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Exercise: Plant Disease App

Exercise: Plant Disease App#

We want to build a plant disease app that can detect wether a plant is healthy or has a disease, given a photo of its leave 🍃. These are a few examples for each condition:

Healthy

Angular Leaf Spot

Bean Rust

All the images are located in the exercise_2 folder, already splited into the three different conditions.

a) Discuss, in your own words, how would you approach this problem.

That is, what type of learning problem is, and the kind of models you can use to solve it.

b) Implement a solution based on what you previously described. Important: you must evaluate your model over a test set, and report the appropriate metrics for your model.

As a starter, you can use the following code to get all the file_paths and the labels for each image:

import pandas as pd
import glob

image_paths = glob.glob('exercise_3/*/*')

labels = []

for image_path in image_paths:
    labels.append(image_path.split('/')[1].split('/')[0])

import torch
from torchvision.io import read_image, ImageReadMode
from torchvision.models import efficientnet_b0, EfficientNet_B0_Weights
from torchvision.models.feature_extraction import create_feature_extractor

# Step 1: Initialize model with the best available weights
weights = EfficientNet_B0_Weights.DEFAULT
model = efficientnet_b0(weights=weights)
model.eval()  # disables gradient calculation for inference.

# Step 2: Initialize the preprocess function
preprocess = weights.transforms()


layer_before_final_classifiers = 'flatten'

return_nodes = {
    layer_before_final_classifiers: layer_before_final_classifiers
}

feature_extractor = create_feature_extractor(model, return_nodes=return_nodes)

cnn_codes = []
for i, image_name in enumerate(image_paths):
    print(f'Processing image {i+1}/{len(image_paths)}')
    with torch.no_grad():
        img = read_image(image_name, mode=ImageReadMode.RGB)
        img_processed = preprocess(img).unsqueeze(0)
        cnn_code = feature_extractor(img_processed)[layer_before_final_classifiers]
    cnn_codes.append(cnn_code)


cnn_codes = torch.cat(cnn_codes)

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cnn_codes = cnn_codes.numpy()

assert len(cnn_codes) == len(labels)

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(cnn_codes, labels, test_size=0.2, random_state=0)

from sklearn.linear_model import LogisticRegression

clf = LogisticRegression(random_state=0, max_iter=1000).fit(X_train, y_train)

y_pred = clf.predict(X_test)

from sklearn.metrics import classification_report

print(classification_report(y_test, y_pred))

                   precision    recall  f1-score   support

angular_leaf_spot       0.86      0.82      0.84        73
        bean_rust       0.86      0.84      0.85        77
          healthy       0.90      0.96      0.93        57

         accuracy                           0.87       207
        macro avg       0.87      0.88      0.87       207
     weighted avg       0.87      0.87      0.87       207