Textile Recycling and Sustainable Fashion AI

Module: Sustainable Tech | Difficulty: Premium

Textile Classification Accuracy

Water Footprint

Comparison

Python Implementation

import numpy as np

class TextileSorter:
    def __init__(self):
        self.fiber_types = ['cotton', 'polyester', 'nylon', 'wool', 'silk', 'acrylic', 'rayon', 'spandex', 'linen', 'hemp']

    def identify_fiber(self, image_features):
        probs = np.random.dirichlet(np.ones(len(self.fiber_types)))
        idx = np.argmax(probs)
        return self.fiber_types[idx], probs[idx]

    def sort_textiles(self, batch, threshold=0.8):
        sorted_items = {'recyclable': [], 'downcycle': [], 'landfill': []}
        for item in batch:
            fiber, conf = self.identify_fiber(item['features'])
            if conf > threshold and fiber in ['cotton', 'polyester', 'nylon']:
                sorted_items['recyclable'].append(item)
            elif conf > 0.6:
                sorted_items['downcycle'].append(item)
            else:
                sorted_items['landfill'].append(item)
        return sorted_items

Research Insight: Hyperspectral imaging + deep learning identifies textile fiber composition with 94% accuracy.