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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 RECOMMENDATION SYSTEM USING MACHINE LEARNING |
| Authors Name: | Ms.P.Sathya , I.Imalin Nijitha , S.Karthika , K.Kavithasri |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_219431
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| Published Paper Id: | IJNRD2404664 |
| Published In: | Volume 9 Issue 4, April-2024 |
| DOI: | |
| Abstract: | The agricultural sector plays a vital role in economic growth, particularly in countries like India where a significant portion of the population relies on farming for livelihood. To assist farmers in maximizing their yield and simplifying crop management, this project introduces a web-based application. This project utilizes Machine Learning (ML) technologies to deliver personalized recommendations for crop selection, fertilizer usage, and disease management. Custom-built datasets for crop and fertilizer recommendations, along with an existing dataset for disease detection, form the foundation of this project. The system employs predictive analytics to forecast crop yields and identify potential risks such as pest infestations or nutrient deficiencies. Based on these insights, personalized recommendations are generated for farmers, including optimal planting schedules, irrigation strategies, and crop rotation plans. this research contributes to the advancement of precision agriculture by offering a practical and effective solution for crop monitoring and management, ultimately fostering a more efficient and resilient food production system. By providing personalized recommendations for crop selection, fertilizer usage, and disease management, your web-based application could empower farmers to make informed decisions tailored to their specific needs and local conditions. The use of predictive analytics to forecast crop yields and identify potential risks such as pest infestations or nutrient deficiencies is particularly exciting, as it enables proactive intervention to mitigate these risks and optimize crop production. The disease detection component utilizes image recognition techniques to identify crop diseases from photographs captured in the field. By integrating these functionalities, the system empowers farmers to make informed decisions for optimal crop selection, improved yield, and efficient resource management. Additionally, a fertilizer optimization model is integrated to accurately determine the optimal type and quantity of fertilizers required for each identified crop, thereby minimizing environmental impact and maximizing yield. |
| Keywords: | agricultural technology, yield forecasting, disease detection, image recognition, crop management, machine learning, predictive analytics, personalized recommendations, crop selection, fertilizer optimization, pest infestations, and recognition. |
| Cite Article: | "CROP RECOMMENDATION SYSTEM USING MACHINE LEARNING", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 4, page no.g539-g547, April-2024, Available :http://www.ijnrd.org/papers/IJNRD2404664.pdf |
| Downloads: | 00031 |
| 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:IJNRD2404664 Registration ID: 219431 Published In: Volume 9 Issue 4, April-2024 DOI (Digital Object Identifier): Page No: g539-g547 Country: Vannarpettai, Tirunelveli, Tamil Nadu, India Research Area: Information Technology Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2404664 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2404664 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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