Estimating millet starch content using NIRS and machine learning

This project is a part of the Avisa Project at ICRISAT.

-- Project Status: [Active]

Objective

The purpose of this project is estimate pearl millet grain starch content using near-infrared spectroscopy and machine learning/deep learning. Calibration models have been developed will be deployed as rapid phenotyping tools for millet breeders.

Partner

• Hone Ag Pty. Ltd.
• https://www.honeag.com/
• Partner contact: Felicity Fraser [[email protected]]

Methods Used

• Inferential Statistics
• Machine Learning
• Deep learning
• Data augmentation
• Data Visualization
• Predictive Modeling
• etc.

Technologies

• Python
• Pandas, jupyter, Numpy
• Scipy, Matplotlib
• Scikit-Learn
• Keras
• Tensorflow
• etc.

Project Description

• Measure oleic acid content on 300 samples of pearl millet
• Scan the same samples to record spectroscopic data covering more than 1000
• Augment data by creating some distortions
• Preprocess the data (filtering, derivating, smoothing, etc)
• Develop ML/DL model architecture
• Train the model
• Make predictions
• Deploy the model

Needs of this project

• frontend development for deployment
• data exploration/descriptive statistics
• data processing/cleaning
• statistical modeling
• writeup/reporting
• etc. (be as specific as possible)

Getting Started

1. Clone this repo (for help see this tutorial).

2. Raw Data is being kept here within this repo.

   If using offline data mention that and how they may obtain the data from the froup)

3. Data processing/transformation scripts are being kept here

4. etc...

If your project is well underway and setup is fairly complicated (ie. requires installation of many packages) create another "setup.md" file and link to it here

Featured Notebooks/Analysis/Deliverables

• Notebook

Contributing Members

Maintener: Adama Ndour

Others

Name	Slack Handle
Adama Ndour	@adamavip
Krithika Anbazhagan	@krithika

Contact

Reach out me