An Inference System for Classifying Oil Palm Fungal Diseases

Olajide Blessing Olajide,Olabiyi Olatunji Coker,Yakubani Yakubu,Stephen Munu,Odeniyi Olufemi Ayodeji

doi:10.18535/ijsrm/v9i11.ec01

Olajide Blessing Olajide, Olabiyi Olatunji Coker + Show 3 more

Open Access

https://doi.org/10.18535/ijsrm/v9i11.ec01

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Abstract

The oil palm plant is one of the major important cash crops of the Nigerian economy and a significant contributor to the world market for vegetable oils. Unfortunately, infection with fungi has caused a decline in the productivity of oil palms and subsequently the palm oil industry. Hence the need to detect oil palm plant disease earlier before it affects it informed this research to develop a fuzzy inference model to predict the influence of fungal disease on the oil plant plant. Following extensive review of related works, the factors associated with the severity of fungal diseases in the oil palm plant were identified following validation by Botanist. Fuzzy triangular membership functions were used to formulate the input factors identified alongside the target variables for identifying the severity of fungal diseases affecting the oil palm plant. The rule base was formulated using IF-THEN statements to combine the values of the input factors with the respective values of the target severity of oil palm plant disease. The classification model for oil palm plant disease severity was simulated using the Fuzzy Logic Toolbox available in the MATLAB R2015b Software. The results showed that the developed inference system for oil palm plant was capable of classifying and predicting the degree of the fungal disease infection into four groups; no severity, low severity, moderate severity and high severity.

Highlights

The Oil Palm Plant (Elaeis guineensis jacq.) is one of the major important cash crops of the Nigerian economy
This study examined the potential use of Terrestrial Laser Scanning (TLS) data to analyse the properties of oil palm tree at canopy and trunk section for non-infected and infected BSR at different severity level of infection
Simulation Results of Severity of Oil Plant Disease The results of the simulation of the model for the classification of the severity of oil palm plant disease is shown in Figure 2 such that the interval [-0.5, 0.5] with center 0 was used to model noseverity, [0.5, 1.5] with center 1 was used to model low severity, [1.5, 2.5] with center 2 was used to model moderate severity while [2.5, 3.5] with center 3 was used to model high severity

Summary

Introduction

The Oil Palm Plant (Elaeis guineensis jacq.) is one of the major important cash crops of the Nigerian economy. The importance of oil palm to the national economy of Nigeria cannot be over emphasized It ranges from production of food for human consumption, employment, income to farmers and nation and raw materials for industries. Infection with fungi has caused a decline in the productivity of oil palms and subsequently the palm oil industry, and created significant concern (Ekenta et al, 2017). For places where experts are not available, supplementary methods for carrying out field-based diagnoses are a critically required. Computational work in this area has been towards automating this process through building machine learning models that can take an image of a leaf

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