Determining Banana Types and Ripeness from Image using Machine Learning Methods

Sabilla, Irzal Ahmad; Wahyuni, Cahyaningtyas Sekar; Fatichah, Chastine; Herumurti, Darlis

doi:10.1109/icaiit.2019.8834490

Cited by 29 publications

(25 citation statements)

References 3 publications

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“…A single banana is called a finger, and a grouping of attached fingers of 10 or more bananas is called a hand [ 3 ]. In this review, out of the 35 studies, a total of 27 [ 3 , 4 , 5 , 6 , 9 , 10 , 11 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ] have used the fingers of bananas, 3 [ 37 , 38 , 39 ] studies have used the fingers by hand and Chen et al [ 40 ] used a bunch of bananas. Mohamedon et al and Rodrigues et al [ 41 , 42 ] used a mixed sample of bananas, Mendoza and Aguilera [ 43 ] used the combination of fingers by hand and a single batch of bananas, and whereas Saragih and Emanuel [ 44 ] also used the used the combination of fingers and fingers and hand bananas.…”

Section: Resultsmentioning

confidence: 99%

“…Out of the 35 studies, most of the studies (21) have used Musa cavendish banana categories. Apart from that, three [ 6 , 9 , 25 ] studies used the Egyptian species, and Sabilla et al [ 21 ] used local bananas from traditional markets in Indonesia (Pisang Emas, Pisang Kepok, Pisang Ambon, Pisang Raja Nangka, Pisang Santen, Pisang Susu, and Pisang Candi). Likewise, Saragih and Emanuel’s [ 44 ] study used the combination of two categories (Egyptian species and an unreported category), while the remaining 10 [ 11 , 18 , 24 , 26 , 30 , 34 , 37 , 38 , 41 , 42 ] studies did not report their banana categories.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Non-Destructive Banana Ripeness Detection Using Shallow and Deep Learning: A Systematic Review

Baglat

Hayat

Mendonça

et al. 2023

Sensors

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The ripeness of bananas is the most significant factor affecting nutrient composition and demand. Conventionally, cutting and ripeness analysis requires expert knowledge and substantial human intervention, and different studies have been conducted to automate and substantially reduce human effort. Using the Preferred Reporting Items for the Systematic Reviews approach, 1548 studies were extracted from journals and conferences, using different research databases, and 35 were included in the final review for key parameters. These studies suggest the dominance of banana fingers as input data, a sensor camera as the preferred capturing device, and appropriate features, such as color, that can provide better detection. Among six stages of ripeness, the studies employing the four mentioned stages performed better in terms of accuracy and coefficient of determination value. Among all the works for detecting ripeness stages prediction, convolutional neural networks were found to perform sufficiently well with large datasets, whereas conventional artificial neural networks and support vector machines attained better performance for sensor-related data. However, insufficient information on the dataset and capturing device, limited data availability, and exploitation of data augmentation techniques are limitations in existing studies. Thus, effectively addressing these shortcomings and close collaboration with experts to predict the ripeness stages should be pursued.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Non-Destructive Banana Ripeness Detection Using Shallow and Deep Learning: A Systematic Review

Baglat

Hayat

Mendonça

et al. 2023

Sensors

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show abstract

“…A random forest classifier was utilized to grade the bananas according to the color features in [28], and the accuracy arrived at 94.2%. In [29], they also adopted machine learning algorithms to classify different types of bananas and their ripeness levels. SVM achieved an accuracy of 99.1% to classify the banana types and has a 96.6% accuracy in distinguishing the level of ripeness.…”

Section: Related Workmentioning

confidence: 99%

Support Vector Machine and YOLO for a Mobile Food Grading System

Zhu,

Spachos

2021

Preprint

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Food quality and safety are of great concern to society since it is an essential guarantee not only for human health but also for social development, and stability. Ensuring food quality and safety is a complex process. All food processing stages should be considered, from cultivating, harvesting and storage to preparation and consumption. Grading is one of the essential processes to control food quality. This paper proposed a mobile visual-based system to evaluate food grading. Specifically, the proposed system acquires images of bananas when they are on moving conveyors. A two-layer image processing system based on machine learning is used to grade bananas, and these two layers are allocated on edge devices and cloud servers, respectively. Support Vector Machine (SVM) is the first layer to classify bananas based on an extracted feature vector composed of color and texture features. Then, the a You Only Look Once (YOLO) v3 model further locating the peel's defected area and determining if the inputs belong to the mid-ripened or well-ripened class. According to experimental results, the first layer's performance achieved an accuracy of 98.5% while the accuracy of the second layer is 85.7%, and the overall accuracy is 96.4%.

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“…During ripening, the firmness of banana decreases, which can also be used as a quality indicator. The tempering of banana mainly instigated by the enzyme activities in the cell wall involves polygalacturonase (PG), Pectate Lyase (PL), Pectin Methyl Esterase (PME), and cellulose, and activities of these enzymes are mainly ethylene dependent [ 17 ].…”

Section: Banana Ripening Processmentioning

confidence: 99%

Xbee-Based WSN Architecture for Monitoring of Banana Ripening Process Using Knowledge-Level Artificial Intelligent Technique

Altaf

Ahmad

Zaindin

et al. 2020

Sensors

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Real-time monitoring of fruit ripeness in storage and during logistics allows traders to minimize the chances of financial losses and maximize the quality of the fruit during storage through accurate prediction of the present condition of fruits. In Pakistan, banana production faces different difficulties from production, post-harvest management, and trade marketing due to atmosphere and mismanagement in storage containers. In recent research development, Wireless Sensor Networks (WSNs) are progressively under investigation in the field of fruit ripening due to their remote monitoring capability. Focused on fruit ripening monitoring, this paper demonstrates an Xbee-based wireless sensor nodes network. The role of the network architecture of the Xbee sensor node and sink end-node is discussed in detail regarding their ability to monitor the condition of all the required diagnosis parameters and stages of banana ripening. Furthermore, different features are extracted using the gas sensor, which is based on diverse values. These features are utilized for training in the Artificial Neural Network (ANN) through the Back Propagation (BP) algorithm for further data validation. The experimental results demonstrate that the projected WSN architecture can identify the banana condition in the storage area. The proposed Neural Network (NN) architectural design works well with selecting the feature data sets. It seems that the experimental and simulation outcomes and accuracy in banana ripening condition monitoring in the given feature vectors is attained and acceptable, through the classification performance, to make a better decision for effective monitoring of current fruit condition.

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Determining Banana Types and Ripeness from Image using Machine Learning Methods

Cited by 29 publications

References 3 publications

Non-Destructive Banana Ripeness Detection Using Shallow and Deep Learning: A Systematic Review

Non-Destructive Banana Ripeness Detection Using Shallow and Deep Learning: A Systematic Review

Support Vector Machine and YOLO for a Mobile Food Grading System

Xbee-Based WSN Architecture for Monitoring of Banana Ripening Process Using Knowledge-Level Artificial Intelligent Technique

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