This project aims to differentiate among various diseases (multiclass prediction) present in mango leaves. Various machine learning techniques were employed in this project to achieve optimal performance in a model capable of predicting multiple classes.

An interactive machine learning project for predicting mango leaf diseases using a Flask web application and a Tkinter-based GUI. Upload an image of a mango leaf to get instant disease classification results.

A Convolutional Neural Network (CNN) models to classify mango leaf disease

Android app for Mango Leaf Disease Detection and Classification

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Detection of mango leaf disease using the Gray Level Co-Occurrence (GLCM) texture feature and Random Forest classification.

The official source code of the paper Mango-Mamba and VN-MangoLeaf: A Lightweight Mamba Model and New Dataset for Mango Leaf Disease Classification

The Code Implementation of MangoLeafNet-XAI: An Attention-Enhanced Deep Learning Architecture for Accurate and Interpretable Mango Leaf Disease Classification

Deep Learning-based system for mango leaf disease detection using Python, OpenCV, and TensorFlow. Uses CNN and MobileNetV2 for image classification and integrates OpenWeatherMap API for weather-based disease risk alerts and treatment suggestions.

Mango Leaf Disease Classification

Mango Leaf Disease Classification

mango leaf disease classification system

Mango Leaf Disease Classification Using CNN

Mango leaf disease classification with CNN

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芒果叶病害识别与分类——基于paa_r101_fpn_ms-3x_coco模型的实现与优化

Deep learning-based mango leaf disease classification using transfer learning and image preprocessing techniques.

Classifiying mango leaf diseases and comparing 3 different pre trained models

A Comparative Study of Supervised and Semi-Supervised Learning for Mango Leaf Disease Classification

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this is an image classification lab assignment

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Mango Leaf Disease Classification with YOLOv8 + ABO + PSO

Deep Learning Algorithms for Mango Leaf Diseases Classification

The mango leaf disease classification model, trained on a dataset with class_names = ['Anthracnose_Fungal_Leaf_Disease_Mango', 'Healthy_Leaf_Mango', 'Rust_Leaf_Disease_Mango'], achieved an accuracy of 95.72%.