final year project_leaf recognition

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leaf recognition project -image processing
it uses neural network to classify the leaf.

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final year project_leaf recognition

  1. 1. Final Year Project By: JIIT,NOIDA Aarshi Taneja (10104666) Divya Gautam(10104673) Nupur(10104676) LEAF RECOGNITION
  2. 2. Leaf  A leaf is an organ of a vascular plant, and is the principal appendage of the vascular plant stem.[1] The leaves and stem together form the shoot.  Typically a leaf is a thin, dorsiventrally flattened organ, borne above ground and specialized for photosynthesis. Most leaves have distinctive upper (adaxial) and lower (abaxial) surfaces that differ in colour, hairiness, the number of stomata and other features.
  3. 3. Types of leaves  According to Petiole According to Shape Of the Blade a. Petiolated (stalked) a.Ellipitic b. Sessible (unstalked) b. Lanceolate  According to the Blade c. Acicular a. Simple Leaf d. Ovate b. Compound Leaf e. Cordate  According to Edge f. Hastate a. Entire g. Linear b. Sinuate According to the Veins c. Dentate a. Parallel Veined d. Serrate b. Pinnate e. Lobed c. Palmate
  4. 4. Problem Statement  We aim to analyze various algorithms for Leaf Recognition and propose and efficient system with optimal accuracy. We aim at producing a user friendly application for Leaf Recognition. The two algorithms implemented are Back propagation Neural Network and Multiscale Distance Matrix, the results are compared and a user friendly application will be developed with the optimal solution.
  5. 5. Multi Scale Distance Matrix  It is an algorithm which takes into account the edge of the leaf for classifying the leaf.  It creates a distance matrix based on the Euclidean Distance between any two points taken by certain order.  Further redundancy is removed by moving it circularly and sorting it.  Thus, for each leaf in the test dataset we compare the matrices of the leaves in the training data set and result the leaf which is similar to its matrix.
  6. 6. Neural Network  Neural networks offer a modeling and forecasting approach that can accommodate circumstances where the existing data has useful information to offer.  neural networks can generate useful forecasts in situations where other techniques would not be able to generate an accurate forecast.  It is used in three different categories a. Forecasting b. Classification c. Statistical Pattern Recognition
  7. 7. Neural Network Diagram Neural Network has following Components a. Input Layer b. Hidden Layer c. Output Layer d. Activation Function • Identity Function • Binary Step Function • Bipolar Step Function • Sigmoid Function • Ramp Function
  8. 8. Image Preprocessing  Colour to Grayscale Conversion gray = 0.2989*R + 0.5870*G + 0.1140*B  Threshold  Binary Conversion  Filtering  Feature extraction a. Area b. Centre of gravity c. Perimeter d. Aspect ratio e. Circularity f. Solidity
  9. 9. Leaf Recognition Web Application
  10. 10. User application  User friendly Leaf recognition Application where user can upload any leaf image and the system will process the image and compare it with leaves in the training data set and output the leaf image that correctly matches the image uploaded by the user.  If the image doesn’t match with any image in the training data set , then the user is notified with a failure message that image cannot be recognized.

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