Drone-Based Crop Image Acquisition and Targeted Pesticide Spraying Final Year Project with Source Code | Source Code
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Drone-Based Crop Image Acquisition and Targeted Pesticide Spraying Final Year Project with Source Code

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Project Overview

Description, tech stack, and what is included

Full source Frontend + backend
Database .sql file
Setup guide README included

Drone-Based Crop Image Acquisition and Targeted Pesticide Spraying is a Python Flask based final year project developed for smart agriculture, crop image processing, crop-type identification, and pesticide spray target documentation. This major project includes an operator user portal and administrator portal. Operators can sign up, log in, manage profile, register fields and parcels, upload drone crop images, assign images to parcels, run image processing pipeline, view classification results, draw spray target rectangles, download PDF reports, train a Random Forest model using labelled crop samples, test models, and view activity logs. Admins can manage users, field records, drone images, master datasets, machine learning models, classification results, spray records, system reports, backups, restore operations, and admin password settings. This drone-based crop image acquisition source code is suitable for students who need a final year project, major project, minor project, source code, and project report based on Python, Flask, OpenCV, machine learning, and smart farming.

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Project
Drone-Based Crop Image Acquisition and Targeted Pesticide Spraying Final Year Project with Source Code
Stack
MACHINE-LEARNING
Includes
Code, DB, README
License
Academic submission
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Admin Features

Modules and controls available to administrators

  • Admin login and logout in this final year project
  • Admin dashboard with KPIs
  • View total users
  • View operator count
  • View parcel count
  • View image count
  • View completed and error jobs
  • View verified results
  • View spray map count
  • View dataset row count
  • View ready model count
  • View recent users
  • View recent jobs
  • View recent platform events
  • User management
  • Search users by ID, username, or email
  • Create operator account with initial password
  • View user details
  • View user stats
  • View recent images
  • View user activity
  • Edit operator account
  • Validate email and username uniqueness
  • Reset operator password
  • Activate/block users
  • Delete users with cascading cleanup
  • Prevent deletion of own admin account
  • View global activity log
  • Field record management
  • List field records with owner and image counts
  • Create parcel for selected operator
  • View field/parcel detail
  • Edit field/parcel details
  • Reassign owner
  • Update linked image ownership during reassignment
  • Delete field/parcel and unlink images
  • All images management
  • View all uploaded crop images
  • View image detail
  • Edit parcel link
  • Run processing pipeline
  • Delete image with full artifact cleanup
  • Master dataset management
  • View class distribution summary
  • Bulk upload dataset samples
  • Update class label for dataset row
  • Delete dataset sample and file
  • Open stored dataset file
  • Model management
  • List trained models
  • Train/retrain model on entire dataset
  • View model detail
  • View model accuracy
  • View model loss
  • View model classes
  • View model file path
  • View trainer information
  • Delete model file and database record
  • Classification result management
  • List classification results
  • View classification result detail
  • View full processing job
  • View images, features, and spray data
  • Verify result
  • Re-run pipeline
  • Delete job and artifacts
  • Spray record management
  • List spray records
  • View spray record detail
  • View spray regions and notes
  • View overlay map
  • Edit spray notes
  • Delete spray records
  • Reports management
  • Filter activity by date range
  • Download system PDF report
  • System and backup management
  • View entity counts
  • View SQLite path and database size
  • Create backup ZIP
  • Restore system from backup ZIP
  • List recent backups
  • Download recent backups
  • Change admin password

User Features

What end users can do in this application

  • Operator registration in this final year project
  • Operator login and logout
  • Forgot password demo flow
  • Change password with current password validation
  • View and update profile
  • Update full name, phone, and address
  • View read-only username, email, and account status
  • View personal activity log
  • Register field and parcel records
  • Edit parcel details
  • Delete parcel records
  • View parcel details with linked images and job counts
  • Upload multiple drone crop images
  • Validate image file formats
  • Assign parcel during upload
  • Assign or update parcel link later
  • View drone image details
  • View image dimensions, file size, storage path, and processing history
  • Run image processing pipeline
  • Test trained model on uploaded image
  • Delete drone image with related jobs and spray data cleanup
  • View processing history
  • Delete processing job and related processed artifacts
  • View crop classification results
  • View preprocessed image preview
  • View segmented image preview
  • View predicted crop type
  • View confidence score
  • View extracted feature table
  • View linked spray maps
  • Download PDF result report
  • Open print view
  • Draw spray target rectangles on segmented image
  • Save spray overlay map
  • Add spray target notes
  • View spray target list
  • View spray target details
  • Edit spray target notes
  • Delete spray target records
  • Upload labelled dataset samples by crop class
  • Train Random Forest model on uploaded samples
  • View training status history
  • Test model on uploaded image
  • Authenticated access to own uploads and processed outputs

Other Features

Additional capabilities included in the project

Machine Learning Features in this Final Year Project

  • Labelled dataset sample upload
  • Crop class label management
  • Random Forest model training
  • Training status history
  • Model testing on uploaded image
  • Full dataset retraining from admin panel
  • Model accuracy tracking
  • Model loss tracking
  • Model class tracking
  • Model file storage using .joblib
  • joblib model loading
  • scikit-learn based classification
  • Optional MATLAB bridge stub for custom training integration
  • Crop-type prediction with confidence score
  • Master dataset class distribution summary

Spray Target Features in this Final Year Project

  • Draw spray target rectangles
  • Save spray regions
  • Save overlay spray map
  • Add spray notes
  • View spray target details
  • Edit spray notes
  • Delete spray target records
  • Link spray maps with classification results
  • Document targeted pesticide zones
  • Support precision agriculture workflow

Reports and Backup Features in this Final Year Project

  • PDF classification result report
  • Print-friendly result view
  • System PDF report
  • Date-range activity filtering
  • ReportLab PDF generation
  • SQLite database backup
  • ZIP backup creation
  • Backup includes database and runtime folders
  • Restore from backup ZIP
  • Backup download support
  • Instance-backed upload, dataset, model, and processed file backup

Other Features in this Final Year Project

  • Complete drone-based crop image acquisition source code
  • Suitable for final year project, major project, and minor project
  • Project report content can be prepared from included modules
  • Operator Portal and Admin Portal included
  • Field and parcel management
  • Drone crop image management
  • Crop image preprocessing
  • Vegetation segmentation
  • Feature extraction
  • Crop-type identification
  • Targeted pesticide spraying documentation
  • Spray map generation
  • ML dataset management
  • Random Forest model training
  • Model testing
  • PDF reports
  • Backup and restore
  • SQLite database
  • Flask-SQLAlchemy ORM
  • Flask-Login authentication
  • OpenCV image processing
  • Pillow image handling
  • scikit-image feature support
  • scikit-learn machine learning
  • joblib model persistence
  • ReportLab PDF generation
  • Optional MATLAB bridge stub
  • Seed data support
  • Synthetic training data support
  • Useful for viva, source code review, project report writing, and project demonstration

How to Run

Step-by-step setup on your laptop or PC

  1. Open terminal in the project root folder that contains run.py.

  2. Install dependencies:

    
 
    pip install -r requirements.txt

  3. Optional: load demo data:

    
 
    python seed_data.py --force

  4. Start the server:

    
 
    python run.py

  5. Open public landing page:

    
 
    http://127.0.0.1:5000/

  6. Open about page:

    
 
    http://127.0.0.1:5000/about

  7. Open how-it-works page:

    
 
    http://127.0.0.1:5000/how-it-works

  8. Open operator login:

    
 
    http://127.0.0.1:5000/auth/login

  9. Open operator dashboard after login:

    
 
    http://127.0.0.1:5000/app/
  10. Open admin login:

 
http://127.0.0.1:5000/admin/login
  1. Open admin dashboard after login:

 
http://127.0.0.1:5000/admin/

Login Credentials

Default demo accounts for testing after setup

Administrator Account

  • Username: admin
  • Password: Admin@123

Sample Operator Account

  • Username: harper.singh
  • Password: User@123

License

Usage terms for academic and personal projects

Related Tags

Search terms and categories for this source code

Drone-Based Crop Image Acquisition and Targeted Pesticide Spraying Final Year Project with Source Code Source Code Final Year MACHINE-LEARNING Project Ready-to-Run Code With Database File Plagiarism-Free Faculty Approved final year project major project minor project source code project report drone-based crop image acquisition targeted pesticide spraying crop-type identification project crop image processing project agriculture machine learning project smart farming project precision agriculture project Python Flask agriculture project OpenCV crop detection crop classification source code pesticide spraying project drone agriculture project
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