Automated Agronomy: Evaluation of Fruits Ripeness Using Machine Learning Approach

Chmaj, Grzegorz; Sharma, Saugat; Selvaraj, Henry

doi:10.1007/978-3-030-65796-3_17

Cited by 3 publications

(2 citation statements)

References 10 publications

(17 reference statements)

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“…Furthermore, the investigation has not considered in vitro studies, since this review is limited to non-destructive methods for detecting banana ripeness stages. One article produced by Chmaj, Sharma and Selvaraj [ 15 ] could not be accessed due to the subscription for which the authors were contacted, but none of them replied. Another article by Li et al [ 16 ] could not be included because the paper was published in Japanese and there was no English reference available for this paper.…”

Section: Methodsmentioning

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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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: Methodsmentioning

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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“…It can no longer be identified. The proposed framework for this investigation is shown in detail in figure 1 below [26].…”

Section: Proposed Modelmentioning

confidence: 99%

Orange Fruit Image Quality Assessment Using Hybrid Model

Ubaid Ullah,

Ali Hussain,

Usman Ali

et al. 2023

JAC

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Since ripeness is regarded by consumers as the most important quality indication in the crops industry, maintaining and monitoring its ripeness has become a major issue in the business of growing crops. Additionally, as it significantly affects the product's quality and consumer preferences, the product's appearance is one of the makers' most pressing worries. However, the forecasting of storage life and the selection of the best harvest dates are still largely based on individual interpretation and practical experience. Threshold segmentation and certain morphological techniques are used in the early stage of the extraction of an area of interest. The segmented orange images are then divided into training and testing data sets, and the second stage entails extracting color-based traits from them. Choosing the classifier training settings is the focus of the third phase. The final phase, which makes use of the previously trained ANN, categorizes the data. One of the most crucial conclusions of this study is that creating a neural network is an empirical process that requires a lot of trials in order to identify the ideal variables that will give the neural classification algorithm the best performance. initialization of weights, the quantity of hidden neurons. Based on the proposed model the accuracy and efficiency is 1.3% which is more accurate result.

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