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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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| Paper Title: | Machine Learning Algorithm for Smart Agriculture |
| Authors Name: | Pooja S A , Smt. Anitha G |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_206363
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| Published Paper Id: | IJRTI2509103 |
| Published In: | Volume 10 Issue 9, October-2025 |
| DOI: | |
| Abstract: | At the beginning of each season, every farmer in India must make a crucial decision: what to plant? A myriad of elements, from the ground beneath their feet to the sky above, impact this high-stakes decision. A diminished harvest, squandered resources, and considerable financial hardship can result from a slip-up. The inspiration for our initiative came from a need to provide farmers with a trustworthy resource that would allow them to make informed decisions about their property, eliminating guesswork and fostering confidence based on data. Our Crop Recommendation System puts the knowledge of an experienced agriculturalist in the hands of the farmer. Support Vector Machines (SVMs) are the advanced machine learning techniques that drive this system. To put it simply, we trained the SVM to recognize patterns like a pro. The process is analogous to cleverly drawing a line to demarcate sets of data. We provided it hundreds of examples that showed how different climates and soil types affect the growth of different crops. The SVM gets very good at identifying which crops are native to particular ecosystems after learning to spot these intricate patterns. Our technology uses a diverse set of agricultural data to generate reliable forecasts. We zeroed in on what matters most for a crop's success. Among them are the soil nutrients that plants rely on for sustenance, including nitrogen (N), phosphorus (P), and potassium (K). Important climatic factors such as average humidity, local temperature, and rainfall amounts are also included. Lastly, we take the soil's pH into account, since this chemical equilibrium has a major impact on the plant's nutrient absorption capacity. Taken as a whole, these details provide a computerized picture of the farmer's field. |
| Keywords: | Crop Recommendation, Machine Learning, Support Vector Machine, Smart Agriculture, Decision Support System. |
| Cite Article: | "Machine Learning Algorithm for Smart Agriculture", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 9, page no.b11-b15, October-2025, Available :http://www.ijrti.org/papers/IJRTI2509103.pdf |
| Downloads: | 000240 |
| 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 |
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Published Paper ID: IJRTI2509103
Registration ID:206363
Published In: Volume 10 Issue 9, October-2025
DOI (Digital Object Identifier):
Page No: b11-b15 Country: Davanagere, Karnataka, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2509103 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2509103 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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