To address the timely identification of nutritional disorders, it is proposed a bedrock composed of: Load and clean datastore.rar Preprocessing of data: Code for resizing and cropping images to increase the number of original images fivefold. Split the data: MATLAB code to split data into training, validation, and test sets. MATLAB_networks.rar ImageDataStores: Containing training, validation, and test sets. Transfer learning: MATLAB code to run five transfer learning models = VGG16, ResNet50, GoogLeNet, DenseNet201, and MobileNetV2 on the imagesets. Output: The output of each network script are: Traininfo_.csv: Having Training_accuracy, Validation_accuracy, Training_loss, Validation_loss info OutPred_.csv: Predicted labels Outtrue_.csv: True labels confussionMatrix__.pdf: confusion matrix

Monitoring the nutritional status of crops is crucial in agricultural management. Given that nutritional deficiencies primarily manifest through visual characteristics, artificial vision stands out as a competitive choice to assess the nutritional status of individual plants. However, to train a supervised artificial vision system driven by convolutional neural networks (CNNs), a high amount of data, properly formatted and labeled is necessary. That's why transfer learning techniques address some of these challenges.