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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Machine Learning based early detection of Yellow leaf disease in arecanut plant using soil samples |
| Authors Name: | Shwetha Kamath , Prakhyath Devadiga , Prathiksha G K , Prathiksha S , Shrinidhi Shervegar |
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IJNRD_192227
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| Published Paper Id: | IJNRD2304477 |
| Published In: | Volume 8 Issue 4, April-2023 |
| DOI: | |
| Abstract: | Arecanut is an important cash crop grown extensively in India, especially in the southern states. It is also known as betel nut and is a major source of income for many farmers in the region. Arecanut is consumed widely in the form of chewing and is an integral part of social and religious practices in India. However, one of the major threats to arecanut cultivation is the Yellow Leaf Disease (YLD). The disease is characterized by yellowing of leaves, stunted growth, and reduced yield, leading to significant economic losses for farmers. The disease is also highly contagious and can spread rapidly, making early detection crucial for effective mitigation measures. Traditionally, the detection of YLD has relied on visual symptoms, which can be challenging to identify in the early stages. This delay in detection can result in significant crop losses. Therefore, there is a need for an early detection system that can identify the disease at an early stage and help farmers take necessary measures to control its spread. In this context, machine learning-based solutions using soil samples have emerged as a promising approach for early detection of YLD in arecanut plants. By analyzing various soil parameters, ID3 algorithm can detect the presence of the disease at an early stage. This can help farmers take timely action to prevent the spread of the disease and improve crop yields. |
| Keywords: | Yellow Leaf Disease, Arecanut, Machine Learning, Soil parameters, ID3 Algorithm. |
| Cite Article: | "Machine Learning based early detection of Yellow leaf disease in arecanut plant using soil samples", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 4, page no.e546-e551, April-2023, Available :http://www.ijnrd.org/papers/IJNRD2304477.pdf |
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| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2304477 Registration ID: 192227 Published In: Volume 8 Issue 4, April-2023 DOI (Digital Object Identifier): Page No: e546-e551 Country: Dakshina Kannada, Karnataka, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2304477 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2304477 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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