A Comparative Analysis of Machine Learning Algorithms Modeled from Machine Vision-Based Lettuce Growth Stage Classification in Smart Aquaponics

Jonnel D Alejandrino,Ronnie S Concepcion Ii,Dailyne D Macasaet,Rogelio Ruzcko Tobias,Sandy C Lauguico,Elmer P Dadios

doi:10.18178/ijesd.2020.11.9.1288

Jonnel D Alejandrino, Ronnie S Concepcion Ii + Show 4 more

Open Access

https://doi.org/10.18178/ijesd.2020.11.9.1288

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Abstract

The arising problem on food scarcity drives the innovation of urban farming. One of the methods in urban farming is the smart aquaponics. However, for a smart aquaponics to yield crops successfully, it needs intensive monitoring, control, and automation. An efficient way of implementing this is the utilization of vision systems and machine learning algorithms to optimize the capabilities of the farming technique. To realize this, a comparative analysis of three machine learning estimators: Logistic Regression (LR), K-Nearest Neighbor (KNN), and Linear Support Vector Machine (L-SVM) was conducted. This was done by modeling each algorithm from the machine vision-feature extracted images of lettuce which were raised in a smart aquaponics setup. Each of the model was optimized to increase cross and hold-out validations. The results showed that KNN having the tuned hyperparameters of n_neighbors=24, weights='distance', algorithm='auto', leaf_size = 10 was the most effective model for the given dataset, yielding a cross-validation mean accuracy of 87.06% and a classification accuracy of 91.67%.

Highlights

The land resources for agriculture has been decreasing as more rural areas are urbanized to accommodate industrial needs
The general objective of the study is to determine which among the machine learning classification algorithms: Logistic Regression (LR), K-Nearest Neighbor (KNN), and Linear Support Vector Machine (L-SVM) is the most accurate in classifying the three growth stages of lettuce farmed in a smart aquaponics setup
A smart aquaponics system is established in Rizal, Philippines

Summary

Introduction

The land resources for agriculture has been decreasing as more rural areas are urbanized to accommodate industrial needs. UA is defined as the production, process, and distribution of food produced in cities for local needs [1]. One developing form of UA is the aquaponics system. Aquaponics is a combination of hydroponics (soillessbased planting) and aquaculture (fish farming). It is a closed-loop system recycling fresh water between fish and plant, making nutrients shared between them as well [2]. Data acquisition and control systems are studied, developed, and implemented to make the systems smart aquaponics. High quality of fruits and vegetables grading are reliant on images processed from visions systems [6] to properly extract the features for analysis

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