International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 03 | Mar 2023
p-ISSN: 2395-0072
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Flower Species Classification Using ML Prof. Meghana Deshmukh1, Anushri Rahate2, Gauri Raut3, Krutika Varhade4, Sahil Shirbhate5 1Professor, Dept. of CSE Engineering, PRMIT&R college, Maharashtra, India 2Student, Dept. of CSE Engineering, PRMIT&R college, Maharashtra, India 3Student, Dept. of CSE Engineering, PRMIT&R college, Maharashtra, India 4Student, Dept. of CSE Engineering, PRMIT&R college, Maharashtra, India 5Student, Dept. of CSE Engineering, PRMIT&R college, Maharashtra, India
---------------------------------------------------------------------***-------------------------------------------------------------------accurate flower species classification system holds immense Abstract - - Flower species classification is a fundamental value. Leveraging advancements in machine learning and computer vision, this project aims to develop a robust model capable of classifying flower species from images of their petals and leaves. The challenge lies in addressing the high variability in flower appearances, differentiating between closely related species, and creating a system that can generalize effectively. By solving this problem, we can empower botanists, educators, and conservationists with a tool that enhances botanical research, contributes to biodiversity assessments, and accelerates ecological conservation efforts. Machine Learning is program that learns from past data set to perform better with experience. It is tools and technology that we can utilize to answer questions with our data. Machine Learning works on two values these are discrete and continuous. The use and applications of Machine Learning has wide area like Weather forecast, Spam detection, Biometric attendance, Computer vision, Pattern recognition, Sentiment Analysis, Detection of diseases in human body and many more. The learning methods of Machine Learning are of three types these are supervised, unsupervised learning. Supervised learning contains instances of a training data set which is composed of different input attributes and an expected output. Classification which is the sub part of supervised learning where the computer program learns from the input given to it and uses this learning to classify new observation. There are various types of classification techniques; these are Decision Trees, Bayes Classifier, Nearest Neighbor, Support Vector Machine, Neural Networks and many more. Some example of Classification tasks are Classifying the credit card transactions as legitimate or fraudulent, classifying secondary structures of protein as alpha-helix, beta-sheet or random coil and categorize the news stories as finance, weather, entertainment and sports. Python is a programming language created by Guido van Rossum in 1989. Python is interpreted, object-oriented, dynamic data type of high-level programming language. The programming language style is simple, easy to implement and elegant in nature. Python language consists of powerful libraries. Moreover, Python can easily combine with other programming languages, such as C or C++ or Java. ScikitLearn use the scipy library of python as a toolkit. Scikit learn was originally called as "Scikit learn". It includes dataset
task in botany with numerous applications in biodiversity preservation, horticulture, and ecological studies. This research paper intro- duces a novel approach to tackle this challenge by leveraging the power of machine learning, specifically Convolutional Neural Networks (CNNs), withinthe domain of computer science. The problem addressed in this study pertains to the accurate identification and categorization of diverse flower species based on images of their petals, leaves, and overall morphology. The methodology employed involves a comprehensivedataset of high-resolution flower images collected from various sources. Preprocessing techniques are applied to standardize the dataset and improve the model’s performance. Our machine learning model is designed and trained to classify flower species with high precision and accuracy. The key findings of this research paper include the successful development of a flower species classification model, achieving remarkable accuracy in distinguishing between a wide range of flower species. The experimentation phase reveals the potential of deep learning, specifically CNNs, in automating the flower species identification process. We also highlight the model’s adaptability to a variety of flower species, making it a versatile tool for botanists and horticulturists. Key Words: Machine learning, k-Nearest Neighbors (KNN), Convolutional Neural Networks (CNN), Support Vector Machine(SVM).
1. INTRODUCTION Flowers, with their breathtaking diversity of colors and shapes, have fascinated botanists and enthusiasts alike for centuries. The intricate task of classifying flower species based on their unique characteristics is not only a pursuit of scientific curiosity but also plays a pivotal role in the realms of bio diver-sity conservation, horticulture, and ecological research. In the age of advanced computing and artificial intelligence, the field of computer science presents a new frontier for addressing this taxonomic challenge. The manual identification of flower species based on visual characteristics is a time-intensive and error-prone process, requiring specialized botanical knowledge. In the context of a world facing ecological challenges, an automated and
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