Development of an effective system for remote monitoring of banana ripening process

Hegadi, Rajendra; Sahu, K. B. Madhu

doi:10.1109/wiecon-ece.2015.7443987

Cited by 12 publications

(5 citation statements)

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“…There exist different well-known designs that researchers may use to begin assembling their systems instead of beginning from scratch. These incorporate AlexNet [35], VGG, CaffeNet [36], GoogleNet [37], and Inception-ResNet [6], among others. Every design has various points of interest that make it suitable to be used.…”

Section: Ri 3: How the Data Has Been Collected For The Precision Agri...mentioning

confidence: 99%

“…Reduced leaves size, turning brittle Virus [42], [46] Banana bract mosaic virus Programming Interface/Software Used Studies MATLAB [4], [5], [7], [37], [42], [43], [45], [46], [50], [54]- [56] PYTHON [49], [57] Colabeler [57] WEKA [58], [59] Torch7 ML Computational Framework (LuaJIT Programming Language) [9] AgiSoft PhotoScan, (Agisoft LLC, St Petersburg, Russia) software [44]…”

Section: ) Ri 31: What Programming Interfaces Have Been Used For Impl...mentioning

confidence: 99%

“…In [6], the authors have deployed the k-means algorithm to extract the colored features of bananas and then detect the different ripeness stages of it. In [56], the authors have used an Arduino to detect the distinct ripeness stages, [37] deployed the GSM modem to extract the color indices from bananas and monitor the ripeness stages with an accuracy of 93%, and [36] used SVM deployed for the shelf-life monitoring. Table XII deploys various ML-based techniques in order to predict the ripeness and shelf-life of banana fruit.…”

Section: ) Shelf-life and Ripeness Level Detectionmentioning

confidence: 99%

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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: Ri 3: How the Data Has Been Collected For The Precision Agri...mentioning

confidence: 99%

Section: ) Ri 31: What Programming Interfaces Have Been Used For Impl...mentioning

confidence: 99%

Section: ) Shelf-life and Ripeness Level Detectionmentioning

confidence: 99%

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

et al. 2022

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“…In the existing literature, only few efforts are made towards identifying the chemically ripened fruits by employing hybrid techniques including computer vision and sensor techniques [14]. For instance, Verma and Hegadi [20] presented a remote monitoring system for banana ripening process by employing wireless networks, which helps the user to monitor the ripening from a remote place. Recently, Srividya et al [18] proposed an ethylene measurement system in order to predict correct stage of ripening of fruits using image-based features.…”

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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“…Rajendra Hegadi et al has proposed an easy monitoring system to monitor the color indices of the bananas and to identify ripening stage of banana; this information will be sent to the monitoring person automatically to the remote area. The paper has presented an effective system for wireless monitoring of the processing of the banana fruits in the store room [2]. Senthilarasi et al has detected the Musa Species in Fruit Industry using shape based approach.…”

Section: Introductionmentioning

confidence: 99%

A multivariate Gaussian modeling for counting bananas

Senthilarasi¹,

Navarasy²,

C³

et al. 2018

IJET

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Agriculture industries play an important role in economic development. Many post harvest techniques are performed in these industries. Especially in fruit industries, grading is an important process. As per the grading standard, banana hands must have minimum of twelve banana fingers. In the current scenario, the banana fingers are counted manually which is laborious. In order to reduce manual work, this paper proposes an algorithm for performing an automatic counting system of banana fingers in a bunch/hand using image processing algorithms. The parameters of the Gaussian model is acquired from the samples of fruit distal ends. A multivariate Gaussian modeling is accomplished on HSV color model to detect the distal end of banana fingers using maximum likelihood detection.

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Development of an effective system for remote monitoring of banana ripening process

Cited by 12 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 multivariate Gaussian modeling for counting bananas

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