Adaptive Neuro-Fuzzy Inference System Based Grading of Basmati Rice Grains Using Image Processing Technique

Abstract

Grading of rice intents to discriminate broken and whole grain from a sample. Standard techniques for image-based rice grading using advanced statistical methods seldom take into account the domain knowledge associated with the data. In the context of a high product value basmati rice with an image based grading process, one ought to consider the physical properties of grain and the associated knowledge. In this present work, a model of quality grade testing and identification is proposed using a novel digital image processing and knowledge-based adaptive neuro-fuzzy inference system (ANFIS). The rationale behind adopting a grading system based on fuzzy rules relies on capabilities of ANFIS to simulate the behaviour of an expert in the characterization of rice grain using the physical properties of rice grains. The rice kernels are characterized with the help of morphological descriptors and geometric features which are derived from sample images of milled basmati rice. The predictive capability of the proposed technique has been tested on a sufficient number of training and test images of basmati rice grain. The proposed method outperforms with a promising result in an evaluation of rice quality with >98.5% classification accuracy for broken and whole grain as compared to standard machine learning technique viz. support vector machine (SVM) and K-nearest neighbour (KNN). The milling efficiency is also assessed using the ratio between head rice and broken rice percentage and it is 77.27% for the test sample. The overall results of the adopted methodology are promising in terms of classification accuracy and efficiency.

On the Frontiers of Rice Grain Analysis, Classification and Quality Grading: A Review

“…The feature vector consisted of 10 morphological and 13 color attributes. In the same year, Mandal [86] also proposed a rice classification system with 7 morphological features using ANFIS [87] and achieved an accuracy of 98.6%. However, this approach required the rice grains to be non-overlapping and well separated.…”

“…In 2018, Mandal et al proposed a rice grading system based on geometric features which were fed to an adaptive neuro-fuzzy inference system (ANFIS) [86]. The proposed solution outperformed KNN and SVM based systems and exhibited an accuracy of 98.5%.…”

Quality Analysis of Rice Grains Using Ann and SVM

“…In India, Agmark is a standard certification employed for agricultural products. Dipnakar Mandal (2018), estimated the quality of rice as per the present Agmark standards using image processing principles, through MATLAB. The limitations in manual rice grain quality assessment system is its relative grading, time consuming, cost and varying results.…”

Sistem Pendukung Keputusan Rekomendasi Penilaian Kinerja Karyawan Untuk Menentukan Status Pemberian Reward Level Operator dan Foreman Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS)

“…Peneliti lain, Mandal [2] mengusulkan ANFIS untuk mendeteksi dan mengklasifikasikan citra bulir padi basmati yang hasilnya ANFIS memiliki akurasi yang lebih menjanjikan dalam mengevaluasi kualitas beras, dengan akurasi klasifikasi >98,5% untuk benih padi pecah dan utuh, dibandingkan dengan teknik pembelajaran mesin standar yaitu, mesin vektor pendukung (SVM) dan tetangga terdekat K (KNN). Efisiensi penggilingan juga dinilai dengan menggunakan rasio antara beras kepala dan persentase beras pecah dan 77,27% untuk sampel uji.…”