Relevant bibliographies by topics / Arecanuts

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Arecanuts'

Author: Grafiati
Published: 5 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "Arecanuts"

1

Satheesha, K. M., K. S. Rajanna, and Prasad K. Krishna. "A Review of the Literature on Arecanut Sorting and Grading Using Computer Vision and Image Processing." International Journal of Applied Engineering and Management Letters (IJAEML) 7, no. 2 (2023): 50–67. https://doi.org/10.5281/zenodo.7878092.

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<strong>Background/Purpose: </strong><em>These days, the involvement of computer science in agriculture and food science is expanding. Classification and fault identification of diverse products employ a variety of Artificial Intelligence (AI), soft computing approaches, and methodologies, which contribute to higher-quality products for consumers. The position of Arecanuts in the international and Indian markets, as well as the application of computer vision and image processing to a system for categorizing and grading Arecanuts, are the main topics of this article.</em> <strong>Objective: </s
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Shedthi B, Shabari, Madappa Siddappa, Surendra Shetty, and Vidyasagar Shetty. "Classification of arecanut using machine learning techniques." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 2 (2023): 1914. http://dx.doi.org/10.11591/ijece.v13i2.pp1914-1921.

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In agricultural domain research, image processing and machine learning techniques play an important role. This paper provides a unique solution for classifying the good and defective arecanuts based on their color, texture, and density value. In the market different varieties of arecanut are available. Usually, qualitative sorting is done manually, and this can be replaced by applying machine vision techniques to grade the arecanut. Classification of arecanut based on quality is done using various machine learning techniques and it is observed that artificial neural networks give good results
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Shabari, Shedthi Billadi, Siddappa Madappa, Shetty Surendra, and Shetty Vidyasagar. "Classification of arecanut using machine learning techniques." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 2 (2023): 1914–21. https://doi.org/10.11591/ijece.v13i2.pp1914-1921.

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In agricultural domain research, image processing and machine learning techniques play an important role. This paper provides a unique solution for classifying the good and defective arecanuts based on their color, texture, and density value. In the market different varieties of arecanut are available. Usually, qualitative sorting is done manually, and this can be replaced by applying machine vision techniques to grade the arecanut. Classification of arecanut based on quality is done using various machine learning techniques and it is observed that artificial neural networks give good results
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Dhanesha R., Et al. "Segmentation and Classification of Arecanut Bunches before harvesting." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 3953–62. http://dx.doi.org/10.17762/ijritcc.v11i9.9736.

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In the agriculture sector, arecanuts are an extremely valuable crop. The price of an arecanut depends on its stage of ripeness. As a result of a lack of expertise in judging the maturity level of arecanut bunches before harvest, farmers often lose profit. Precision agricultural techniques based on image processing and computer vision have recently assisted farmers in determining crop maturity quality. Precision agricultural techniques based on image processing and computer vision have recently assisted farmers in determining crop maturity quality. Therefore, accuracy in segmenting arecanut bun
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Hegde, Ajit, Vijaya Shetty Sadanand, Chinmay Ganapati Hegde, Krishnamurthy Manjunath Naik, and Kanaad Deepak Shastri. "Identification and categorization of diseases in arecanut: a machine learning approach." Indonesian Journal of Electrical Engineering and Computer Science 31, no. 3 (2023): 1803. http://dx.doi.org/10.11591/ijeecs.v31.i3.pp1803-1810.

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Arecanut is one of the prominent commercial crops that are grown worldwide for traditional medicines, furniture, cosmetics, food, veterinary preparations, and textile industries. It experiences a variety of diseases during its existence, from the bottom to the tip. The conventional method for detection of diseases is through visual inspection and it is also necessary to have properly designed laboratories to check these harvests. It is a time consuming and tedious task to inspect these crops across wide acres of plantations. The proposed system has been developed that uses convolutional neural
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B, Chethan. "Arecanut Disease Detection." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem41024.

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The tropical crop arecanut, sometimes referred to as betel nut, is primarily farmed in India. In terms of arecanut production and consumption, the nation ranks second in the world. The areca nut plant is vulnerable to numerous diseases that impact its roots, stem, leaves, and fruits throughout its life cycle. While some of these illnesses can be seen with the naked eye, others cannot. These illnesses are brought on by abrupt changes in temperature and other meteorological factors; early disease identification is crucial. In order to minimize losses for farmers, this work focuses on early and p
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Chandrashekhara, H., and M. Suresha. "Classification of Healthy and Diseased Arecanuts using SVM Classifier." International Journal of Computer Sciences and Engineering 7, no. 2 (2019): 544–48. http://dx.doi.org/10.26438/ijcse/v7i2.544548.

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Puneeth, B. R., and P. S. Nethravathi. "A Literature Review of the Detection and Categorization of various Arecanut Diseases using Image Processing and Machine Learning Approaches." International Journal of Applied Engineering and Management Letters (IJAEML) 5, no. 2 (2021): 183–204. https://doi.org/10.5281/zenodo.5773853.

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<strong>Background/Purpose: </strong><em>Every scholarly research project starts with a survey of the literature, which acts as a springboard for new ideas. The purpose of this literature review is to become familiar with the study domain and to assess the </em><em>work&#39;s credibility. It also improves with the subject&#39;s integration and summary. This article briefly discusses the detection of disease and classification to achieve the objectives of the study.</em> <strong>Objective:</strong> <em>The main objective of this literature survey is to explore the different techniques applied t
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Sabu, Kiran M., and T. K. Manoj Kumar. "Predictive analytics in Agriculture: Forecasting prices of Arecanuts in Kerala." Procedia Computer Science 171 (2020): 699–708. http://dx.doi.org/10.1016/j.procs.2020.04.076.

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Salunke, AS, and SS Honnungar. "Development of a true density-based automated quality grading device for unboiled arecanut kernels." African Journal of Food, Agriculture, Nutrition and Development 24, no. 8 (2024): 24298–318. http://dx.doi.org/10.18697/ajfand.133.24400.

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The automated sorting of arecanut kernels is a significant challenge that has not been effectively addressed thus far. Scientific grading techniques are necessary given the paradigm change toward investigating alternative uses for arecanuts in industry and the medical field. This research work emphasizes the relatively unexplored aspect of the post-harvest process; quality grading of kernels based on physical properties. It aimed to develop a novel approach for classifying unboiled (Chali) arecanut kernels cultivated in Goa, India based on their true density, using a combination of mechanical
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Dissertations / Theses on the topic "Arecanuts"

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Pilon, Guillaume. "Utilization of Arecanut «(Areca catechu)» husk for gasification." Thesis, McGill University, 2007. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=18271.

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Gasification of areca husk was studied in this research. The husk is an agricultural by-product of Arecanut (Areca catechu) that could be a potential energy source for the processing of the nut. The problem of slagging during arecanut gasification was investigated using a throat-less, lab-scale downdraft gasifier. The effect of air flow rates from 0.001 to 0.006 m3/s on slag formation was studied. With increase of air flow rate, the clinker formation was found to increase (r2 = 0.7191). Subsequent studies consisted of washing the husk to remove external contaminants picked up during sun dr
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Anitha, H. S. "Marketing of arecanut." Thesis, 1996. http://hdl.handle.net/2009/1589.

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Prakash, T. N. "An evaluation of arecanut co-operative marketing system in Karnataka." Thesis, 1997. http://hdl.handle.net/2009/2388.

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Hegde, D. A. "Impact of price behaviour on arecanut plantations and growth in Karnataka - A case study of North Kanara District." Thesis, 1990. http://hdl.handle.net/2009/3503.

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Singh, Thangam Gajan. "Molecular Actions Of Arecoline, An Alkaloid Implicated In The Manifestation Of Oral Submucous Fibrosis (OSMF)." Thesis, 2008. https://etd.iisc.ac.in/handle/2005/776.

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The pathogenesis of oral submucous fibrosis (OSMF) is due to a complex interplay between the production and degradation of extracellular matrix (ECM) protein components. In tissue fibrosis, there is a net accumulation of collagen as a result of an imbalance between enhanced production, deposition and impaired degradation of ECM components. OSMF is a chronic inflammatory condition of the oral cavity and regulation of a number of pro-inflammatory and fibrogenic cytokines such as interleukine-1, -6 and -8 isoforms, TGF-β, PDGF, bFGF, IFN-γ and TNF-α has been reported in OSMF tissues. The expressi
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Singh, Thangam Gajan. "Molecular Actions Of Arecoline, An Alkaloid Implicated In The Manifestation Of Oral Submucous Fibrosis (OSMF)." Thesis, 2008. http://hdl.handle.net/2005/776.

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The pathogenesis of oral submucous fibrosis (OSMF) is due to a complex interplay between the production and degradation of extracellular matrix (ECM) protein components. In tissue fibrosis, there is a net accumulation of collagen as a result of an imbalance between enhanced production, deposition and impaired degradation of ECM components. OSMF is a chronic inflammatory condition of the oral cavity and regulation of a number of pro-inflammatory and fibrogenic cytokines such as interleukine-1, -6 and -8 isoforms, TGF-β, PDGF, bFGF, IFN-γ and TNF-α has been reported in OSMF tissues. The expressi
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Books on the topic "Arecanuts"

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K, Manojkumar, and P. Premaja. Arecanut and spices database. Directorate of Arecanut and Spices Development, Government of India, 2007.

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2

Padmini, S. V. Economics of arecanut cultivation in Karnataka. Serials Publications, 2008.

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Z, John Kennedy, and Tamil Nadu Agricultural University, eds. Status report of arecanut in India. AICRP on Post Harvest Technology, Dept. of Agricultural Processing, College of Agricultural Engineering, Tamil Nadu Agricultural University, 2001.

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Giriappa, S. Arecanut production and marketing in India. MD Publications, 1994.

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Manojkumar, K., K. Ushakumari, P. Premaja, and P. K. Malathy. Area and production statistics of arecanut and spices. Directorate of Arecanut and Spices Development, Department of Agriculture and Cooperation, Ministry of Agriculture, Government of India, 2008.

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6

Buchy, M. Teak and arecanut: Colonial state, forest, and people in the Western Ghats, South India, 1800-1947. Institut français de Pondichery, 1996.

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India. Ministry of Agriculture & Co-operation. and India. Directorate of Cocoa, Arecanut & Spices Development., eds. Central sector schemes for the development of spices, medicinal and aromatic plants, arecanut, cocoa, betelvine during 8th plan period, 1992-'97. Govt. of India, Ministry of Agriculture, Dept. of Agriculture & Co-operation, Directorate of Cocoa, Arecanut & Spices Development, 1997.

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Agricultural marketing: A case study of arecanut marketing. Mangal Deep Publications, 2000.

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Book chapters on the topic "Arecanuts"

1

Sharma, Ankur, Prabhat Kumar, and V. K. Tripathi. "Arecanut." In Production Technology of Fruits and Plantation Crops. CRC Press, 2024. http://dx.doi.org/10.1201/9781003570592-23.

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Parthasarathy, S., R. Kalaivanan, R. Sangavi, and P. Lakshmidevi. "Arecanut." In Pests and Diseases in Spices, Plantation and Tuber Crops. CRC Press, 2025. https://doi.org/10.1201/9781003623779-2.

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Bhat, Ravi, S. Sujatha, Bhavishya, U. K. Priya, Alka Gupta, and Anok Uchoi. "Arecanut (Areca catechu L.)." In Soil Health Management for Plantation Crops. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0092-9_4.

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Nair, Kodoth Prabhakaran. "Arecanut (Areca catechu L.)." In Tree Crops. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62140-7_1.

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Danti, Ajit, and Suresha M. "Effective Multiclassifier for Arecanut Grading." In Wireless Networks and Computational Intelligence. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31686-9_41.

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Karun, Anitha, K. S. Muralikrishna, K. K. Sajini, and M. K. Rajesh. "Cryopreservation of Coconut and Arecanut Pollen." In Springer Protocols Handbooks. Springer US, 2023. http://dx.doi.org/10.1007/978-1-0716-2843-0_50.

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Mohan, Chandrika, A. Josephrajkumar, Shivaji H. Thube, E. K. Saneera, and Rajkumar. "Pests and Their Management in Arecanut." In Trends in Horticultural Entomology. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0343-4_66.

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Dhanesha, R., D. K. Umesha, Gurudeva Shastri Hiremath, G. N. Girish, and C. L. Shrinivasa Naika. "Segmentation and Classification of Unharvested Arecanut Bunches Using Deep Learning." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-75605-4_11.

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Dhanesha, R., and C. L. Shrinivasa Naika. "A Novel Approach for Segmentation of Arecanut Bunches Using Active Contouring." In Integrated Intelligent Computing, Communication and Security. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8797-4_69.

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Karthik, C. S., A. Pariari, Karma Chewang Bhutia, and Sonam Ongmu Bhutia. "Processing Methodologies for Few Plantation Crops in India (Arecanut, Betelvine, Cashew, Cocoa and Oil Palm)." In Innovations in Food Technology. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6121-4_3.

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Conference papers on the topic "Arecanuts"

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Salunke, Ajit, and Sunilkumar Honnungar. "Estimation of Physical Properties of Unboiled Type Arecanuts Using Load Cell and Ellipsoid Approximation." In 2025 3rd International Conference on Mechatronics, Control and Robotics (ICMCR). IEEE, 2025. https://doi.org/10.1109/icmcr64890.2025.10963097.

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Siddalingadevaru, Sneha Chikkanayakanahalli, Poojitha Devarahalli Ravindra, Vanitha Gangadhar, Srija Basavapatna Srinivas, Vivek Singh, and Ajay Kumar Dwivedi. "Disease Diagnosis and Sustainable Prevention in Arecanut Farming." In 2025 International Conference on Electronics and Renewable Systems (ICEARS). IEEE, 2025. https://doi.org/10.1109/icears64219.2025.10940189.

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G, Madhu B., Ram kumar G, Shreehari S. Rao, Chandrashekara M., and Amith Shetty A. R. "Detection Of Diseases In Arecanut Using Convolutional Neural Network." In 2024 Second International Conference on Advances in Information Technology (ICAIT). IEEE, 2024. http://dx.doi.org/10.1109/icait61638.2024.10690427.

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K, Beena, Sangeetha V, Hrishikesh Keraba Lohar, Nidhi G D, Varshini S, and Yogesh KN. "Arecanut Disease Prediction Using MobileNet V2, ResNet and VGG-16 Framework." In 2024 4th International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS). IEEE, 2024. https://doi.org/10.1109/icuis64676.2024.10866077.

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Hegde, Shreedhara N., and H. S. Vijaya Kumar. "Karnataka Malenadu Region Arecanut Leafspot Disease Severity Assessment Using Machine Learning." In 2025 3rd International Conference on Smart Systems for applications in Electrical Sciences (ICSSES). IEEE, 2025. https://doi.org/10.1109/icsses64899.2025.11009869.

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S, Sushitha, and Aparna K. "Exploring the Impact of Rainfall Variable on Arecanut Yield Production in Karnataka." In 2025 International Conference on Computing for Sustainability and Intelligent Future (COMP-SIF). IEEE, 2025. https://doi.org/10.1109/comp-sif65618.2025.10969877.

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Siddesha, S., S. K. Niranjan, and V. N. Manjunath Aradhya. "Texture based classification of arecanut." In 2015 International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT). IEEE, 2015. http://dx.doi.org/10.1109/icatcct.2015.7456971.

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Shwetha, B., and B. Bhaskar. "Smart Pesticide Sprayer for Arecanut." In 2017 International Conference on Recent Advances in Electronics and Communication Technology (ICRAECT). IEEE, 2017. http://dx.doi.org/10.1109/icraect.2017.44.

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Mittal, Rajat, Vijay Raghav Varada, Sidharth Dave, Ashish Khanna, Rahul Korpu, and Shristi Tilak. "Semi-autonomous arecanut tree-climbing robot." In 2014 9th International Conference on Industrial and Information Systems (ICIIS). IEEE, 2014. http://dx.doi.org/10.1109/iciinfs.2014.7036657.

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Devang, P. Soni, N. A. Gokul, M. Ranjana, S. Swaminathan, and B. Nair Binoy. "Autonomous arecanut tree climbing and pruning robot." In 2010 International Conference on Emerging Trends in Robotics and Communication Technologies (INTERACT 2010). IEEE, 2010. http://dx.doi.org/10.1109/interact.2010.5706160.

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You might also be interested in the extended bibliographies on the topic 'Arecanuts' for particular source types:
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