Loading article...
Loading article...
Stream/Area:
-
Subject:
-
Paper ID:
10.55041/IJSREM24635
Volume:
7 |
7 |
Jul 2023
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Since it may help growers and farmers maximize crop yields and preserve resources, predicting plant development is a significant topic in agriculture and forestry. Machine learning offers a potential tool for forecasting plant development based on data, unlike older approaches that rely on the shape of the plant, we collect data on plant growth and shape and utilize a variety of machine learning techniques, including linear regression, decision trees, and neural networks. From the geometry of the plant, we extract a number of geometric properties, including curvature, length, and breadth, and we feed these data into the machine learning algorithms. Our tests demonstrate the great accuracy of the suggested method for forecasting plant development based on form.
KEYWORDS
Publication Date
7/16/2023
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
Documents
Article Views
0
Total views since publication
PDF Downloads
1
Total downloads since publication
Geographic Reach
5
Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.
Plagiarism Screening
This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.
Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
Related research in the same subject area
Loading similar articles...