SmartYields 🌱

A Crop Recommendation and Crop Disease Detection System using Machine Learning, Big data Analysis

Crop Recommendation based on factors:

• Temperature
• Humidity
• pH
• Rainfall
• N, P, K - Nitrogen, Phosphorous, Potassium content of soil

Screenshots

Disease Detection

Diseased crop image

Result

Healthy crop image

Result

Crop Recommendation

Inputs

Result

Models Used:

1. Crop Recommendation: Random Forests (98.51% accuracy)
2. Disease Detection: ResNet (98% accuracy)

Modeling

Disease Detection

Crop Recommendation

Tools/Languages

• Jupyter Notebook: For data cleaning, analysis, profiling, modeling, visualizing, recording different results.
• Tensorflow: For image recognition
• Libraries/Modules Used: Pandas, Seaborn, Matplotlib, Sklearn, Pickle.

Web app:

• Flask: Framework to use the predict function from pickle file and build middleware and frontend.
• Bootstrap: Framework used for presentation and styling the frontend.