Machine Learning–Based Predictive Farmland Optimization and Crop Monitoring System
Joint Authors
Adebiyi, Marion Olubunmi
Ogundokun, Roseline Oluwaseun
Abokhai, Aneoghena Amarachi
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-05-11
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
E-agriculture is the integration of technology and digital mechanisms into agricultural processes for more efficient output.
This study provided a machine learning–aided mobile system for farmland optimization, using various inputs such as location, crop type, soil type, soil pH, and spacing.
Random forest algorithm and BigML were employed to analyze and classify datasets containing crop features that generated subclasses based on random crop feature parameters.
The subclasses were further grouped into three main classes to match the crops using data from the companion crops.
The study concluded that the approach aided decision making and also assisted in the design of a mobile application using Appery.io.
This Appery.io then took in some user input parameters, thereby offering various optimization sets.
It was also deduced that the system led to users’ optimization of information when implemented on their farmlands.
American Psychological Association (APA)
Adebiyi, Marion Olubunmi& Ogundokun, Roseline Oluwaseun& Abokhai, Aneoghena Amarachi. 2020. Machine Learning–Based Predictive Farmland Optimization and Crop Monitoring System. Scientifica،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1208273
Modern Language Association (MLA)
Adebiyi, Marion Olubunmi…[et al.]. Machine Learning–Based Predictive Farmland Optimization and Crop Monitoring System. Scientifica No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1208273
American Medical Association (AMA)
Adebiyi, Marion Olubunmi& Ogundokun, Roseline Oluwaseun& Abokhai, Aneoghena Amarachi. Machine Learning–Based Predictive Farmland Optimization and Crop Monitoring System. Scientifica. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1208273
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1208273