Popular GitHub Repositories for Plant Disease

QiZhenGPT: An Open Source Chinese Medical Large Language Model｜一个开源的中文医疗大语言模型

Using an LLM to discover the genetic causes of rare disease

⚕️GenAI powered multi-agentic medical diagnostics and healthcare research assistance chatbot. ? Designed for healthcare professionals, researchers and patients.

It is a medical chatbot that will provide quick answers to FAQs by setting up rule-based keyword chatbots.

Automatic detection of plant diseases

Chronic Disease Prediction Using Medical Notes

Code for "Graph Neural Network on Electronic Health Records for Predicting Alzheimer’s Disease"

[CoRL'22] PlanT: Explainable Planning Transformers via Object-Level Representations

Aliro: AI-Driven Data Science

Alzheimer's Disease Prediction by using ResNet, AlexNet

Plant Disease Identification Using Convulutional Neural Network

A sophisticated smart symptom search engine

Resources for the paper titled "EEG-GCNN: Augmenting Electroencephalogram-based Neurological Disease Diagnosis using a Domain-guided Graph Convolutional Neural Network". Accepted for publication (with an oral spotlight!) at ML4H Workshop, NeurIPS 2020.

? Visualizing Convolutional Networks for MRI-based Diagnosis of Alzheimer’s Disease

Performing Leaf Image classification for Recognition of Plant Diseases using various types of CNN Architecture, For detection of Diseased Leaf and thus helping the increase in crop yield.

A Machine Learning and Deep Learning based webapp used to predict multiple diseases.

Spatial Pyramid Pooling on top of AlexNet using tensorflow. ***New updates for SPPnet in Pytorch**

AI powered plant disease detection and assistance platform currently available as an App and API.

Ssebowa is an open-source generative AI platform offering free access to powerful AI models for everyone. Use 100% of revenue got from ads and apis to plant trees and give meals to children in need. Think of us as the ChatGPT4 that serves humanity!

A Python-based computer vision and AI system for skin disease recognition and diagnosis. Led end-to-end project pipeline, including data gathering, preprocessing, and training models. Utilized Keras, TensorFlow, OpenCV, and other libraries for image processing and CNN models, showcasing expertise in deep learning and machine learning techniques.