after augmentation validation accuracy going down? My main question is about augmentation.
if I process the augmentation I believe it always better than less data
but in my case the validation accuracy going down 

train : 7000 images , validation: 3000 images : validation accuracy:0.89

train : 40000 images , validation: 17990 images : validation accuracy:0.85

my augmentation code 

def data_augmentation_folder(trainImagesPath,saveDir):
    #X_train=load_training_data(trainImagesPath,"train")
    print("=====================================================")

    X_train = cleanData(trainImagesPath)
    X_train = np.array(X_train)
    print(X_train[0].shape)



    for i in range(5):

        #print(i)

        datagen = ImageDataGenerator(rotation_range=15,
                       width_shift_range=0.1,
                       height_shift_range=0.1,
                       shear_range=0.01,
                       zoom_range=[0.9, 1.25],
                       horizontal_flip=True,
                       vertical_flip=False,
                       fill_mode='reflect',
                       data_format='channels_last',
                       brightness_range=[0.5, 1.5])


        if i==1:
            datagen = ImageDataGenerator(
                       featurewise_center=True,
                                     featurewise_std_normalization=True,
                                     rotation_range=90,
                                     width_shift_range=0.1,
                                     height_shift_range=0.1,
                                     #zoom_range=0.2
            )
        if i==2:
            datagen = ImageDataGenerator(
                   featurewise_center=True,
                                 featurewise_std_normalization=True,
                                 rotation_range=100,
                                 width_shift_range=0.1,
                                 height_shift_range=0.1,
                                 #zoom_range=0.2
            )
        elif i==3:
            datagen = ImageDataGenerator(
                    rescale=1./255,
                    shear_range=0.2,
                    zoom_range=0.2,
                    horizontal_flip=True)
        elif i==4:
             datagen = ImageDataGenerator(
                    rescale=1./255,
                    shear_range=0.1,
                    rotation_range=80,
                    zoom_range=0.1,
                    horizontal_flip=True,
                    brightness_range=[0.5,1.5])




        datagen.fit(X_train)


        for x, y in datagen.flow(X_train, np.arange(X_train.shape[0]),shuffle=True, save_to_dir=saveDir,save_format='jpg',save_prefix='aug'):
            #print(y)
            assert x.shape[1:] == X_train.shape[1:]
            break



  questions
  
  
  in which case the validation going down even though I proceeded augmentation? 
  what you have to worry about when You proceed the augmentation?
You can not compare validation accuracies if the validation datasets are fundamentally different.

What you were doing initially was to validate your model on a limited set of images. Augmentation is applied to make your dataset more representative of your deployment environment. 

If in your deployment environment you expect images to be drawn from the same subspace as your augmented dataset, the validation accuracy of the first case will not be a true representative of your model's real world performance.