Crop Recommendation Using Machine Learning Algorithms
DOI:
https://doi.org/10.54228/mjaret05230001Keywords:
Crop Recommendation, Machine Learning, Precision Agriculture, Random ForestAbstract
Agriculture plays an important role in the Indian economy and employment. The
most common problem faced by farmers in India is that they do not choose products
according to the needs of the soil and therefore they face serious problems in their
productivity. This problem can be solved with precision agriculture. The decision-making
process includes three parameters: land area, soil type, and crop information based on these
parameters to show farmers the appropriate crop. Precision farming helps reduce
unnecessary crops that increase profitability in addition to the following benefits such as
better input and output efficiency and higher farming. This approach offers solutions such as
suggestion from mixed model with majority voting, using random trees, using K- Nearest
Neighbors as a learner, suggesting Crops suitable for soil for lack of accuracy and yield.
Downloads
Downloads
Published
Issue
Section
License
License Terms for MJARET
Creative Commons Attributio 4.0 International (CC BY) License:
This license allows for the following:
-
Sharing — Copy and redistribute the material in any medium or format.
-
Adaptation — Remix, transform, and build upon the material.
The license is subject to the following terms:
-
Attribution:
- You must give appropriate credit, provide a link to the license, and indicate if changes were made.
- Attribution should include the citation of the article, the author's name, and a link to the original work published in MJARET.
- This must be done in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
-
NonCommercial:
- The material cannot be used for commercial purposes.
- Any use of the work intended to provide a commercial advantage or monetary compensation is considered outside the scope of this license.
-
No Additional Restrictions:
- You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
General Provisions:
- Understandability: This license can be revoked if you do not comply with its terms and conditions.
- Public Domain: Where the work or any of its elements is in the public domain under applicable law, that status is in no way affected by the license.
- Other Rights:
- The license does not cover rights such as publicity, privacy, or moral rights that may affect your ability to use the material as contemplated by the license.
- Such rights might need to be considered and respected separately.
Disclaimer:
- MJARET does not provide any warranties with the work. The work is provided "as is" without any representations or warranties, express or implied. MJARET will not be liable for any damages resulting from the use of the work.
How to Cite:
- Proper attribution for use of the licensed work should follow the standard citation format provided by MJARET, which should include the author(s), the title of the work, MJARET, and the DOI link.
