Machine Learning–Based Predictive Farmland Optimization and Crop Monitoring System

Adebiyi, Marion and Ogundokun, Roseline Oluwaseun and Abokhai, Aneoghena Amarachi (2020) Machine Learning–Based Predictive Farmland Optimization and Crop Monitoring System. Scientifica, 2020. pp. 1-12. ISSN 2090-908X

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Abstract

E-agriculture is the integration of technology and digital mechanisms into agricultural processes for more efficient output. &is 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. &e subclasses were further grouped into three main classes to match the crops using data from the companion crops. &e study concluded that the approach aided decision making and also assisted in the design of a mobile application using Appery.io. &is 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.

Item Type:	Article
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Depositing User:	Mr Uchechukwu F. Ekpendu
Date Deposited:	05 Jul 2021 09:05
Last Modified:	05 Jul 2021 09:05
URI:	https://eprints.lmu.edu.ng/id/eprint/3188

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