soil_image_detection.py

import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras import layers, models
import matplotlib.pyplot as plt
import numpy as np
import os

dataset_path = '/mnt/c/Users/jbtff/OneDrive/Documents/dataset' # replace with path to soil image dataset 
train_dir = os.path.join(dataset_path, 'train') 
test_dir = os.path.join(dataset_path, 'test')

train_datagen = ImageDataGenerator(rescale=1./255)
test_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(
    train_dir,
    target_size=(128, 128),
    batch_size=32,
    class_mode='categorical'
)

test_generator = test_datagen.flow_from_directory(
    test_dir,
    target_size=(128, 128),
    batch_size=32,
    class_mode='categorical'
)

model = models.Sequential([
    layers.InputLayer(input_shape=(128, 128, 3)),
    layers.Conv2D(32, (3, 3), activation='relu'),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(128, (3, 3), activation='relu'),
    layers.MaxPooling2D((2, 2)),
    layers.Flatten(),
    layers.Dense(128, activation='relu'),
    layers.Dense(4, activation='softmax')
])

model.compile(optimizer='adam',
              loss='categorical_crossentropy',
              metrics=['accuracy'])

history = model.fit(train_generator, epochs=10, validation_data=test_generator)

test_loss, test_acc = model.evaluate(test_generator)
print(f'Test Accuracy: {test_acc * 100:.2f}%')
print(f'Test Loss: {test_loss:.4f}')

model.save('soil_classifier.keras')

acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']

epochs = range(1, len(acc) + 1)

plt.figure(figsize=(12, 4))

plt.subplot(1, 2, 1)
plt.plot(epochs, acc, 'bo-', label='Training accuracy')
plt.plot(epochs, val_acc, 'r*-', label='Validation accuracy')
plt.title('Training and validation accuracy')
plt.legend()

plt.subplot(1, 2, 2)
plt.plot(epochs, loss, 'bo-', label='Training loss')
plt.plot(epochs, val_loss, 'r*-', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()

plt.show()

def predict_image(image_path):
    img = tf.keras.utils.load_img(image_path, target_size=(128, 128))
    img_array = tf.keras.utils.img_to_array(img)
    img_array = np.expand_dims(img_array, axis=0)
    img_array /= 255.

    prediction = model.predict(img_array)
    predicted_class = np.argmax(prediction, axis=1)
    
    class_labels = list(train_generator.class_indices.keys())
    print(f'The predicted soil type is: {class_labels[predicted_class[0]]}')

image_path = '/mnt/c/Users/jbtff/OneDrive/Documents/test_image.jpg' # replace with path of test soil image 
predict_image(image_path)