lustering in Machine Learning Introduction to Clustering It is basically a type of unsupervised learning method . An unsupervised learning method is a method in which we draw references from datasets consisting of input data without labelled responses. Generally, it is used as a process to find meaningful structure, explanatory underlying processes, generative features, and groupings inherent in a set of examples. Clustering is the task of dividing the population or data points into a number of groups such that data points in the same groups are more similar to other data points in the same group and dissimilar to the data points in other groups. It is basically a collection of objects on the basis of similarity and dissimilarity between them. For ex– The data points in the graph below clustered together can be classified into one single group. We can distinguish the clusters, and we can identify that there are 3 clusters in the below picture. It is not necessary for clusters to be a spherical. Such as : DBSCAN: Density-based Spatial Clustering of Applications with Noise These data points are clustered by using the basic concept that the data point lies within the given constraint from the cluster centre. Various distance methods and techniques are used for calculation of the outliers. Why Clustering ? Clustering is very much important as it determines the intrinsic grouping among the unlabeled data present. There are no criteria for a good clustering. It depends on the user, what is the criteria they may use which satisfy their need. For instance, we could be interested in finding representatives for homogeneous groups (data reduction), in finding “natural clusters” and describe their unknown properties (“natural” data types), in finding useful and suitable groupings (“useful” data classes) or in finding unusual data objects (outlier detection). This algorithm must make some assumptions which constitute the similarity of points and each assumption make different and equally valid clusters. Clustering Methods : Density-Based Methods : These methods consider the clusters as the dense region having some similarity and different from the lower dense region of the space. These methods have good accuracy and ability to merge two clusters.Example DBSCAN (Density-Based Spatial Clustering of Applications with Noise) , OPTICS (Ordering Points to Identify Clustering Structure) etc. Hierarchical Based Methods : The clusters formed in this method forms a tree-type structure based on the hierarchy. New clusters are formed using the previously formed one. It is divided into two category Agglomerative (bottom up approach) Divisive (top down approach) examples CURE (Clustering Using Representatives), BIRCH (Balanced Iterative Reducing Clustering and using Hierarchies) etc. Partitioning Methods : These methods partition the objects into k clusters and each partition forms one cluster. This method is used to optimize an objective criterion similarity function such as when the distance is a major parameter example K-means, CLARANS (Clustering Large Applications based upon Randomized Search) etc. Grid-based Methods : In this method the data space is formulated into a finite number of cells that form a grid-like structure. All the clustering operation done on these grids are fast and independent of the number of data objects example STING (Statistical Information Grid), wave cluster, CLIQUE (CLustering In Quest) etc. Clustering Algorithms : K-means clustering algorithm – It is the simplest unsupervised learning algorithm that solves clustering problem.K-means algorithm partition n observations into k clusters where each observation belongs to the cluster with the nearest mean serving as a prototype of the cluster . Applications of Clustering in different fields Marketing : It can be used to characterize & discover customer segments for marketing purposes. Biology : It can be used for classification among different species of plants and animals. Libraries : It is used in clustering different books on the basis of topics and information. Insurance : It is used to acknowledge the customers, their policies and identifying the frauds. City Planning: It is used to make groups of houses and to study their values based on their geographical locations and other factors present. Earthquake studies: By learning the earthquake-affected areas we can determine the dangerous zones. References : Wiki Hierarchical clustering Ijarcs matteucc analyticsvidhya knowm

Nowadays, computerized plant species classification systems are used to help the people in the detection of the various species. However, the automated analysis of plant species is challenging as compared to human interpretation. This research as been provided in this field for the better classification of plant species. Even now, these methodologies lack an exact classification of the plant species. The challenge is due to the inappropriate classification algorithm. In Particular, when we consider the medicinal plant species recognition, the accuracy will be the main criteria. In this research, the suggested system implements the deep learning technique to obtain high accuracy in the classification process using computer prediction methods.The Convolutional Neural Network (CNN) is employed beside transfer learning for deep learning of medicinal plant images. This research work has been carried out on the flower images dataset of four Canadian medical plants; namely, Clubmoss, Dandelion, Lobelia, and Bloodroot, which is fed as the training dataset for the CNN and machine learning-based proposed system. Finally, an accuracy of 96% has been achieved in classification of the medicinal plant species.

PyTorch webinar on using DINOv2 and Faiss for plant species classification

Plants Species Explorer is an interactive web app that allows users to search and explore thousands of plant species, providing detailed scientific information, images, and classification data. The app integrates free open-data APIs for accurate, real-time access to a vast database of plants.

In this project we aimed to classify species of plants, which are divided into categories according to the species of the plant to which they belong. Being a classification problem, given an image, the goal is to predict the correct class label.

3-stage approach using transformer-based image encoders (Clip-Vit) for plant species classification, tackling long-tail issues with parameter specialization & residual fusion. On Herbarium 2021/2022 datasets in few/medium/many-shot settings, advancing biodiversity monitoring.

A mobile and ML project for plant analysis, including disease detection, species classification, and leaf/age analysis. Provides Python backend for model training and inference, and a React Native mobile app for on-device usage.

Edge-Aided Plant Species Identification Using Leaf Image Analysis and Hybrid Classification Techniques

This tutorial is a primer on image data related machine learning. You will learn the basics of image data preprocessing & data augmentation. Then you will build classification models to classify plant species using traditional ML algorithm, CNN-based and pre-trained models.

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🥇 Official repository for the 1st Place solution in the AI of GOD 4.0 Kaggle Competition. Implements a 3-stage CNN-Vision Transformer ensemble for 9-species plant classification, designed to handle limited and imbalanced datasets with local-global feature fusion.

"FloraVision" is a plant image classification app hosted on Google Cloud using Docker, Flask, and Kubernetes. It identifies plant species from images using machine learning models.

This is a research project that focus on identifying plants by image processing and uses a classification system to identify plant species from an image of their acquired plant part: leaf.

This repository contains the implementation and experimental results of our research on utilizing Vision Transformer (ViT) models for fine-grained classification of plant species.

In this project, our main aim is to create a Medicinal plant identification system using Deep Learning concept. This project will classify the medicinal plant species with high accuracy. Identification and classification of medicinal plants are essential for better treatment.

The Leaf Classification app uses machine learning to identify various leaf types from uploaded images. It provides quick and accurate predictions of plant species, aiding botanists and enthusiasts in their identification efforts and promoting biodiversity awareness.

Classifying the iris plants data into 3 different species.

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Here's the model written in Python using TensorFlow, where I employ Convolutional Neural Networks and deep learning techniques for the task of plant classification

GUI-based plant species classification using machine learning models and image processing for leaf images.

Image classification of plant seedlings having 12 species of plants

Using Fuzzy Logic

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thesis

My machine learning pet project on performing image classification of plant species using different benchmark classification techniques

Plant species classification is done by the transferred VGG-16 pre-trained CNN model

The dataset comprises of images from 12 plant species. Source: https://www.kaggle.com/c/ plant-seedlings-classification/data

Multi classification of plant diseases based on cut leaf images. There are 8 plant species 26 classes including healthy leafs.

Deep Learning-powered plan classification system, identifies plant species from images using advanced computer vision techniques.

Scientific study of the classification and naming of various plant species using Deep Learning and Web Development