HOW THE INTENDED SOFTWARE WILL WORK COLOR DETECTION MAPPING/SURFACE PLOT SURFACE GRID FOR INTERPOLATION INTERPOLATING VALUES USING GRID DATA

THE COLOUR DETECTION SOFTWARE

The Color Detection Software is a tool developed for our project aimed at detecting the color of bioindicators based on the RGB value of an image. The software utilizes two machine learning models: Decision Trees and K-Nearest Neighbors (KNN). This documentation provides an overview of the software, its functionality, and the process of developing and improving it.

Figure 2: Training Dataset

1.0 GETTING STARTED

1.1 Prerequisites

1.2 Installation

Install the required libraries using pip:

1.3 Use the model

To use the model, load the `color_detection_model.pkl` file using the Pickle library: Then, use the `predict` function to predict the color of an image:

Or read the RGB values using the `cv2` (python-opencv) library

The model returns a numerical value representing the color category. The color categories are as follows:

• 0: Grey
• 1: Black
• 2: White
• 3: Orange
• 4: Brown
• 5: Blue
• 6: Yellow
• 7: Green
• 8: Violet
• 9: Red

2.0 DATA COLLECTION

2.1 Folder Structure

The software expects a structured dataset in a folder called 'training_dataset.' Inside this folder, each subfolder represents a different color category, containing images of that color.

2.2 Image Selection

The number of images under each color category should be roughly the same for higher training accuracy.

3.0 Data Preprocessing

3.1 Extracting RGB Values

The software extracts the RGB values from each image using the cv2 library. The RGB values are stored in a Pandas DataFrame.

3.2 Data Labeling

The color categories are labeled numerically using Pandas `factorize` function.

4.0 Machine Learning Model

4.1 Decision Tree Classifier

A Decision Tree Classifier is trained on the dataset with different `max_depth` values to determine the optimal tree depth. The accuracy of the model is evaluated using a test dataset.

4.2 K-Nearest Neighbors Classifier

A K-Nearest Neighbors (KNN) Classifier is trained on the dataset with varying numbers of neighbors. The 'distance' metric and 'euclidean' distance measure are used. The accuracy of the KNN model is also evaluated.

5.0 Model Comparison

5.1 Performance Metrics

Decision Tree and KNN models are evaluated using the accuracy metric, which measures the proportion of correctly classified samples.

5.2 Accuracy Visualization

The software generates accuracy vs. parameter plots to visualize the performance of both models and help determine the best model for the task.

Figure 3: Decision Tree Accuracy Plot

Figure 4: KNN Accuracy Plot

6.0 Model Deployment

The final KNN model with the highest accuracy is saved using the Pickle library as 'color_detection_model.pkl.'

7.0 Conclusion

After exploring two machine learning models, Decision Trees and K-Nearest Neighbors, the KNN model achieved the highest accuracy (92%) and was chosen for deployment. Future improvements could include fine-tuning the model and expanding the dataset for more accurate detection. This software is a valuable tool for color-based bioindicator detection and can be further enhanced to support a wider range of applications.

MAPPING/ SURFACE PLOT SOFTWARE

Our Lithium Deposit Mapping Software is the second critical piece of this suite. It visualizes and analyzes geological data to provide insights into lithium deposits. The software generates a surface plot to show lithium deposits. It aids in visualizing and analyzing geological data related to lithium deposits possibilities in the area that was mapped. This is how the software works:

1. Sample Geological Data Generation:

• num_points defines the number of geological data points to generate.
• x, y, and z are arrays of latitude and longitude coordinates within specified mapped area. The latitude and longitude entails the location of the biosensors which is extracted from the barcode of each biosensor to represent the location of lithium samples. For illustration of how the software works random values were used.
• values represents lithology values; a probability between 0 and 1 distinguishes between different lithologies. This probability is calculated from the probability model software which make use of the color detection model. The different probability values are inteprated as follows:.

N.B Random values were used in this example for illustration purpose

2. Grid for interpolation:

meshgrid generates a grid of points in three dimensions (`xq`, `yq`, `zq`) to form a 3D mesh. This grid will be used for interpolation.

3. Interpolating Values Using griddata:

interp_values = griddata(x, y, z, values, xq, yq, zq, 'natural');

griddata performs interpolation to estimate lithology values (values) at the grid points defined by `xq`, `yq`, and `zq`. The 'natural' option specifies a natural neighbour interpolation method;

Where are the lithium deposits most probably located?

• This code segment creates a 3D scatter plot to visualize the actual lithium deposits location.
• The color and size of the points represent different lithology values.

3D Display of lithium deposits location

• This section generates a 3D voxel plot using the interpolated values, providing a 3D visualization of the geological model.
• The color bar shows the color relationship with the associated probability.
• This plot is somehow similar with the one on Eyowa webisite:

Cross-sectional view of the geological model

This code generates a cross-sectional view of the geological model at a specified depth (z = 5) to visualize lithium deposits:

Comparison with Köhler et al.'s artificial neural networks model:

Our Mapping Software

Köhler et al.'s Li Geochemical anomaly map

Our Mapping Software reliably generates lithium maps that align with geological expectations as validated by Köhler et al.'s artificial neural networks model that provides precise predictions based on real-world data. As a result, our mapping software offers flexibility to assess lithium potential in any region. This adaptability is a crucial advantage over case-specific datasets. The code combines data generation, interpolation, and various visualization techniques to provide insights into the distribution of lithium deposits in a 3D geological model. The surface plot, isosurface plot, and cross-section view help mining companies and researchers to know the chances of finding lithium deposits in a particular area of interest. This pre-exploratory detection is crucial for lithium exploration.

CONCLUSION

In lithium exploration, our software shines as a dependable companion. It combines data generation, interpolation, and visualization techniques to understand lithium deposit distribution better. While synthetic data is used for illustration purposes, the software's real strength lies in its reliability, offering valuable insights for mining companies and researchers engaged in lithium exploration. It is a crucial tool for pre-exploratory detection, setting the course for sustainable energy solutions.

NOTE:

While working on the gene sequences for our project we encountered certain problems we realized could be solve with software tools. Hence, we developed tool softwares as a contribution to future iGem teams and synthetic biologists incase they face similar issues while working on their gene sequences. A link to the documentation of these softwares can be found here

LOCAL FUNCTIONS