International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 08 | Aug 2023
p-ISSN: 2395-0072
www.irjet.net
Automatic Recognition of Medicinal Plants using Machine Learning Techniques S. Vinothini1, J. Agnes2 1Student, M.E, Dept. of Computer Science and Engineering, T.J.S Engineering College, Tamil Nadu, India
2Assistant Professor, Dept. of Computer Science and Engineering, T.J.S Engineering College, Tamil Nadu, India
---------------------------------------------------------------------***--------------------------------------------------------------------2. EXISTING SYSTEM Abstract - A fully automated method for the recognition of medicinal plants using computer vision and machine learning techniques has been presented. Leaves from 24 different medicinal plant species were collected and photographed using a smart phone in a laboratory setting. A large number of features were extracted from each leaf such as its length, width, perimeter, and area, number of vertices, color, perimeter and area of hull. Several derived features were then computed from these attributes. The best results were obtained from a random forest classifier using a 10-fold cross-validation technique. With an accuracy of 90.1%, the random forest classifier performed better than other machine learning approaches such as the k-nearest neighbor, naïve Bayes, support vector machines and neural networks. Key Words: Medicinal Plants, classifier cross-validation, leaves
attributes,
This research proposed a new mobile application based on Android operating system for identifying Indonesian medicinal plant images based on texture and color features of digital leaf images. In the experiments we used 51 species of Indonesian medicinal plants and each species consists of 48 images, so the total images used in this research are 2,448 images. This research investigates the effectiveness of the fusion between the Fuzzy Local Binary Pattern (FLBP) and the Fuzzy Color Histogram (FCH) in order to identify medicinal plants. The FLBP method is used for extracting leaf image texture. The FCH method is used for extracting leaf image color. The fusion of FLBP and FCH is done by using the Product Decision Rules (PDR) method. This research used Probabilistic Neural Network (PNN) classifiers for classifying medicinal plant species. The experimental results show that the fusion between FLBP and FCH can improve the average accuracy of medicinal plants identification. The accuracy of identification using fusion of FLBP and FCH is 74.51%. This application is very important to help people identify and find information about Indonesian medicinal plants.
features,
1. INTRODUCTION Identifying unknown plants relies much on the inherent knowledge of an expert botanist. The most successful method to identify plants correctly and easily is a manualbased method based on morphological characteristics. Thus many of the processes involved in classifying these plant species are dependent on knowledge accumulation and skills of human beings. However, this process of manual recognition is often laborious and timeconsuming. Hence many researchers have conducted studies to support the automatic classification of plants based on their physical characteristics. Systems developed so far use a varying number of steps to automate the process of automatic classification, though the processes are quite similar. Essentially, these steps involve preparing the leaves collected, undertaking some pre-processing to identify their specific attributes, classification of the leaves, populating the database, training for recognition and finally evaluating the results. Although leaves are most commonly used for plant identification, the stem, flowers, petals, seeds and even the whole plant can be used in an automated process. An automated plant identification system can be used by non-botanical experts to quickly identify plant species quite effortlessly.
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3. PROPOSED WORK The proposed technique was tested on a dataset of 55 medicinal plants from Vietnam and a very high accuracy of 98.3% was obtained with a support vector machines (SVM) classifier. The size of each image was 256*256 pixels. Proposed an approach based on fractal dimension features based on leaf shape and vein patterns for the recognition and classification of plant leaves. Using a knearest neighbor classifier with 20 features, they were able to achieve a high recognition rate of 87.1%. Using a volumetric fractal dimension approach to generate a texture signature for a leaf and the GLCM (Gray level co occurrence matrix) algorithm.
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