2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 6 , Issue 2 , PP: 57-63, 2021 | Cite this article as | XML | PDF | Full Length Article
Akshita Waldia 1 * , Pragati Garg 2 , Priyanka Garg 3 , Rachna Tewani 4 , Arun Kumar Dubey 5 , Anurag Agrawal 6
Doi: https://doi.org/10.54216/FPA.060203
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.
Machine Learning , Crop prediction , Decision tree , Naive Bayes , Random Forest , crop recommendation
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