CROP RECOMMENDATION SYSTEM USING MACHINE LEARNING

Registration ID: IJNRD_219431

Published ID: IJNRD2404664

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Ms.P.Sathya, I.Imalin Nijitha, S.Karthika , K.Kavithasri

agricultural technology, yield forecasting, disease detection, image recognition, crop management, machine learning, predictive analytics, personalized recommendations, crop selection, fertilizer optimization, pest infestations, and recognition.

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.

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Ms.P.Sathya, I.Imalin Nijitha, S.Karthika , & K.Kavithasri (April-2024). CROP RECOMMENDATION SYSTEM USING MACHINE LEARNING. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(4), g539-g547. https://ijnrd.org/papers/IJNRD2404664.pdf

Volume 9 Issue 4, April-2024

Pages : g539-g547

Paper Reg. ID: IJNRD_219431

Published Paper Id: IJNRD2404664

Research Area: Information Technology 

Author Type: Indian Author

Country: Vannarpettai, Tirunelveli, Tamil Nadu, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2404664.pdf

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