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  • Machine Learning
  • Deep Learning

AI-Driven Crop Disease Detection and Prediction System

An agricultural intelligence system that recommends crops, fertilizers, and detects plant diseases.

Overview

Machine learning recommends suitable crops from soil nutrient and weather data, suggests fertilizer type and quantity based on nutrient deficiencies, and a deep learning model identifies crop diseases from leaf images while suggesting corrective measures.

Problem Statement

Crop choice, fertilizer dosage and disease response are three decisions a farmer makes largely blind. By the time a disease is visible enough to identify by eye, a significant part of the yield is already lost.

Proposed Solution

Cover the full season in one system — data-driven crop selection at sowing, deficiency-based fertilizer dosing during growth, and image-based disease detection with remedies when symptoms appear.

Key Features

  • Crop Recommendation

    ML model suggests suitable crops from soil nutrients and weather data.

  • Fertilizer Suggestion

    Recommends fertilizer type and quantity based on nutrient deficiencies.

  • Plant Disease Detection

    Identifies crop diseases from leaf images and suggests corrective measures.

Technologies Used

Machine Learning

  • Random Forest
  • Scikit-Learn
  • Pandas

Deep Learning

  • CNN
  • Transfer learning
  • TensorFlow / Keras

Application

  • Flask
  • Weather API
  • OpenCV
  • Bootstrap

Project Workflow

  1. 1

    Soil & weather input

    Nutrient levels and local weather data are entered.

  2. 2

    Crop recommendation

    The model suggests suitable crops for the conditions.

  3. 3

    Fertilizer dosing

    Type and quantity are computed from deficiencies.

  4. 4

    Disease detection

    A leaf image is classified and remedies suggested.

What You'll Receive

  • Complete, runnable source code with folder structure
  • Project report and technical documentation
  • Ready-to-present PPT content
  • Setup and installation walkthrough
  • Line-by-line project explanation session
  • Viva question bank with answers
  • Bug fixing and troubleshooting help
  • Post-delivery support after submission

Frequently Asked Questions

How much does this project cost?

Every project is quoted individually, because the price depends on the modules you need, your technology stack, your college's format and your deadline. Send us the project name on WhatsApp and we'll share a quote the same day.

Can the project be customised to my college requirements?

Yes. Share your guide's requirements, preferred technology stack and abstract format, and we adapt the modules, dataset or UI accordingly.

Will I be able to explain this project during my viva?

That is the point of the explanation session. We walk you through the architecture, every module, the flow of data and the results, and hand over a viva question bank with answers.

What if the project does not run on my laptop?

We help you with setup end to end — dependencies, environment, database and configuration — over chat or a call until it runs on your machine.

Do I get support after submission?

Yes. Post-delivery support is included, so you can come back for fixes, doubts or demo help even after the project is delivered.

Want This Project?

Get complete source code, documentation, PPT, explanation, and support.

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