Automatic Recognition of Plant Types with ML

Abstract

Advances in machine learning and mobile technology have made it feasible to identify new plant species automatically in recent years. This work focuses on an Android application that use visual analysis and machine learning approaches to reliably identify plant species. The objective is to provide a basic tool that enables real- time plant identification on mobile devices for users such as botanists, horticulturists, and nature enthusiasts. For plant enthusiasts, professionals, and specialists, the suggested Android- based plant species distinguishing proof framework provides a useful and open tool. This approach makes advantage of machine learnings and mobile technology to help with plant identification, conservation, and ecological study. Additional improvements and optimizations can be looked at to increase the application\'s accuracy, speed, and usability.

Introduction

Plants are crucial to human life, providing food, medicine, and oxygen. However, identifying plant species can be challenging for non-experts. Traditional methods like field guides are slow and require expertise.

This project proposes an Android-based application that leverages machine learning (ML) and image recognition to identify plant species in real time, using photos taken by a smartphone. The system utilizes Convolutional Neural Networks (CNNs) trained on diverse images of leaves, flowers, and fruits.

II. Literature Review Highlights

Research and developments in plant identification using AI include:

1. SVM & KNN Classifiers: Used for leaf image recognition.

2. CNN Models: Shown effective in deep learning-based plant recognition.

3. Mobile Applications: Integrated ML with image processing for portable plant ID tools.

4. TensorFlow & MobileNet: Lightweight CNNs optimized for mobile devices.

5. Leafsnap: Computer vision system using supervised learning for plant recognition.

6. Review Studies: Compared performance of various ML techniques like Random Forest, Naive Bayes, and traditional ML.

III. Methodology

A. Data Collection

• Collect diverse images of plant parts (leaves, flowers, fruits).

• Dataset should represent various species.

B. Data Preparation

• Resize and standardize images.

• Apply augmentation: flipping, rotation, brightness changes.

C. Feature Extraction

Techniques include:

• Texture features (e.g., Gabor filters, LBP)

• Shape features (e.g., contours, symmetry)

• Color histograms

• Evaluation of each method’s discriminative power is necessary.

D. Testing and Feedback

• Test across various Android devices.

• Collect user feedback and iterate based on usability and bugs.

IV. Modeling and System Architecture

A. Preprocessing Techniques

• Resizing and normalization of images.

• Noise removal (e.g., Gaussian blur).

• Contrast enhancement, cropping, and illumination correction.

B. Feature Extraction Methods

• CNNs for deep feature extraction.

• Local Binary Patterns (LBP) for texture.

• Histogram of Oriented Gradients (HOG) for shape detection.

• Color histograms in RGB/HSV color spaces.

• Principal Component Analysis (PCA) for dimensionality reduction.

C. CNN Architecture

• Input Layer: Accepts standardized images.

• Convolutional Layers: Extract spatial features using filters.

• Activation Functions: ReLU, Sigmoid, or Tanh for non-linearity.

• Additional layers may include pooling and fully connected layers depending on the final model design.

V. Results and Performance Evaluation

• The model was trained using TensorFlow Lite, optimized for mobile deployment.

• The final system supports real-time plant recognition on Android.

• Evaluation Metrics:

  • Accuracy: 95%

  • Precision, Recall, F1-score: All show strong performance

• Dataset used: Included 9 distinct plant species; 25.8% of features were filtered out as non-informative before training.

• The model successfully identifies plant species based on features like leaf texture, color, and shape.

Key Contributions

• A user-friendly mobile application for real-time, offline plant identification.

• High-performing ML model (CNN-based) achieving 95% accuracy.

• Utilizes lightweight models (TensorFlow Lite) suitable for low-memory devices.

• Helps non-experts, hobbyists, and students easily identify plant species.

Conclusion

The creation of an Android application that can distinguish between several leaf species is covered in this study. A collection of leaf descriptors—specific traits taken from leaf photos— are the foundation of the program. Experiments on different leaf datasets have produced positive outcomes for these descriptors. The main feature of this Its excellent accuracy in recognizing plant species from photos of their leaves is an application. The capacity of the application to correctly identify the plant species and produce accurate output is referred to as accuracy. Experts in botany or those with extensive plant knowledge are the primary benefits of this application. By merely examining leaf photos, This instrument is available to them. to get precise findings and swiftly identify plant species.

References

[1] \"Plant Identification System Based on Leaf Image Recognition Using Machine Learning Techniques\" by N. Jayasudha and R. Shobana (2019) [2] \"Identifying Plants Through Deep Learning and Convolutional Neural Networks\" by R. Thanh, N. Hai, and N. Nhu (2020) [3] \"Automatic plant identification System using Mobile Application\" by N. Masruroh, M. A. Wibowo, and D. Lestari (2020) [4] \"Plant Identification Using Mobile Net and TensorFlow for Android\" by A. T. Kurniawan and A. P. Nugroho (2020) [5] B. J. Stachelek\'s 2018 paper \"Deep Plant: Plant Identification with Convolutional Neural Networks\" [6] \"Leaf snap: An Automated Plant Species Computer Vision System Identification\" by N. Kumar et al. (2012) [7] \"Plant Species Deep Learning- Based Identification: A Review\" by D. Singh and N. Srivastava (2020) [8] \"Machine Learning Techniques for Identification of Plant Species from Leaves Images: A Comprehensive Review\" by S. Jadhav, M. Patil, and S. Bodhe (2020)

Copyright

Copyright © 2025 Miss. Mounika, Mrs. Sharvani V. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.