Classification of Plant Health (Capsicum Frutescens) Normalize Differences Vegetation Index using Image Processing

Abstract

The evolution of food manufacturer in global contribute in national income of the country. Agriculture has been a part of everyone’s life which result in providing food become the building block of every human being. Malaysia is only country experiencing deteriorating development contribute (25.9%) agriculture in Gross Deficient Domestics Product (GDP) while others are fishing (12%), rubber (3.0%) and forestry & logging (6.3%), livestock (15.3%). In line with the development of technology in present century, a lot of methods and technique introduced to upgrowth agriculture sector by focusing to the plant health. The aims of this study are to classify of agriculture plant health through NDVI using image processing. Image processing is a technique representing operations and observation on an image. The images of plant will be captured in this investigation to obtain a photo without (Infra-Red) IR imaging filter. Some of steps must be perform which also include of using multi-function software to gain NDVI values of plant. The main objective in this study is to classify plant health by performing the vegetation index of plant and identify the best machine learning to be applied.

Plant phenotyping relevance

植物画像からNDVIを算出し、植物の健康状態を分類する画像処理・機械学習手法が研究の中心であるため。

Code and data availability

The article describes NDVI-based chilli plant health classification using Raspberry Pi noIR images and Orange3/VGG-19 with kNN, SVM, and Naïve Bayes, but contains no data availability statement, no public dataset or image repository, and no author code/workflow deposit. The 75 plant images and MATLAB/Orange3 analysis (

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