A Machine Learning Framework to Predict Determinant Factors of Seeds

Tekalign Tujo Gurmessa


In this paper, we audit the machine learning apparatuses for foreseeing determinant components of seeds. We depict this issue regarding Big Data, ANN, Hadoop and R. We consider Machine-learning techniques especially suited to forecasts dependent on existing information, yet exact expectations about the far off future are frequently on a very basic level unthinkable. Farming is an industry where recorded and current information flourish. This survey researches the various information sources accessible in the horticultural field and dissects them for utilization in Seed determinant factor Predictions. We recognized certain relevant information and researched techniques for utilizing this information to improve forecast inside the farming action.

Full Text: PDF
Download the IISTE publication guideline!

To list your conference here. Please contact the administrator of this platform.

Paper submission email: JIEA@iiste.org
ISSN (Paper)2224-5782 ISSN (Online)2225-0506
Please add our address "contact@iiste.org" into your email contact list.
This journal follows ISO 9001 management standard and licensed under a Creative Commons Attribution 3.0 License.
Copyright © www.iiste.org