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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

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Published on: December 9, 2012

Machine Learning-Based Predictive Farmland Optimization and Crop Monitoring System.

Marion Olubunmi Adebiyi¹, Roseline Oluwaseun Ogundokun¹, Aneoghena Amarachi Abokhai¹

¹Department of Computer Science, Landmark University, Omu-Aran, Kwara State, Nigeria.

|May 27, 2020

Summary

This study introduces an e-agriculture mobile system using machine learning for farmland optimization. The system analyzes crop features to provide users with tailored optimization sets, improving farm management decisions.

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Area of Science:

Agricultural Science
Computer Science
Data Science

Background:

E-agriculture integrates technology into farming for enhanced efficiency.
Optimizing farmland requires analyzing complex datasets like soil type, pH, and crop features.

Purpose of the Study:

To develop a machine learning-aided mobile system for farmland optimization.
To assist farmers in making informed decisions for improved crop yields.

Main Methods:

Utilized the Random Forest algorithm and BigML for data analysis and classification.
Developed a mobile application using Appery.io to process user inputs and provide optimization sets.

Main Results:

Generated subclasses based on random crop features, grouped into three main classes.
The system successfully provided various optimization sets based on user input parameters.
Demonstrated improved information optimization for users implementing the system on their farmlands.

Conclusions:

The developed system effectively aids decision-making in farmland management.
The mobile application offers practical optimization solutions for farmers.
The approach enhances the utilization of agricultural data for better outcomes.