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ML+DL based Web application for solving agriculture 👨‍🌾problems

License: MIT License

JavaScript 5.74% CSS 0.02% HTML 0.01% Svelte 1.12% Python 0.17% Shell 0.01% Jupyter Notebook 92.93%
agroml regression-analysis multi-class-classification flask svelte machine-learning deep-learning

agroml's Introduction

AgroML

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Tech: Python, Flask, PyTorch, HTML, CSS, JavaScript, Svelte, Scikit-learn, AWS

• Features: Full-stack web application Recommendation system has three features Crop Prediction, Crop Yield, and Plant Disease Detection.

• Full-stack: Machine Learning + Deep Learning based web application(Agriculture domain) develop on Svelte and integrated front-end & back-end with FLask API & deployed using AWS(EC2 instance).

• ML+DL models: Crop prediction(Multi-class classification analysis) we used Random Forest gives 99% accuracy Crop yield(Regression analysis) used DecisionTreeRegressor gives 98.2% accuracy & Plant disease used ResNet architecture gives 99.2% accuracy.

Built with 🛠️

Demo

Home Page

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Crop Prediction 🌾🚜

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Crop Yield Analysis 👨‍🌾

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Plant Disease Detection ☘️

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Supported crops

  • Apple
  • Blueberry
  • Cherry
  • Corn
  • Grape
  • Pepper
  • Orange
  • Peach
  • Potato
  • Soybean
  • Strawberry
  • Tomato
  • Squash
  • Raspberry

System Design

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agroml's People

Contributors

patelris avatar piyushbagani15 avatar soft1ce avatar varunkhambhata avatar

Stargazers

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Watchers

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Forkers

piyushbagani15 rohitdadlani

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