Design and development of a portable instrument for the detection of artificial ripening of banana fruit

Hallur, Veena; Atharga, Bhagyashree; Hosur, Amruta; Binjawadagi, Bhagyashree; Bhat, Krishnamurthy

doi:10.1109/cimca.2014.7057776

Cited by 8 publications

(4 citation statements)

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“…Furthermore, in [86], the authors have proposed the development of a device that could determine if a banana has been artificially or organically ripened. It has a motor, a banana holder for rotating the fruit, an image processing part, a camera, and a display unit.…”

Section: ) Shelf-life and Ripeness Level Detectionmentioning

confidence: 99%

A Systematic Literature Review of Machine Learning Techniques Deployed in Agriculture: A Case Study of Banana Crop

et al. 2022

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for a project on "Application of IoT in Agriculture Sector" through the ICPS division under grant ID DST-319."ABSTRACT Agricultural productivity is the asset on which the world's economy thoroughly relies. This is one of the major causes that disease identification in fruits and plants occupies a salient role in farming space, as having disease disorders in them is obvious. There is a need to carry genuine supervision to avoid crucial consequences in vegetation; otherwise, corresponding vegetation standards, quantity, and productiveness gets affected. At present, a recognition system is required in the food handling industries to uplift the effectiveness of productivity to cope with demand in the community. The study has been carried out to perform a systematic literature review of research papers that deployed machine learning (ML) techniques in agriculture, applicable to the banana plant and fruit production. Thus; it could help upcoming researchers in their endeavors to identify the level and kind of research done so far. The authors investigated the problems related to banana crops such as disease classification, chilling injuries detection, ripeness, moisture content, etc. Moreover, the authors have also reviewed the deployed frameworks based on ML, sources of data collection, and the comprehensive results achieved for each study. Furthermore, ML architectures/techniques were evaluated using a range of performance measures. It has been observed that some studies used the PlantVillage dataset, a few have used Godliver and Scotnelson dataset, and the rest were based on either real-field image acquisition or on limited private datasets. Hence, more datasets are needed to be acquired to enhance the disease identification process and to handle the other kind of problems (e.g. chilling injuries detection, ripeness, etc.) present in the crops. Furthermore, the authors have also carried out a comparison of popular ML techniques like support vector machines, convolutional neural networks, regression, etc. to make differences in their performance. In this study, several research gaps are addressed, allowing for increased transparency in identifying different diseases even before symptoms arise and also for monitoring the abovementioned problems related to crops.

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Section: ) Shelf-life and Ripeness Level Detectionmentioning

confidence: 99%

A Systematic Literature Review of Machine Learning Techniques Deployed in Agriculture: A Case Study of Banana Crop

et al. 2022

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“…Veena and Bhat [5] designed a simple portable instrument for the detection of chemically ripened banana fruits using color-based features, which can also find out the specific ripening stage of the sample banana fruit. Therefore, this method performs better in terms of detecting chemical ripening, yet it suffers in case of complex banana structures.…”

Section: Related Workmentioning

confidence: 99%

Chemical ripening and contaminations detection using neural networks-based image features and spectrometric signatures

Roopalakshmi

2021

MG&V

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In this pandemic-prone era, health is of utmost concern for everyone and hence eating good quality fruits is very much essential for sound health. Unfortunately, nowadays it is quite very difficult to obtain naturally ripened fruits, due to existence of chemically ripened fruits being ripened using hazardous chemicals such as calcium carbide. However, most of the state-of-the art techniques are primarily focusing on identification of chemically ripened fruits with the help of computer vision-based approaches, which are less effective towards quantification of chemical contaminations present in the sample fruits. To solve these issues, a new framework for chemical ripening and contamination detection is presented, which employs both visual and IR spectrometric signatures in two different stages. The experiments conducted on both the GUI tool as well as hardware-based setups, clearly demonstrate the efficiency of the proposed framework in terms of detection confidence levels followed by the percentage of presence of chemicals in the sample fruit.

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“…Texture is a significant property of the surface of fruits that characterizes its nature, so in many situations it is the only information that can be used for image analysis. The term texture [1] In the literatures reviewed, the researchers worldwide have been effectively working in a variety of texture analysis algorithms for different applications like detection, recognition, classification, segmentation, clustering of different chemicals present in fruits. Many algorithms endure from low sensitive detection, difficult back ground adaption and high memory requisite.…”

Section: Relatedworkmentioning

confidence: 99%

“…As the world is getting colonized, health is getting poor and everyone wants to have fruits. Fruit ripening is the natural process in which the fruits attained their desirable color, flavor, quality, edible nature, palatable, nutritious and other textual properties [1]. Usually fruits produce plant hormone called ethylene, which helps in the ripening process.…”

Section: Introductionmentioning

confidence: 99%

A Review on Non-Destructive Techniques for Evaluating Quality of Fruits

Ravindran¹,

Anitha²,

Ravindran³

2015

IJERT

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Even though the fruit ripening is a natural process, it can also be processed by means of different artificial fruit ripening agents. In the recent years, due to the effect of artificial ripening of fruits the various health related issues has increased. Thus the consequence of artificial ripening has become questionable. There are both direct and indirect health hazards allied with artificial fruit ripening agents, which requires both qualitative and quantitative analysis of chemicals and their impact on fruit quality. So it is important to evaluate the quality of artificially-ripened fruits, and to analyze the changes in food value. The presence of artificial ripening agent is usually encountered on the fruit skin. But it is also very important to enumerate the existence of detrimental chemicals within fruitflesh and to analysis the impact of these chemicals on the food value of artificially ripened fruits. This paper is a review in image processing in order to design a fruit grading system based on Machine vision, Near infrared spectroscopy and Hyperspectral imaging.

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Design and development of a portable instrument for the detection of artificial ripening of banana fruit

Cited by 8 publications

References 1 publication

A Systematic Literature Review of Machine Learning Techniques Deployed in Agriculture: A Case Study of Banana Crop

A Systematic Literature Review of Machine Learning Techniques Deployed in Agriculture: A Case Study of Banana Crop

Chemical ripening and contaminations detection using neural networks-based image features and spectrometric signatures

A Review on Non-Destructive Techniques for Evaluating Quality of Fruits

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