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README.md

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 # PlantRootAnalysis
-Analysis of plant root measurement via automatic segmentation. 
+
+# lylybell
+Ushizima working with Lylybell Teran in 2022
+
+## How to start the analysis
+1. Download and open `FINAL_ALGORITHMS` folder on Google Colaboratory (two files in folder)
+2. Run all code on a Python notebook
+
+# Project Overview 
+- Importance: Characterizing root system architecture furthers our understanding of the development of plants, which can lead to increased crop production and environmental protection. 
+- Through computer vision, we developed four semantic segmentation algorithms for Spring Wheat plant roots. Segmented binary images are essential to the quantification and phenotyping of roots. 
+- Automatic Thresholding Algorithms: Otsu and Li Method
+- Machine Learning Algorithms: Random Forest and MLP Classifiers
+
+
+# Automatic Thresholding Algorithm
+The segmentation algorithms performs binary classification consisting of 4 steps: 
+- image denoising
+- applying automatic threshold
+- background cleaning
+- implementing morphological operations
+
+
+# Machine Learning Classifiers
+The segmentation algorithms performs binary classification consisting of 4 steps: 
+- Background Cleaning
+- Feature Selection
+- Determine optimal classifier parameters
+- Train and Test Models
+
+# Measuring Plant Roots
+Recorded properties for scientific purposes within Berkeley Lab through `skimage.measure.regionprops()`
+- area
+- perimeter
+- feret diameter max
+- solidity 
+
+# Results Summarized
+The automatic thresholding algorithms outperformed the machine learning models. Ostu's method provided the best dice coefficient score above 75%. 
+
+![alt_text](https://github.com/dani-lbnl/lylybell/blob/main/Summary_Algorithms.png)