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

Algorithm 2

Extracting image features.

	Input: Image Input Directory, Sample Image Count, Batch Size.
	Output: Extracted Features of the input black fungus dataset image.
	Step-1: Define the function named ‘feature_extract’ with required input parameters.
	Step-2: Specify the input parameters such as input directory, total number of images and batch size to the function called ‘feature_extract'
	Step-3: Generate the given image data by using data-Generator() function.
	Step-4: Provide the required parameters to manipulate the data_generator function such as directory, batch size, target image size and image shuffle status.
	Pseudocode:
	define feature_extract(img_directory, sampleCount, batchSize):
	data_gen = Image_DataGenerator {scale = 1./255}
	data_gen_1←data_gen.flow (from_directory{img_directory, batch_Size = batchSize, targetSize:[32,32], shuffle:False});
	Step-5: Initialize the variable called img_cnt and set a constant value to that for the loop initiation.
	Step-6: Declare X and Y batches to identify the input and associated labels.
	Step-7: Generate the ‘for' loop to process the features of the images presented into the dataset.
	Step-8: Concatenate the X and Y batches to the temporary variable called X and Y.
	Step-9: Return the class names with respect to X and Y.
	Pseudocode:
	img_cnt = 0;
	x_batches = [];
	y_batches = [];
	for (inputs_batch, labels_batch in generator)
	{
	x_batches_append{inputs_batch}; y_batches_append{labels_batch};
	img_cnt+ = inputs_batch{shape(0)};
	if (img_cnt ≥ generator.n)
	{
	break;
	}
	X.assign(np_concatenate{x_batches});
	Y.assign(np_concatenate{Y_batches});
	}
	Return X, Y, class_names;
	Step-10: Image Features extracted and set to an object called train_features.