Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (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)
—Sustainable agriculture demand precise, data-driven
strategies to maximize crop productivity while minimizing environmental harm. This study presents an innovative, scalable, and
intelligent decision- making framework that leverages Machine
Learning (ML) algorithm such as random forest regression
(RFR) to estimate the nutrients requirement. Decision Tree
classifier for smart crop recommendation. Predictive analytics
for fertilizer recommendation on the basis of N, P, K values
in the soil. Also, Convolutional Neural Network (CNN) with
RaceNet for diseases detection, to help farmer in assisting proper
plant health. Beyond enhancing farming efficiency, this research
aspires to drive technological advancements in agriculture by
demonstrating the transformative power of AI, ML, and data
analytics. By integrating cutting-edge technology with traditional
farming methods, this study envisions a future where precision
agriculture empowers farmers, strengthens global food security,
and supports eco-friendly agricultural evolution.
"Intelligent Decision-Making for Sustainable Farming Using Machine Learning", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 5, page no.a674-a678, May-2025, Available :http://www.ijrti.org/papers/IJRTI2505080.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator