Simple Flask web app able to distinguish between real world images of Apples, Bananas, Oranges with predicting weather the fruit in image is Fresh or Rotten with respective probabilities on real-time hosted web service (Heroku)

AI-powered fruit ripeness classification combining computer vision, feature selection, and machine learning algorithms

This project aims to classify different types of fruits using deep learning. The objective is to build a model that can accurately identify the type of fruit based on images.

Image classification : Detecting the fresh and spoiled fruits for the IEEE event.

Using an algorithm that allows it to distinguish fruit images by type of fruit.

🍎 Patter Recognition project for fruit ripeness detection using image processing and 5 different ML algorithms (SVM, Random Forest, Gradient Boosting, Logistic Regression, Ensemble)

Using Alexnet Architecture for Multiclass Fruit Classification

Fruit Classification using CNN

Fresh rotten fruit classification using mobilenetv2 architecture

Project for Computer Vision

Deep learning model (Fruit Classification Model) deployment using Flask and HTML

Computer vision project for classifying fruit ripeness using deep learning

Fruit Classification Model that demonstrate how to perform fruit classification. The model is trained to classify different types of fruits from images.

A web-based Fruit Classifier App built with Streamlit that predicts fruit species based on features like diameter, weight, and RGB color values.

Fruit Ripeness Classifier, ensembling deep learning CNN and shallow learning. Consists of two stage: (1) fruit classifier (2) ripe classifier

A lightweight and efficient computer vision solution for classifying various fruits using MobileNetV2 and transfer learning. Includes robust data preprocessing pipelines, clear repository structure, and detailed documentation for easy collaboration.

This repository contains research from "Unveiling Predictive Factors in Apple Quality," presented at ICEEICT 2024. It covers apple quality classification using ANN (92.87% accuracy) and XAI tools (LIME, SHAP), along with stress prediction in students using LIME-interpretable ML.

Leveraging machine learning for precise fruit classification with advanced preprocessing and algorithm optimization.

Guava Fruit Disease Classification