IBMRD's Journal of Management & Research

Manisha Amlekar ¹, Ramesh R. Manza ², Pravin Yannawar ³, Ashok T. Gaikwad ¹

Affiliations
1 Institute of Management Studies and IT, Vivekanand College Campus, Aurangabad, Maharashtra, IN
2 Department of CSIT Dr.BAMU, Auranagabad, Maharashtra, IN
3 Department of CSIT Dr.BAMU, Auranagabad, Maharashtra

Abstract

This paper presents the classification of plant leaf images with biometric features. Traditionally, the trained taxonomic perform this process by following various tasks. The taxonomic usually classify the plants based on flowering and associative phenomenon. It was found that this process was time consuming and difficult. The biometric features of plants leaf like venation make this classification easy. Leaf biometric feature are analyzed using computer based method like morphological feature analysis and artificial neural network based classifier. ANN model take input as the leaf venation morphological feature and classify them into four different species. The result of this classification based on leaf venation is achieved 96.53% accuracy in the training of the model for classification of leaves provide the 91% accuracy in testing to classify the leaf images.

Keywords

Artificial Neural Network, Canny Edge Detection, K-nn Classification, Leaf Venation Pattern, Morphological Features

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Narayan P. Bhosale ¹, Ramesh R. Manza ¹

Affiliations
1 Dept of Computer Science and IT, Dr.B.A.M.U, Aurangabad, IN

Abstract

This paper addresses the issues of noise which involve pre-processing task before using images for various applications. We have applied various filters on remote sensing images for denoising them. The fundamental key challenge to noise reduction is to reduce or eliminate the noise without failing other aspects of the image. Image denoising involves the manipulation of the image data to produce a visually high quality image. There are many kinds of noise has affect remote sensing images but we have selected only Poisson noise and interpret the results thus, we took remote sensing images that denoising them with a Average filter, Median filter, Unsharp filter and Wiener Filter and used statistical quality measured in this work.

Keywords

Filter, Image, Poisson Noise, Remote Sensing Images

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Manisha Amlekar ¹, Ramesh R. Manza ², Pravin Yannawar ², Ashok T. Gaikwad ¹

Affiliations
1 Institute of Management Studies and IT, Aurangabad, IN
2 Department of CSIT, Dr.Babasaheb Ambedkar Marathwada University, Auranagabad, IN

Abstract

Plants are very much significant component of ecosystem. Plants can be used as foodstuff, in medicines and in many industries for manufacturing various products. Identifying those helps ensure the protection and survival of all natural life. Plant identification can be performed using many different techniques. As plant leaves are more readily available, it is efficient to identify and classify plants by using their leaves. Plant classification by using leaves requires different biometric features. This paper presents the review on various methods for plant classification based on leaf biometric features. For plant classification traditionally, the trained taxonomist and botanist had required to perform set of various tasks. The taxonomist usually classifies the plants based on flowering and associative phenomenon. It was found that this process was time consuming and difficult for following various tasks. The biometric features of plants leaf such as shape and venation make this classification easy. This review study may help the rural people for easily identifying in addition to classifying the plant based on the leaf features. This plant classification method include two basic tasks leaf biometric feature extraction and classification of plants based on these features.

Keywords

Artificial Neural Network, Leaf Shape, Leaf Venation, Morphological Features, Probabilistic Neural Network.

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