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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 PREDICTION IN AGRICULTURAL ENVIROMENT USING FEATURE SELECTION METHODS AND CLASSIFIERS |
| Authors Name: | Dr.A.Mummoorthy , Aduri Mano Rashmitha , Chintakayala Bhavesh , Bommu Naveen Kumar |
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IJNRD_214760
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| Published Paper Id: | IJNRD2403149 |
| Published In: | Volume 9 Issue 3, March-2024 |
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| Abstract: | In India, agriculture is a major source to increase the Indian economy. Generally, based on conventional methods like depending on agriculture field, the farmers are used to cultivating the specific crop type such as Wheat, Rice, Maize, Muskmelon, etc. But sometimes due to the bad weather conditions, the farmers will get poor crop yield and sometimes they lose the entire crop. Based on the traditional process, only experts can predict the crop type with previous knowledge but sometimes this prediction will get false results. Therefore, based on machine learning methodologies, it will improve the accuracy of crop prediction & fertilizer suggestion, because in modern automatic technologies the machine learning methods are providing good results. So that we would like to implement the ML classification techniques such as KNN, RF, DT, SVM and Voting ensemble classifiers to predict the crop type. In this work, we have collected a crop dataset with parameters such as temperature, humidity, rainfall, pH which is trained with all ML algorithms to obtain the best accuracy model. Here the experimental results evaluated that the Voting ensemble classifier is providing the best accuracy with 95% compared to other classifiers. |
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| Cite Article: | "CROP PREDICTION IN AGRICULTURAL ENVIROMENT USING FEATURE SELECTION METHODS AND CLASSIFIERS", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 3, page no.b441-b445, March-2024, Available :http://www.ijnrd.org/papers/IJNRD2403149.pdf |
| Downloads: | 00046 |
| 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:IJNRD2403149 Registration ID: 214760 Published In: Volume 9 Issue 3, March-2024 DOI (Digital Object Identifier): Page No: b441-b445 Country: Hyderabad, Telangana, India Research Area: Information Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2403149 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2403149 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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