Project Overview

This project focuses on developing an intelligent plant and weed identification system using deep learning techniques. By leveraging YOLO (You Only Look Once) for real-time object detection and PyTorch for model training and inference, the system efficiently classifies and detects different plant species and weeds in agricultural fields. This automation (integrated with machines/robots/drones) aims to assist farmers in precision agriculture by enabling targeted weed removal and optimizing crop management.

Demo

Alt Text

Technologies Used

  • Deep Learning Framework: PyTorch
  • Object Detection Model: YOLO (You Only Look Once) Version V8
  • Dataset: Custom dataset containing labeled images of various plants and weeds
  • Programming Language: Python
  • Additional Libraries: OpenCV, NumPy, Matplotlib, Pandas, Torchvision
  • Training Platform: GPU-enabled environment for accelerated deep learning computations

Features Implemented

  • Real-time Plant and Weed Detection: Utilizes YOLO to perform fast and accurate identification of plant species and weeds.
  • Custom Dataset Training: Trained on a curated dataset with diverse plant and weed samples.
  • Model Optimization: Implements transfer learning and hyperparameter tuning to improve accuracy.
  • Deployment Ready: Can be integrated into agricultural drones, robots, or mobile applications for field usage.
  • Edge Computing Compatibility: Optimized for deployment on low-power edge devices like Google Coral Boards, NXP devices or NVIDIA sold hardware.

Implementation Stages

  1. Dataset Preparation

    • Collected and labeled images of plants and weeds.
    • Augmented dataset to improve model robustness.
    • Split dataset into training, validation, and test sets.

  2. Model Training

    • Used a pre-trained YOLO model and fine-tuned it on the custom dataset.
    • Optimized training using techniques like learning rate scheduling and batch normalization.
    • Evaluated performance using precision, recall, and mAP (mean Average Precision).

  3. Inference and Testing

    • Deployed the trained model on real-world images and video streams.
    • Measured detection accuracy and inference speed.
    • Fine-tuned post-processing to minimize false positives and false negatives.

Results and Performance

  • Achieved high detection accuracy 98% with a precision-recall tradeoff.
  • Real-time inference capable of processing multiple frames per second > 10fps on Intel Nuc or 15fps on NVIDIA hardware.
  • Successfully identified weeds and plants in diverse lighting and background conditions.

I am constantly working on enhancements

  • Expanding Dataset: Collect more diverse plant and weed images to improve generalization.
  • Edge & Scaled Deployment: Optimize the model for embedded systems with low power consuming devices and on the field robots.
  • Integration with Agricultural Equipment: Implement automated weed removal mechanisms.
  • Mobile App Development for other use cases: Create a smartphone application for on-the-go plant and weed identification.

Note: Source code, Datasets or AI models were not released as open source as its proprietory.