Pawan Kumar Gupta
Biographic Data
| ID | 10129008 |
|---|---|
| NAME | Pawan Kumar Gupta |
| GIVEN NAMES | Pawan Kumar |
| FAMILY NAME | Gupta |
| SIGNATURE | GUPTA P K |
| AFFILIATIONS | Amicable Knowledge Solution University |
| ORCID | 0009-0009-1586-1675 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 0 |
A Systematic Analysis of Current Developments and Potential Challenges in Applied Deep Learning-Based Seed Yield Prediction
Seed yield prediction is crucial in modern agriculture, aiding farmers and stakeholders in making informed decisions regarding crop management, resource allocation, and harvest planning. Traditionally, seed yield prediction relied on empirical models and historical data, which often lacked accuracy and robustness, particularly in dynamic agricultural environments. However, with the advent of deep learning (DL) techniques, there has been a paradig…
Analyzing Machine Learning Methods to Enhance Grain Quality Assessment and Evolution
Assessing grain quality is a crucial component of agricultural production, with significant implications for global food security and economic prosperity. With the increasing demand for top-tier grains, there arises a pressing need for sophisticated methodologies to aid in the ongoing assessment and monitoring of grain quality throughout its development. This paper offers a comprehensive examination of machine learning (ML) techniques utilized in…
No prominent works on this page.
A Systematic Analysis of Current Developments and Potential Challenges in Applied Deep Learning-Based Seed Yield Prediction
Seed yield prediction is crucial in modern agriculture, aiding farmers and stakeholders in making informed decisions regarding crop management, resource allocation, and harvest planning. Traditionally, seed yield prediction relied on empirical models and historical data, which often lacked accuracy and robustness, particularly in dynamic agricultural environments. However, with the advent of deep learning (DL) techniques, there has been a paradig…
Analyzing Machine Learning Methods to Enhance Grain Quality Assessment and Evolution
Assessing grain quality is a crucial component of agricultural production, with significant implications for global food security and economic prosperity. With the increasing demand for top-tier grains, there arises a pressing need for sophisticated methodologies to aid in the ongoing assessment and monitoring of grain quality throughout its development. This paper offers a comprehensive examination of machine learning (ML) techniques utilized in…
Artificial Intelligence (2 works) · Computer Science (2 works) · Engineering (2 works) · Machine learning (2 works) · Smart Agriculture and AI (2 works) · Electrical engineering (1 work) · Epistemology (1 work) · Evaluation methods (1 work) · Leaf Properties and Growth Measurement (1 work) · Materials Science (1 work)