The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
A Web Portal for Rice Crop Improvements
Abstract
High quality models of factors influencing rice crop yield are needed in countries where rice is a staple food. These models can help select optimal rice varieties for expected field conditions. Development of a system to help scientist track and make decisions using this data is challenging. It involves incorporation of complex data structures - genomic, phenotypic, and remote sensing - with computationally intensive statistical modeling. In this article, the authors present a web portal designed to help researchers to manage and analyze their datasets, apply machine learning to detect how factors taken together influence crop production, and summarize the results to help scientists make decisions based on the learned models. The authors developed the system to be easily accessed by the entire team including rice scientist, genetics, and farmers. As such, they developed a system on a server architecture comprised of a SQLite database, a web interface developed in Python, the Celery job scheduler, and statistical computing in R.
Related Content
|
Shweta Arun Avhad.
© 2023.
21 pages.
|
|
Majorie Moraa Nyasani, Victor Odhiambo Shikuku.
© 2023.
10 pages.
|
|
Prashant Kumar, Sunil Kumar Verma.
© 2023.
12 pages.
|
|
Sunil Kumar Verma, Prashant Kumar.
© 2023.
21 pages.
|
|
Kannadhasan S., Nagarajan R..
© 2023.
10 pages.
|
|
Kondapalli Vamsi Krishna, Sompalli Bhavana, Koushik Koujalagi, Alok Malaviya.
© 2023.
44 pages.
|
|
Bela Khiratkar, Shankar Mukundrao Khade, Abhishek Dutt Tripathi.
© 2023.
10 pages.
|
|
|