Proceedings :: International Society of Precision Agriculture

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Scarpin, G

Kaushal, S

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Authors

Kaushal, S

Sharda, A

Bastos, L

Porter, W

Scarpin, G

Jakhar, A

Bhattarai, A

Bastos, L

Scarpin, G

Topics

Big Data, Data Mining and Deep Learning

In-Season Nitrogen Management

Type

Oral

Poster

Year

2024

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Filter results3 paper(s) found.
1. Enhancing Agricultural Feedback Analysis Through VUI and Deep Learning Integration A substantial amount of information relies on consumers, influencing aspects from product adoption to overall satisfaction. Similarly, the agricultural sector is entirely dependent on farmers, who dictate the success of products and highlight associated challenges. Our study aligns with this perspective, recognizing the significance of understanding farmers' needs to assist tractor manufacturing industries. As these industries aim for widespread adoption of their products among farmers, it... S. Kaushal, A. Sharda
2. Satellite-based On-farm Variable Rate Nitrogen Management on and Main Spatial Drivers of Cotton Yield, Nitrogen Use Efficiency, and Profitability In the United States of America, Georgia is the second largest cotton producing state, responsible for 2.6 million bales produced in 2022. In Georgia, cotton is the most economically important row crop, with ~514,000 ha harvested and $USD 1.5 billion in economic impact in the state economy in 2022. Nitrogen (N) fertilizer is one of the main inputs required to optimize cotton lint yield and quality, while also being a large input cost representing ~25% of variable costs. As a non N-fixing crop,... L. Bastos, W. Porter, G. Scarpin Download paper
3. Proximal, Drone, and Satellite Sensors for In-season Variable Nitrogen Rate Application in Corn: a Comparative Study of Fixed-rate and Sensor-based Approaches Effective nitrogen (N) management is essential for optimizing corn yield and enhancing agricultural sustainability. Traditional N application methods, typically uniform split pre-plant and in-season applications, often neglect the spatial and temporal variability of N requirements across different fields and years, potentially leading to N overuse. With the rise of precision agriculture technologies, it is crucial to reassess these conventional practices. This study had two main objectives: first,... A. Jakhar, A. Bhattarai, L. Bastos, G. Scarpin Download paper

Filter results3 paper(s) found.

1. Enhancing Agricultural Feedback Analysis Through VUI and Deep Learning Integration

A substantial amount of information relies on consumers, influencing aspects from product adoption to overall satisfaction. Similarly, the agricultural sector is entirely dependent on farmers, who dictate the success of products and highlight associated challenges. Our study aligns with this perspective, recognizing the significance of understanding farmers' needs to assist tractor manufacturing industries. As these industries aim for widespread adoption of their products among farmers, it... S. Kaushal, A. Sharda

2. Satellite-based On-farm Variable Rate Nitrogen Management on and Main Spatial Drivers of Cotton Yield, Nitrogen Use Efficiency, and Profitability

In the United States of America, Georgia is the second largest cotton producing state, responsible for 2.6 million bales produced in 2022. In Georgia, cotton is the most economically important row crop, with ~514,000 ha harvested and $USD 1.5 billion in economic impact in the state economy in 2022. Nitrogen (N) fertilizer is one of the main inputs required to optimize cotton lint yield and quality, while also being a large input cost representing ~25% of variable costs. As a non N-fixing crop,... L. Bastos, W. Porter, G. Scarpin

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3. Proximal, Drone, and Satellite Sensors for In-season Variable Nitrogen Rate Application in Corn: a Comparative Study of Fixed-rate and Sensor-based Approaches

Effective nitrogen (N) management is essential for optimizing corn yield and enhancing agricultural sustainability. Traditional N application methods, typically uniform split pre-plant and in-season applications, often neglect the spatial and temporal variability of N requirements across different fields and years, potentially leading to N overuse. With the rise of precision agriculture technologies, it is crucial to reassess these conventional practices. This study had two main objectives: first,... A. Jakhar, A. Bhattarai, L. Bastos, G. Scarpin

Download paper