Mango Fruit Sortation System using Neural Network and Computer Vision

Yossy, Emny Harna; Pranata, Jhonny; Wijaya, Tommy; Hermawan, Heri; Budiharto, Widodo

doi:10.1016/j.procs.2017.10.013

Cited by 47 publications

(23 citation statements)

References 2 publications

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“…Sidehabi et al [33], developed a system for classifying the maturity level of passion fruit with 90% accuracy. Hamza and Chtourou (2018), implemented a model for the classification of apple maturity with 92.5-96.6% accuracy, and the system proposed by Yossy et al [34] allowed to detect mango ripeness with 94% accuracy.…”

Section: Discussionmentioning

confidence: 99%

Hass avocado ripeness classification by mobile devices using digital image processing and ANN methods

Jaramillo-Acevedo

Choque-Valderrama

Álvarez

et al. 2020

International Journal of Food Engineering

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Proper farming, transportation, and storage processes of Hass avocado are important owing to its recent increase in production, export, and economic activity in Colombia. Since Hass avocado pricing and utility depend on its consumption ripeness, related to changes in skin color, sensory properties, texture, and nutritional value, developing an Android mobile application, namely iHass for smartphones and tablets, which estimates the number of days in which the Hass avocado reaches its optimal ripening level during post-harvest storage, contributes toward improving the fruit quality and decreasing the export costs and losses. This study aims to monitor the ripening processes of Hass avocados in complex backgrounds and indoor environments using various digital image processing techniques. The proposed study uses the red, green, and blue color model based on the physical and chemical changes that are observed during the ripening process. Herein, the color, shape, and texture characteristics of the fruits are obtained, and the fruits are classified using an artificial neural network, which features three layers, four input parameters, six hidden neurons, and four output parameters. Furthermore, ripeness was monitored in two crops, which provided 65 samples each. The results provided a ripeness estimate accuracy of 88% and a regression value of 0.819 during the post-harvest period.

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Section: Discussionmentioning

confidence: 99%

Hass avocado ripeness classification by mobile devices using digital image processing and ANN methods

Jaramillo-Acevedo

Choque-Valderrama

Álvarez

et al. 2020

International Journal of Food Engineering

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show abstract

“…When it comes to classifications, the most common techniques in machine learning are Support Vector Machines (SVM), Artificial Neural Networks (ANN) and K-Nearest Neighbors (K-NN). The study by Yossy, Pranata, Wijaya, Hermawan, & Budiharto (2017) focused on recognition of mango using ANN. The mango sortation system can sort mango accurately with 94% success percentage.…”

Section: Article Historymentioning

confidence: 99%

Size Classification of Tomato Fruit Using Thresholding, Machine Learning, and Deep Learning Techniques

2019

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The size of tomato fruits is closely related to the market segment and price. Manual sorting in tomato is very dependent on human interpretation and thus, very prone to error. The study presents thresholding, machine learning and deep learning techniques in classifying the tomato as small, medium and large based from a single tomato fruit image implemented using Open CV libraries and Python programming. Tomato images with different sizes are gathered where features like area, perimeter and enclosed circle radius are extracted. The experiment shows that using thresholding, a classification accuracy of 85.83%, 65.83% and 80% was achieved for area, perimeter and enclosed circle radius, respectively. For machine learning, the training accuracy rates were recorded as 94.00%-95.00% for SVM, 97.50-92.50% for KNN and 90.33-92.50% for ANN. Comparison of models revealed that SVM is the most model without over fitting. The deep learning approach, regardless of the algorithm, produced low performances with 82.31%-78.21%-55.97% trainingvalidation-testing accuracy for VGG16, 48.17%-41.44%-37.64% for InceptionV3 and 56.05%-44.96%-22.78% for ResNet50 models. Comparative analysis showed that machine learning technique bested the performance of the thresholding and deep learning techniques in classifying the tomato fruit size in terms of accuracy performance.

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“…With D and L in cm and VP in cm 3 . All of the shape features apart from area are invariant to size, since they are measured from profile images normalised to unit area.…”

Section: A Inspection Processmentioning

confidence: 99%

“…Using the obtained image, the features of the mango are extracted and used to determine the mango layer. The characteristics of the extracted mango are perimeter, area, roundness and defect rate; The mango classification system uses machine vision and Neural network [3] as a system that can classify ripe or unripe mangoes. The method used to carry out this study was split into several steps: object identification, algorithm development, implementation and evaluation.…”

Section: Introductionmentioning

confidence: 99%

Mango Sorting Mechanical System Uses Machine Vision and Artificial Intelligence

Thong¹,

Thinh²,

Cong³

2019

IJET

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Sorting and Classification of mango, there are different colors, weights, sizes, shapes and densities. Currently, classification based on the above features is being carried out mainly by manuals due to farmers' awareness of low accuracy, high costs, health effects and high costs, costly economically inferior. This study was conducted on three main commercial mango species of Vietnam to find out the method of classification of mango with the best quality and accuracy. World studies of mango classification according to color, size, volume and almost done in the laboratory but not yet applied in practice. The quality assessment of mango fruit has not been resolved. Application of image processing technology, computer vision combined with artificial intelligence in the problem of mango classification or poor quality. The goal of the study is to create a system that can classify mangoes in terms of color, volume, size, shape and fruit density. The classification system using image processing incorporates artificial intelligence including the use of CCD cameras, C language programming, computer vision and artificial neural networks. The system uses the captured mango image, processing the split layer to determine the mass, volume and defect on the mango fruit surface. Especially, determine the density of mangoes related to its maturity and sweetness and determine the percentage of mango defects to determine the quality of mangoes for export and domestic or recycled mangoes. This article is about the development of an automatic mango classification system to control and evaluate mango quality before packaging and exporting to the market. It is in the research, design and fabrication of mango classification model and the completion of an automatic mango classification system using machine vision combining artificial intelligence. Index Terms-The classification of mango, sorting of mangoes, image processing technology, artificial intelligence; computer vision, artificial neural networks.

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Mango Fruit Sortation System using Neural Network and Computer Vision

Cited by 47 publications

References 2 publications

Hass avocado ripeness classification by mobile devices using digital image processing and ANN methods

Hass avocado ripeness classification by mobile devices using digital image processing and ANN methods

Size Classification of Tomato Fruit Using Thresholding, Machine Learning, and Deep Learning Techniques

Mango Sorting Mechanical System Uses Machine Vision and Artificial Intelligence

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