Automatic region-of-interest selection for corn seed grading

Prakasa, Esa; Rosiyadi, Didi; Ni'mah, Driszal Fryantoni Iftitahu; Khoiruddin, Arwan Ahmad; Lestriandoko, Nova Hadi; Suryana, Nana; Fajrina, N

doi:10.1109/ic3ina.2017.8251734

Cited by 4 publications

(4 citation statements)

References 11 publications

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“…Traditional techniques have been in use for a long time, but they are extremely tedious, expensive and out of control over time. In this context, high-tech switches are needed to use machine vision to classify the quality of agricultural food products and to assess timely and accurately [32][33][34][35][36][37][38][39][40][41][42].…”

Section: Introductionmentioning

confidence: 99%

Towards a Real-Time Oil Palm Fruit Maturity System Using Supervised Classifiers Based on Feature Analysis

et al. 2022

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Remote sensing sensors-based image processing techniques have been widely applied in non-destructive quality inspection systems of agricultural crops. Image processing and analysis were performed with computer vision and external grading systems by general and standard steps, such as image acquisition, pre-processing and segmentation, extraction and classification of image characteristics. This paper describes the design and implementation of a real-time fresh fruit bunch (FFB) maturity classification system for palm oil based on unrestricted remote sensing (CCD camera sensor) and image processing techniques using five multivariate techniques (statistics, histograms, Gabor wavelets, GLCM and BGLAM) to extract fruit image characteristics and incorporate information on palm oil species classification FFB and maturity testing. To optimize the proposed solution in terms of performance reporting and processing time, supervised classifiers, such as support vector machine (SVM), K-nearest neighbor (KNN) and artificial neural network (ANN), were performed and evaluated via ROC and AUC measurements. The experimental results showed that the FFB classification system of non-destructive palm oil maturation in real time provided a significant result. Although the SVM classifier is generally a robust classifier, ANN has better performance due to the natural noise of the data. The highest precision was obtained on the basis of the ANN and BGLAM algorithms applied to the texture of the fruit. In particular, the robust image processing algorithm based on BGLAM feature extraction technology and the ANN classifier largely provided a high AUC test accuracy of over 93% and an image-processing time of 0,44 (s) for the detection of FFB palm oil species.

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Section: Introductionmentioning

confidence: 99%

Towards a Real-Time Oil Palm Fruit Maturity System Using Supervised Classifiers Based on Feature Analysis

et al. 2022

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“…The capturing process of corn image data is conducted at the Laboratory of Computer Vision Research Group, Research Center for Informatics -Indonesian Institute of Sciences (LIPI) Bandung, with a modified apparatus from the previous research [2]. The con seed samples are provided by the Assessment Institutes for Agricultural Technology of Gorontalo, Ministry of Agriculture Republic of Indonesia.…”

Section: A Capturing the Corn Image Datamentioning

confidence: 99%

“…The purpose of ROI detection is to obtain a single corn seed in each image. Prakasa et al [2] had successfully developed an algorithm to detect the ROI automatically using the k-means algorithm. The ROI will produce images containing only one corn seed by determining the location and boundary box of each corn seed in the input image.…”

Section: B Detection Of the Region Of Interest (Roi) And Preprocessingmentioning

confidence: 99%

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Corn Seeds Identification Based on Shape and Colour Features

Yafie¹,

Rachmawati²,

Prakasa³

et al. 2020

khif

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Corn is one of the agricultural products that are essential as daily food sources or energy sources. Corn selection or sorting is important to produce high-quality seeds before its distribution to areas with varying conditions and agricultural characteristics. Hence, it is necessary to build corn seeds identification. In this paper, we propose a corn seed identification technique that incorporates the advantage of combining shape and color features. The identification process consists of three main stages, namely, ROI selection, feature extraction, and classification using the Artificial Neural Network (ANN) algorithm. The shape feature originates from the eccentricity value or comparison value between a distance of minor ellipse foci and major ellipse foci of an object. Meanwhile, the color features are extracted based on the HSV (Hue-Saturation-Value) channel. The experimental result shows that the proposed system achieves excellent performance for the identification of poor and good corn quality for BIMA-20 and NASA-29 species. The classification result for BIMA-20 Good vs. BIMA-20 Bad gives an accuracy of 89%, while the classification accuracy of BIMA-20 Good vs. NASA-29 Good is 97%.

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A Comparative Study on Predicting Food Quality using Machine Learning Techniques

Antony

Kumar

2021

2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS)

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Automatic region-of-interest selection for corn seed grading

Cited by 4 publications

References 11 publications

Towards a Real-Time Oil Palm Fruit Maturity System Using Supervised Classifiers Based on Feature Analysis

Towards a Real-Time Oil Palm Fruit Maturity System Using Supervised Classifiers Based on Feature Analysis

Corn Seeds Identification Based on Shape and Colour Features

A Comparative Study on Predicting Food Quality using Machine Learning Techniques

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