A Novel Deep Learning-Based Black Fungus Disease Identification Using Modified Hybrid Learning Methodology

Algorithm 1

Image acquisition and preprocessing.

	Input: Library Definitions, Image Samples and Dataset Accessing Permission Factors.
	Output: Preprocessed Image with proper Features.
	Step-1: Importing the required libraries such as numpy, matplot and so on.
	Pseudocode:
	Import_Lib matplot_lib; Import Plt from matplot_pyplot;
	Import np_1 from numpy;
	Import_Lib tensorflow, keras;
	Import_Lib applications from tensorflow_keras;
	Import_Lib optimizer from tensorflow_keras;
	Import_Lib sequential_model, Density, Flat, Drop from tensorflow_keras;
	Import_Lib categorizer from tensorflow_keras_utils;
	Import_Lib convolutional_layers from tensorflow_keras_layers;
	Step-2: Define a path to acquire the dataset, in which it is usually positioned into the Google Colaboratory drive. So, that the path permissions are required to acquire it for processing.
	Step-3: Define the dataset directory into the object called ‘DATASET_DI’.
	Step-4: Declare the training path into the object called train_path.
	Step-5: Declare the testing path into the object called test_path.
	Pseudocode:
	Import the Drive from Colab;
	Mount the Directory using drive_mount function.
	DATASET_DIR.assign (‘Dataset_Directory’);
	Define Training and Testing Path;
	Step-6: Resize the acquired images based on 64 × 64 pixel ratio.
	Step-7: List the directory images under defined classes such as Normal and Abnormal.
	Step-8: Acquire all the normal and diseased images with respect to the dataset classes, in which it is indexed all the classes into the array variable.
	Pseudocode:
	Image_size (64 × 64);
	List_Directory (DATASET_DIR);
	Import the package called mpimg from matplotlib;
	Generate the loop to acquire all images from the dataset.
	for (img_path in mpimg(DATASET_DIR[‘Normal’]):
	{
	normal_images_Append {mpimg_imread [normal_img_path]};
	}
	for (img_path in mpimg (DATASET_DIR [‘Disease’]):
	{
	disease_images_Append {mpimg_imread [disease_img_path]};
	}
	Step-9: Count the diseased and normal images from the dataset and store those respective images into the array with respect to the proper class indexing.
	Step-10: Display the corresponding details into the user’s perspective by using print function.
	Pseudocode:
	NI = format {len [normal_images]);
	DI = format {len [diesease_images]);
	Print (‘Normal Image Count:’ +NI);
	Print (‘Diseased Image Count:’ +DI);