Prediction of Herbs with its Benefits using Deep Learning Techniques

Abstract

This project focuses on using machine learning to identify plants, especially medicinal herbs, by analyzing leaf images. It is important because herbal remedies are often cheaper and have fewer side effects than modern medicine. The study reviews techniques for processing leaf images, extracting key features like shape and texture, and using machine learning models to classify the plants accurately. The system tested has shown improved accuracy in identifying plants, which could be useful in real-time applications for herbal medicine identification

Introduction

I. Introduction

• Image processing is a technique used to analyze and manipulate visual data (images/videos) using signal processing and mathematical techniques.

• The study intersects with computer vision and digital photography, emphasizing the identification of image content using automated systems.

• One growing application is in agriculture and botany—specifically, the automatic identification of medicinal plants.

II. Related Work

• Prior research has focused on:

  • Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs) for plant classification.

  • Using transfer learning and data augmentation to improve performance.

  • Proposals for real-time detection systems and novel classification models.

  • Use of leaf vein patterns and high-resolution imagery to enhance accuracy.

• Limitations noted include poor handling of complex backgrounds and lack of real-time adaptability in some systems.

III. Proposed Methodology

The system uses deep learning and image processing for automatic detection and classification of medicinal plants via leaf images.

Modules:

1. Image Acquisition

   • Focuses on collecting images of herbs from various environments.

   • Aims to support common users (e.g., farmers, students) without botanical expertise.

2. Preprocessing

   • Converts RGB images to grayscale for simplified processing.

   • Enhances image quality using filters for better feature extraction.

3. Segmentation

   • Applies contour models and polygon-based shape definitions to accurately isolate leaves from the background.

   • Uses contrast mapping to refine leaf edges.

4. Classification

   • Utilizes CNN algorithms to identify plant species based on leaf characteristics.

   • CNNs are chosen for their efficiency in visual pattern recognition and classification tasks.

5. Usage Details

   • Once identified, the system provides the user with medicinal uses, plant part references, and availability.

   • Useful in fields like Ayurveda, pharmaceuticals, and agriculture.

IV. System Architecture

• Integrates camera-based image capture, processing pipeline (segmentation, CNN classification), and a user interface to display plant information.

• Designed to reduce manual effort and enhance plant identification speed and accuracy.

Conclusion

Traditional medicine methods continue to be widely used in many cases. Population growth, drug shortages, illicit medical costs, side effects of many synthetic drugs, and improved resistance to drugs currently used for infectious diseases has led to increased use of plants as a source of various medicines for human diseases. The project proposed CNN-based methods to obtain Indian leaf varieties. Testing is done with pre -learn and edge detection. CNN is tested with softmax and sigmoid layer. The results confirm that with good edge detection and pre-training, the CNN binary with sigmoid can detect leaf type. The improvement project provides the best and easiest way to separate good trees. The medicinal use of the plant and the great demand for the plant have made it possible to achieve a diversity of plant species.

References

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Copyright

Copyright © 2025 Siddhesh Deshmukh, Tushar Holkar, Mahesh Jagtap, Amruta Ghadge, Prof. Ulka Bansode. 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.