Saliency maps for individual channels?

I trained a CNN using Keras and R with the TensorFlow backend for classifying multispectral images. I want to calculate saliency maps per input data band for a single input image. My idea is to calculate the mean of the saliency map for each channel to get the information, which input band contributed most to the classification. Is this possible and if yes, how? Everywhere I looked, I only found python implementations of saliency maps.

Let's just assume, this is my network and I want to calculate saliency maps for all three channels of the one image in the end, so I know which channel is most important:

# download & load data
cifar <- dataset_cifar10()

# set up model
model <- keras_model_sequential() %>%
  layer_conv_2d(filters = 32, kernel_size = c(3,3), activation = "relu", input_shape = c(32,32,3)) %>%
  layer_max_pooling_2d() %>%
  layer_conv_2d(filters = 64, kernel_size = c(3,3), activation = "relu") %>%
  layer_max_pooling_2d() %>%
  layer_flatten() %>%
  layer_dense(units = 64, activation = "relu") %>%
  layer_dense(units = 10, activation = "softmax")

# compile model
model %>% compile(
  optimizer = "adam",
  loss = "sparse_categorical_crossentropy",
  metrics = "accuracy"
)

# run model
history <- model %>% 
  fit(
    x = cifar$train$x,
    y = cifar$train$y,
    epochs = 10
  )

# pick out one image
test_img <- cifar$test$x[1,,,]

# what now?