Agricultural Crop Recommendation Based on Productivity and Season
Khandode Priyanka
, Ugale Sakshi , Nagare Sakshi , Khairnar Vrushali , Prof. Rahul Dhokane
Precision Agriculture, Machine learning, Crop prediction, Naive Bayes, Supervised Learning, Effective farming
As we know the fact that, India is the second largest population country in the world and majority of people in India have agriculture as their occupation. Farmers are growing same crops repeatedly without trying new verity of crops and they are applying fertilizers in random quantity without knowing the deficient content and quantity. So, this is directly affecting on crop yield and also causes the soil acidification and damages the top layer. So, we have designed the system using machine learning algorithms for betterment of farmers. Our system will suggest the best suitable crop for particular land based on content and weather parameters. And also, the system provides information about the required content and quantity of fertilizers, required seeds for cultivation. Hence by utilizing our system farmers can cultivate a new variety of crop, may increase in profit margin and can avoid soil pollution
"Agricultural Crop Recommendation Based on Productivity and Season", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.7, Issue 11, page no.990 - 992, November-2022, Available :https://ijsdr.org/papers/IJSDR2211145.pdf
Volume 7
Issue 11,
November-2022
Pages : 990 - 992
Paper Reg. ID: IJSDR_202764
Published Paper Id: IJSDR2211145
Downloads: 000347340
Research Area: Engineering
Country: -, -, --
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
