Outdoor Colour Recognition System for Oil Palm Fresh Fruit Bunches (Ffb)

Abstract

Abstract-The variations of day light intensity must be taken into account to recognize the color of the agriculture product when using camera vision system. In this study, the development of outdoor image analysis for oil palm fruit fresh bunches (FFB) was developed to analyses image of oil palm FFB. The software analysis will generates the mathematical model and correlation factor between the light intensity in relation to value of FFB from Red, Green and Blue component (RGB) of image taken. The visual basic programming language was used. An Excel worksheet programming software was developed to draw a chart and modify the charts location and dimensions. The API (Application Programming Interface) called GDI32 (Graphic Device Interface: 32-bit version) was applied to read and manipulate the pixel value of image. The software automation technique was used in the development of outdoor image analysis for agriculture product. The automation concept is to describe the automatic generation of the software to manipulate the data for producing the graph, mathematical analysis and equation, automatically from a bundle of data collected by only a click of mouse button. The result generated was used to develop mathematical model for color-based recognition for outdoor image of agriculture product. Linear regression function was used to find the equation of mathematical model for outdoors image analysis. To find the best equation of mathematical model, various trendline of data manipulation can be chosen manually from option provided in Microsoft Excel. The images of FFB were taken by using sony digital camera Handycam at various times of day from morning to afternoon in the oil palm plantation.

Integration Precision and Unmanned Aerial Vehicles Technology in Oil Palm Management System Development

“…Thermal imagery and image processing algorithm -The software automation methodology for imagery processing is followed by such process: image was captured first, then converted into pixel values, stored in the automatic graph formatting, applied regression calculation algorithm and lastly provide correlation [24]. However such imagery processing still requires the user to understand several items, such as what does RGB stands for in their regression model, or sometimes even black and white value [25]. This research deploys a simpler methodology by utilizing open source data imagery processing during this development stage, which is by the OpenCV, a computer vision cloud platform developed by Microsoft.…”

Kajian Karakteristik Spektrum Tandan Buah Segar (Tbs) Kelapa Sawit Berdasarkan Tingkat Kematangan Menggunakan Spektrofotometer Uv – Vis

“…Hal ini karena penggunaan sensor tidak dipengaruhi oleh faktor eksternal, dan sensor lebih baik dari manusia, namun memiliki keterbatasan. Beberapa sistem sensor untuk menentukan kematangan TBS kelapa sawit yang telah diteliti antara lain sensor optik camera base system (Abdullah et al 2001(Abdullah et al , 2004Makky 2005;Alfatni et al 2008;Ismail et al 2010;Razali et al 2011;Hazir et al 2011;May and Amaran 2011), kamera hiperspektruml (Junkwon et al 2009), resonansi magnetik pencitraan dan resonansi magnetik nuklir (Shaarani et al 2010), sensor kadar air (Yeow et al 2010), sensor fluoresensi multiparametrik (Hazir et al 2011, sensor optik aktif (Saeed et al 2012), dan sensor ultrasonik (Suwannarat et al 2012). Teknikteknik tersebut telah menggambarkan potensi menggunakan sensor optik dalam mendeteksi kematangan TBS.…”

Towards Sustainable Green Production: Exploring Automated Grading for Oil Palm Fresh Fruit Bunches (FFB) Using Machine Vision and Spectral Analysis

“…Several studies have been carried out to assess oil palm FFB properties using optical devices, such as camera [6], [7], [8], Color meter [9], as well as visible and non-visible spectrum [10], [11]. However, it was found that several factors reduces the practicality and efficiency of the equipment used [12] [13].…”