Classification for the Ripeness of Papayas Using Artificial Neural Network (ANN) and Threshold Rule

Saad, Hasnida; Hussain, Aini

doi:10.1109/scored.2006.4339325

Cited by 11 publications

(9 citation statements)

References 2 publications

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“…Image classification has been done by taking out various datasets of rice seed varieties [11] or any other seeds of crops like almonds [7], maize and wheat. H. Saad and A. Hussain [5] proposed model for checking the ripeness of papayas which are mature, over-mature or immature using the artificial neural network as well as threshold rule but accuracy shows more while using neural networks rather than threshold rule. Neural network toolbox of some MATLAB version to be used in order to classify it.…”

Section: Related Workmentioning

confidence: 99%

Image Classification of Agricultural Data Using Supervised Learning Techniques:-a Survey

Sukhwani¹

2018

ijarcs

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Nowadays with the increasing progress in the field of machine learning and image processing, image classification plays a vital role and provides various advantages like to classify the different varieties of wheat/rice seeds or any other agricultural seeds/grains. The literature basically includes the algorithm for feature extraction and dimensionality reduction algorithms in order to reduce error. Accuracy totally depends on a number of the ratio of samples which are divided for training and testing phase. This survey includes an artificial neural network with back-propagation having multiple hidden layer and support vector machine, these supervised learning models are taken into consideration in order to classify the types, by which through the accuracy could be known. Various issues and challenges are highlighted in this survey. It is evident from the literature that back-propagation with multiple hidden layers can reduce more error in comparison to single layer hidden layer.

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Section: Related Workmentioning

confidence: 99%

Image Classification of Agricultural Data Using Supervised Learning Techniques:-a Survey

Sukhwani¹

2018

ijarcs

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“…The recognition rate obtained from their experiment using these three network structure are 91.7, 91.45, and 94.75% respectively. Saad and Hussain () also proposed a grading system for papayas using neural network and threshold concept. In this work, he applied preprocessing methods to the sample, methods such as edge detection, grayscale to RGB and masking were also applied to the samples.…”

Section: Related Workmentioning

confidence: 99%

Automatic system for grading banana using GLCM texture feature extraction and neural network arbitrations

Olaniyi

Adekunle

Odekuoye

et al. 2017

J Food Process Engineering

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Nowadays, an identification system is needed in the food processing industries to boost the efficiency of production so as to meet up with demand in the society. Manual approach is often used in product grading and quality control, and this unfortunately could lead to uneven products, higher time expense, and fatigue by the human operators. Therefore, we propose in this article, an automatic system for classification of banana whether it is healthy for production or not. Such a system is faster, accurate and also relieves the stress that an operator may have. Our system uses GLCM texture feature analysis to extract the features required for training and testing three classification models; namely, radial basis function (RBF), support vector machine (SVM), and backpropagation neural network (ANN). A classification performance comparison is drawn between the different classification models, and the obtained experimental results indicate that such intelligent grading systems may be efficiently used in real life applications for similar tasks in food processing industries. Practical applications The Automatic system is highly needed in food processing industries to meet up with the production of food products required in the societies. Healthy food products are needed in the society and this can be achieved by implementing a system that will enhance in the sorting or grading of the raw materials (such as banana) used in food processing industries. This system is accurate, economical, and faster in achieving the best product. Such a system will make the product be readily available in the market i.e. meeting the need of the people.

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“…Dalam [4] menerapkan teknologi pengolahan citra digital dan jaringan saraf tiruan untuk mengidentifikasi mutu fisik biji pala (Myristica fragrans houtt), pengujian fisik biji pala dilakukan secara non-destruktif meliputi warna, bentuk dan tekstur, kemudian keluaran adalah kelas mutu biji pala yang terdiri dari mutu ABC, mutu rimpel, dan mutu BWP. Penelitan [5] menggunakan jaringan saraf tiruan dan threshold rule untuk klasifikasi kematangan buah pepaya ke dalam tiga kategori yaitu pepaya belum matang, matang sedang, dan matang berdasarkan pada ratarata nilai RGB (red, green, blue). Penelitian [6] melakukan perancangan dan konstruksi mesin sortasi dengan sensor kamera CCD sebagai sensor citra dan unit pengolahannya untuk melakukan evaluasi pemutuan buah jeruk.…”

Section: Pendahuluanunclassified

Klasifikasi Mutu Pepaya Berdasarkan Ciri Tekstur GLCM Menggunakan Jaringan Saraf Tiruan

Wibowo

Harjoko

2017

khif

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Proses sortasi buah pepaya berdasarkan mutu merupakan salah satu proses yang sangat menentukan mutu buah pepaya yang akan dilepas ke konsumen. Proses identifikasi mutu dengan cara konvensional menggunakan visual mata manusia memiliki kelemahan di antaranya yaitu membutuhkan tenaga lebih banyak untuk memilah, tingkat persepsi manusia dalam hal pemutuan buah yang berbeda, tingkat konsistensi manusia dalam menilai mutu buah tidak menjamin karena manusia dapat mengalami kelelahan.Penelitian ini bertujuan merancang dan menyusun program pengolahan citra digital dan jaringan saraf tiruan untuk klasifikasi pemutuan buah pepaya (Carica Papaya L) Calina IPB-9 ke dalam tiga kelas mutu yaitu kelas Super, A, dan B. Fitur tekstur yang diekstrak meliputi nilai energy, entropy, contras, homogeneity, invers difference moment, variance, dan dissimilarity yang didapatkan berdasarkan GLCM (gray level cooccurrence matrices). Fitur-fitur tersebut dijadikan sebagai input pada algoritma pelatihan jaringan saraf tiruan backpropagation. Hasil pengujian pada proses pengenalan menunjukan fitur energy, dan entropy dapat membedakan kelas mutu pepaya Calina IPB-9 dengan tingkat akurasi terbaik yaitu sebesar 86.11%.

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Classification for the Ripeness of Papayas Using Artificial Neural Network (ANN) and Threshold Rule

Cited by 11 publications

References 2 publications

Image Classification of Agricultural Data Using Supervised Learning Techniques:-a Survey

Image Classification of Agricultural Data Using Supervised Learning Techniques:-a Survey

Automatic system for grading banana using GLCM texture feature extraction and neural network arbitrations

Klasifikasi Mutu Pepaya Berdasarkan Ciri Tekstur GLCM Menggunakan Jaringan Saraf Tiruan

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