Fertiledata: Advanced Strategies For Crop Optimization Through Machine Learning Processing

Year : 2025 | Volume : 14 | Issue : 02 | Page : 25 35
    By

    Ramaraj S,

  • Sanjay K,

  • Haris Nisanthan M.,

  • Vikash Kannan B.,

  • Linges G.,

  1. Assistant Professor, Department of Computer Science and Engineering, Karpagam College of Engineering Coimbatore, Tamil Nadu, India
  2. Research Scholar, Department of Computer Science and Engineering, Karpagam College of Engineering Coimbatore, Tamil Nadu, India
  3. Research Scholar, Department of Computer Science and Engineering, Karpagam College of Engineering Coimbatore, Tamil Nadu, India
  4. Research Scholar, Department of Computer Science and Engineering, Karpagam College of Engineering Coimbatore, Tamil Nadu, India
  5. Research Scholar, Department of Computer Science and Engineering, Karpagam College of Engineering Coimbatore, Tamil Nadu, India

Abstract

The venture, titled “Fertile Data: Advanced Strategies for Crop Optimization Through Machine Learning processing” is created utilizing HTML, CSS, and JavaScript for the front conclusion, and Python for the back conclusion. In a nation like India, where a noteworthy parcel of the populace depends on agribusiness for their vocation, joining progressed advances such as Machine Learning and Profound Learning into cultivating hones can revolutionize the industry. This venture presents a user-friendly site planned to help agriculturists in making educated choices through two key instruments: Trim Proposal and Fertilizer Proposal. The Trim Suggestion device makes a difference. Agriculturists recognize the most reasonable crops for their soil and natural conditions, guaranteeing ideal utility of their arrival and maximizing abdication. In the meantime, the Fertilizer Proposal apparatus gives experiences into the particular supplements required by the soil for their chosen crops, empowering productive fertilizer utilization and minimizing squander. By leveraging progressed AI innovation, the stage advances maintainability, asset productivity, and natural preservation, making advanced cultivating available indeed to those with constrained mechanical involvement. This activity points to engaging ranchers and drives a more feasible and beneficial future in horticulture.

Keywords: Crop Optimization, Machine Learning, Python, Agriculture, Crop & Fertilizer Recommendation, AI, Sustainability, Resource Efficiency, Farmers, Precision Farming.

[This article belongs to Research & Reviews : Journal of Agricultural Science and Technology ]

How to cite this article:
Ramaraj S, Sanjay K, Haris Nisanthan M., Vikash Kannan B., Linges G.. Fertiledata: Advanced Strategies For Crop Optimization Through Machine Learning Processing. Research & Reviews : Journal of Agricultural Science and Technology. 2025; 14(02):25-35.
How to cite this URL:
Ramaraj S, Sanjay K, Haris Nisanthan M., Vikash Kannan B., Linges G.. Fertiledata: Advanced Strategies For Crop Optimization Through Machine Learning Processing. Research & Reviews : Journal of Agricultural Science and Technology. 2025; 14(02):25-35. Available from: https://journals.stmjournals.com/rrjoast/article=2025/view=230754


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Regular Issue Subscription Original Research
Volume 14
Issue 02
Received 02/04/2025
Accepted 01/05/2025
Published 08/11/2025
Publication Time 220 Days
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