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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: | Crop yield prediction based on geographical location for Indian agriculture |
| Authors Name: | Ch.Tejasri , A Vinay Kumar , B.Vinay Kumar , Ayesha siddiquah , Ch.Suresh |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_190740
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| Published Paper Id: | IJNRD2304146 |
| Published In: | Volume 8 Issue 4, April-2023 |
| DOI: | |
| Abstract: | Predicting crop yields is a crucial issue in agriculture because it enables farmers and decision-makers to plan the planting, harvesting, and distribution of their products. The use of machine learning and artificial intelligence approaches to create precise and trustworthy agricultural production prediction models has gained popularity in recent years. These models make use of a variety of data sources, including satellite imaging, soil characteristics, historical crop yields, and weather patterns. In India, agriculture is a significant source of both income and employment. The most frequent issue Indian farmers have is that they choose the wrong crop and don't utilize the right fertilizer for their soil. As a result, they will see a major decline in productivity. The farmer's facility problem has been solved with precision agriculture. They seek to accurately and precisely anticipate agricultural yields so that farmers may improve their planting techniques, cut down on waste, and boost overall output. Random forests, neural networks, and support vector machines are a few of the well-liked machine learning techniques used for agricultural yield prediction. To understand the correlations between input variables and agricultural yields, these algorithms are trained on vast databases of historical crop yields and other pertinent data. By giving farmers useful information and direction on crop management, crop yield prediction models have the potential to change the agricultural industry. They can aid in cost-cutting, cost optimization, and profit maximization. |
| Keywords: | Soil properties, algorithms, Geographical location, agriculture, crop recommendation,yield |
| Cite Article: | "Crop yield prediction based on geographical location for Indian agriculture", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 4, page no.b403-b406, April-2023, Available :http://www.ijnrd.org/papers/IJNRD2304146.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:IJNRD2304146 Registration ID: 190740 Published In: Volume 8 Issue 4, April-2023 DOI (Digital Object Identifier): Page No: b403-b406 Country: Martur, Andhra Pradesh, India Research Area: Electronics & Communication Engg. Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2304146 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2304146 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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