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Full Length Article
Fusion: Practice and Applications
Volume 6 , Issue 2, PP: 57-63 , 2021 | Cite this article as | XML |PDF

Title

Crop Recommendation Using Machine Learning

Authors Names :   Akshita Waldia   1 *     Pragati Garg   2     Priyanka Garg   3     Rachna Tewani   4     Arun Kumar Dubey   5     Anurag Agrawal   6  

1  Affiliation :  Bharati Vidyapeeth’s College of Engineering, New Delhi, India

    Email :  akshitawaldia7@gmail.com


2  Affiliation :  Bharati Vidyapeeth’s College of Engineering, New Delhi, India

    Email :  gargpragati9@gmail.com


3  Affiliation :  Bharati Vidyapeeth’s College of Engineering, New Delhi, India

    Email :  priyanka.garg1803@gmail.com


4  Affiliation :  Data Scientist ,Great Learning, India

    Email :  rachnatewani09@gmail.com


5  Affiliation :  Bharati Vidyapeeth’s College of Engineering, New Delhi, India

    Email :  arudubey@gmail.com


6  Affiliation :  Bharati Vidyapeeth’s College of Engineering, New Delhi, India

    Email :  ind.anurag@gmail.com



Doi   :   https://doi.org/10.54216/FPA.060203

Received February 02, 2021 Accepted August 30, 2021

Abstract :

The population of India is over one billion. Nearly 65 percent of the population of India lives in villages with the main occupation being agriculture. The diverse climatic conditions in the country result in the production of a large number of agricultural items. Many surveys have proved that the suicide rate of farmers is proliferating over years due to the selection of the wrong crop resulting in less yield. In some areas, farmers lack information about the composition of soil and weather conditions and may choose the wrong crop to sow which results in lesser yield. Production of crops depends on geographical parameters like humidity, rainfall, and properties of soil such as pH, and NPK content. Integration of technology with agriculture helps the farmer to improve his production. The main goal of agricultural planning is to achieve the maximum yield rate of crops by using a limited number of land resources. This paper mainly focuses on recommending the appropriate crop using ML Algorithms ( Decision Tree, Naive Bayes, Random Forest ) based on soil composition and weather conditions to maximize the yield of the farm and increase the economic condition of India’s farmers.

Keywords :

Machine Learning; Crop prediction; Decision tree; Naive Bayes; Random Forest; crop recommendation

References :

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[6] Shah, Ayush, Akash Dubey, Vishesh Hemnani, Divye Gala, and D. R. Kalbande. "Smart farming system: Crop yield prediction using regression techniques." In Proceedings of International Conference on Wireless Communication, pp. 49-56. Springer, Singapore, 2018.

[7] A. Sharma, A. Jain, P. Gupta and V. Chowdary, "Machine Learning Applications for Precision Agriculture: A Comprehensive Review," in IEEE Access, vol. 9, pp. 4843-4873, 2021, doi: 10.1109/ACCESS.2020.3048415.

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Cite this Article as :
Akshita Waldia , Pragati Garg , Priyanka Garg , Rachna Tewani , Arun Kumar Dubey , Anurag Agrawal, Crop Recommendation Using Machine Learning, Fusion: Practice and Applications, Vol. 6 , No. 2 , (2021) : 57-63 (Doi   :  https://doi.org/10.54216/FPA.060203)