flavus can cause extensive economic losses to peanut growers and shellers by contaminating peanut kernels with aflatoxins. In the southeastern U.S., contamination from aflatoxin continues to be a major threat to the peanut industry and... G. Vellidis, M. Abney, T. Burlai, J. Fountain, R.C. Kemerait, S. Kukal, L. Lacerda, S. Maktabi, A. Peduzzi, C. Pilcon, M. Sysskind

Download paper

3. Optimizing Soil Nutrient Management: Agricultural Policy/environmental Extender (APEX) Model Simulation for Field Scale Phosphorous Loss Reduction in Virginia

Managing soil nutrients is crucial for enhancing crop productivity and meeting consumptions demands while minimizing environmental impacts. Sustainable agriculture relies on well-planned soil nutrient management strategies. Phosphorous (P) stands out among the 16 essential soil nutrients, particularly in Virginia, where natural P levels are typically low. Adequate amount of P is necessary for the early root formation and plant growth. However, excess amount of P in the soil leads to increase the... S. Kumari, J. Rathore, S. Mitra, M. Gardezi, O. Walsh

Download paper

4. Predicting Soybean Yield Using Remote Sensing and a Machine Learning Model

Soybean (Glycine max L.), a nutrient-rich legume crop, is an important resource for both livestock feed and human dietary needs. Accurate preharvest yield prediction of soybeans can help optimize harvesting strategies, enhance profitability, and improve sustainability. Soybean yield estimation is inherently complex because yield is influenced by many factors including growth patterns, varying crop physiological traits, soil properties, within-field variability, and weather conditions. The objective... M. Gardezi, O. Walsh, D. Joshi, S. Kumari, D.E. Clay, J. Rathore

Download paper