Follow these steps:

1. First, you'll need to gather a collection of dirt photos to categorize. This dataset should be divided into two parts: training and testing.

2. The photos must then be pre-processed, such as resizing and normalizing the pixel values.

3. After that, import the VGG19 model and delete the final completely linked layer.

4. Then, you will need to add new layers to the model and train it using the training dataset of soil images.

5. Once the model is trained, you can test it on the testing dataset of soil images and evaluate the performance using metrics such as accuracy, precision, recall, and F1-score.

Hi !

Indeed, I already have images what is currently blocking me is the code. So I request a code under matlab to try to implement the model.

My request is in the sense of having a script in order to carry out the analysis and the prediction

Krah Kouadio

% Load the pre-trained VGG19 model

net = vgg19;

% Load the soil images

soilImages = imageDatastore('path/to/soil_images','IncludeSubfolders',true,'LabelSource','foldernames');

% Resize the images to the size of the input layer of the VGG19 model

soilImages.ReadFcn = @(filename)imresize(imread(filename),net.Layers(1).InputSize(1:2));

% Divide the data into training and validation sets

[soilImagesTrain,soilImagesValidation] = splitEachLabel(soilImages,0.8,'randomized');

% Perform data augmentation on the training images

augmentedImages = augmentedImageDatastore(net.Layers(1).InputSize(1:2),soilImagesTrain);

% Replace the last layer of the VGG19 model with a fully connected layer

layers = net.Layers;

layers(end-2) = fullyConnectedLayer(numel(categories(soilImagesTrain.Labels)));

layers(end) = classificationLayer;

% Train the modified VGG19 model on the soil images

options = trainingOptions('sgdm','InitialLearnRate',0.001,'MaxEpochs',20,'ValidationData',soilImagesValidation);

net = trainNetwork(augmentedImages,layers,options);

% Use the trained model to classify new soil images

predictedLabels = classify(net,soilImagesValidation);

% Calculate the classification accuracy

accuracy = mean(predictedLabels == soilImagesValidation.Labels);

you can use other pre-trained CNN models like ResNet50, Inception-v3, etc. depending on the size and complexity of your dataset. You may also consider using other deep learning frameworks such as TensorFlow, Keras, PyTorch, etc.