Arduino based supervision of banana ripening stages

Butola, Brijesh Singh; Sharma, Piyush; Singh, Yashika; Amin, Yabrin

doi:10.1109/ngct.2015.7375254

Cited by 5 publications

(5 citation statements)

References 2 publications

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“…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 31: What Programming Interfaces Have Been Used For Impl...mentioning

confidence: 99%

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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“…In [73], [74], [76], [80], [82], [86], [88]- [90], [91], [92], [94]- [97], [100] and Raspberry PI [75], [77]- [79], [83], [87], [91] have been the most widely used platforms to be integrated into IoT4D projects.…”

Section: A Low-cost Iotmentioning

confidence: 99%

“…The network sends data to a central node that usually acts as a gateway by sending information to the cloud via WWAN technologies (mobile communications in many cases). Mobile communications [72], [80], [91], [93], [99] also offer a good solution in areas deprived of traditional telecommunications (wired) networks. After reviewing, Sothern Asia was identified as the region with more IoT4D projects based on low cost technology.…”

Section: A Low-cost Iotmentioning

confidence: 99%

Challenges and Opportunities of the Internet of Things for Global Development to Achieve the United Nations Sustainable Development Goals

2020

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Internet of Things (IoT) makes a significant contribution to development in economical, social and ecological terms. In this work, the current context to successfully apply IoT in developing countries to achieve the Sustainable Development Goals (SDGs) of United Nations is studied. Potential and opportunities of the IoT in the developing world as well as the current challenges of IoT in developing countries are reviewed. The research methodology is focused on the IoT context in the developing world as well as profitable IoT projects carried out in developing countries, using the SDGs as a reference. Key features as a technical challenges, environmental conditions or social differences are studied to contextualize IoT in developing countries. Finally, the combination of low-cost IoT and pay-as-you-go is presented as the best scenario to better disseminate the Internet of Things for Development (IoT4D) to achieve the Sustainable Development Goals by 2030. INDEX TERMS Internet of things, developing countries, sustainable development goals, IoT4D.

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“…This will be a great loss to the farmers. [1][2]. This paper represents the study of these stages of Mango by MATLAB R2014b version.…”

Section: Introductionmentioning

confidence: 99%

Study of Ripening Stages of Mango by Image Processing Techniques

Sumathi,

Archana,

Senthil

et al. 2023

J. Phys.: Conf. Ser.

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Mango reaches its maturity in different stages which comprise both size and color. These maturity stages are well-known by the farmer in the field. On the basis of these different stages of maturity or commonly say ‘ripening of Mango’ one can predict when these are perfect for harvesting. The stages comprise a distinct pattern in which Mango gets ripped. In this paper, a study is carried out on the different stages of ripening of Mangoes based on their color. Image processing techniques are used to predict the mango’s different ripening stages by taking the Mango’s image.

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Arduino based supervision of banana ripening stages

Cited by 5 publications

References 2 publications

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

Challenges and Opportunities of the Internet of Things for Global Development to Achieve the United Nations Sustainable Development Goals

Study of Ripening Stages of Mango by Image Processing Techniques

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