Design and Evaluation of a Hybrid Technique for Detecting Sunflower Leaf Disease Using Deep Learning Approach

Algorithm 1

Technique for detecting sunflower leaf disease.

	start:
(1)	import numpy, sklearn, tensorflow, keras as np1, sk, tf, kr
(2)	def convert_img_array(img):
	If img not None:
	Return array of img
	else
	Return np1.array[]
(3)	for I in imgdir://…imgdir contains list of images
	imgList[].append(convert_img_array(img))
	labelList[].append(i.label)//…i.label gives label or class of each images
(4)	modify labelList with label_binarizer library
(5)	image_list = np1.array(imgList, dtype = np.float16)/225.0
(6)	train_test_split(image_list, labelList, 0.2,3)
(7)	do Augmentation with Keras.ImageDataGenerator()
(8)	Implement Models:
	from keras.applications.vgg16 import VGG16
	from keras.applications.mobilenet import MobileNet
(9)	models[].append(VGG16() & MobileNet)
(10)	for i in range models:
	Models[i].fit_transform();//to train the models
(11)	Now combine prediction values;
	For i in range models:
	Yhat = models[i].prediction(); //stacked data set
(12)	Now create stacked model
	stackedx = call process 11
	Model = LogisticRegression();
13Now to train the stacked model:
	Model.fit(stackedx, test_x);
(14)	Now store prediction value of stacked model:
	Yhat = Model.predict(stackedx)
(15)	Now calculate accuracy:
	Sklearn.accuracy_score(yhat, test_y)
	end;