diff --git a/docs/kmeans_classifier.md b/docs/kmeans_classifier.md
index fab80fbed..f52ac3e57 100644
--- a/docs/kmeans_classifier.md
+++ b/docs/kmeans_classifier.md
@@ -1,6 +1,6 @@
 ## Classification using a kmeans cluster model
 
-The first function (`pcv.predict_kmeans`) takes a target image and uses a trained kmeans model produced by [`pcv.learn.train_kmeans`](train_kmeans.md) to classify regions of the target image by the trained clusters. The second function (`pcv.mask_kmeans`) takes a list of clusters and produces the combined mask from clusters of interest. 
+The first function (`pcv.predict_kmeans`) takes a target image and uses a trained kmeans model produced by [`pcv.learn.train_kmeans`](train_kmeans.md) to classify regions of the target image by the trained clusters. The second function (`pcv.mask_kmeans`) takes a list of clusters and produces the combined mask from clusters of interest. The target and training images may be in grayscale or RGB image format.
 
 **plantcv.kmeans_classifier.predict_kmeans**(img, model_path="./kmeansout.fit", patch_size=10)
 
@@ -18,14 +18,13 @@ The first function (`pcv.predict_kmeans`) takes a target image and uses a traine
 - **Example use below**
 
 
-**plantcv.kmeans_classifier.mask_kmeans**(labeled_img, k, patch_size, cat_list=None)
+**plantcv.kmeans_classifier.mask_kmeans**(labeled_img, k, cat_list=None)
 
 **outputs** Either a combined mask of the requestedlist of clusters or a dictionary of each cluster as a separate mask with keys corresponding to the cluster number 
 
 - **Parameters:**
     - labeled_img = The output from predict_kmeans, an image with pixels labeled according to their cluster assignment
     - k = The number of clusters in the trained model
-    - patch_size = Size of the NxN neighborhood around each pixel, used for classification
     - cat_list = List of clusters to include in a combined mask. If None, output is a dictionary of separate masks for each cluster
 
 - **Context:**
@@ -46,9 +45,9 @@ labeled_img = pcv.predict_kmeans(img='./leaf_example.png',
                                  model_path="./kmeansout_leaf.fit", patch_size=5)
 
 #Choosing clusters for each category within the seed image
-background = pcv.mask_kmeans(labeled_img=labeled_img, k=10, patch_size=5, cat_list=[0, 2, 4, 6, 7])
-sick = pcv.mask_kmeans(labeled_img=labeled_img, k=10, patch_size=5, cat_list=[1, 3])
-leaf = pcv.mask_kmeans(labeled_img=labeled_img, k=10, patch_size=5, cat_list=[5, 8, 9])
+background = pcv.mask_kmeans(labeled_img=labeled_img, k=10 cat_list=[0, 2, 4, 6, 7])
+sick = pcv.mask_kmeans(labeled_img=labeled_img, k=10, cat_list=[1, 3])
+leaf = pcv.mask_kmeans(labeled_img=labeled_img, k=10, cat_list=[5, 8, 9])
 
 ```
 
diff --git a/docs/train_kmeans.md b/docs/train_kmeans.md
index 6bd07c86b..2aadc53b1 100644
--- a/docs/train_kmeans.md
+++ b/docs/train_kmeans.md
@@ -1,6 +1,6 @@
 ## Kmeans clustering training 
 
-This function takes in a collection of training images and fits a patch-based kmeans cluster model for later use in classifying cluster assignment in a target image. 
+This function takes in a collection of training images and fits a patch-based kmeans cluster model for later use in classifying cluster assignment in a target image. The target and training images may be in grayscale or RGB image format.
 
 **plantcv.learn.train_kmeans**(img_dir, k, out_path="./kmeansout.fit", prefix="", patch_size=10, sigma=5, sampling=None, seed=1, num_imgs=0, n_init=10)
 
diff --git a/plantcv/learn/train_kmeans.py b/plantcv/learn/train_kmeans.py
index 6f88291e6..728b5f854 100644
--- a/plantcv/learn/train_kmeans.py
+++ b/plantcv/learn/train_kmeans.py
@@ -44,17 +44,15 @@ def train_kmeans(img_dir, k, out_path="./kmeansout.fit", prefix="", patch_size=1
     else:
         training_files = random.choices(file_names, k=num_imgs)  # choosing a set of random files
     # Read and extract patches
-    i = 0
-    for img_name in training_files:
+    for idx, img_name in enumerate(training_files):
         if prefix in img_name:
-            img = cv2.imread(os.path.join(img_dir, img_name))
-            if i == 0:
+            img = cv2.imread(os.path.join(img_dir, img_name), -1)
+            if idx == 0:
                 # Getting info from first image
                 patches = patch_extract(img, patch_size=patch_size, sigma=sigma, sampling=sampling)
             else:
                 # Concatenating each additional image
                 patches = np.vstack((patches, patch_extract(img, patch_size=patch_size, sigma=sigma, sampling=sampling)))
-            i += 1
 
     kmeans = MiniBatchKMeans(n_clusters=k, n_init=n_init, random_state=seed)
     fitted = kmeans.fit(patches)
@@ -79,7 +77,11 @@ def patch_extract(img, patch_size=10, sigma=5, sampling=None, seed=1):
     :return patches_lin: numpy.ndarray
     """
     # Gaussian blur
-    img_blur = np.round(gaussian(img, sigma=sigma, channel_axis=2)*255).astype(np.uint16)
+    if len(img.shape) == 2:
+        img_blur = np.round(gaussian(img, sigma=sigma)*255).astype(np.uint16)
+    elif len(img.shape) == 3 and img.shape[2] == 3:
+        img_blur = np.round(gaussian(img, sigma=sigma, channel_axis=2)*255).astype(np.uint16)
+
     # Extract patches
     patches = image.extract_patches_2d(img_blur, (patch_size, patch_size),
                                        max_patches=sampling, random_state=seed)
diff --git a/plantcv/plantcv/kmeans_classifier.py b/plantcv/plantcv/kmeans_classifier.py
index 8a052604c..195edd744 100644
--- a/plantcv/plantcv/kmeans_classifier.py
+++ b/plantcv/plantcv/kmeans_classifier.py
@@ -24,9 +24,23 @@ def predict_kmeans(img, model_path="./kmeansout.fit", patch_size=10):
     kmeans = load(model_path)
     train_img, _, _ = pcv.readimage(img)
 
+    before = after = int((patch_size - 1)/2)   # odd
+    if patch_size % 2 == 0:   # even
+        before = int((patch_size-2)/2)
+        after = int(patch_size/2)
+
+    # Padding
+    if len(train_img.shape) == 2:  # gray
+        train_img = np.pad(train_img, pad_width=((before, after), (before, after)), mode="edge")
+    elif len(train_img.shape) == 3 and train_img.shape[2] == 3:  # rgb
+        train_img = np.pad(train_img, pad_width=((before, after), (before, after), (0, 0)), mode="edge")
+
     # Shapes
     mg = np.floor(patch_size / 2).astype(np.int32)
-    h, w, _ = train_img.shape
+    if len(train_img.shape) == 2:
+        h, w = train_img.shape
+    elif len(train_img.shape) == 3 and train_img.shape[2] == 3:
+        h, w, _ = train_img.shape
 
     # Do the prediction
     train_patches = patch_extract(train_img, patch_size=patch_size)
@@ -38,7 +52,7 @@ def predict_kmeans(img, model_path="./kmeansout.fit", patch_size=10):
     return labeled
 
 
-def mask_kmeans(labeled_img, k, patch_size=10, cat_list=None):
+def mask_kmeans(labeled_img, k, cat_list=None):
     """
     Uses the predicted clusters from a target image to generate a binary mask.
     Inputs:
@@ -52,29 +66,23 @@ def mask_kmeans(labeled_img, k, patch_size=10, cat_list=None):
     :param patch_size: positive non-zero integer
     :param cat_list: list of positive non-zero integers
     """
-    mg = np.floor(patch_size / 2).astype(np.int32)
-    h, w = labeled_img.shape
     if cat_list is None:
         mask_dict = {}
         L = [*range(k)]
         for i in L:
-            mask = np.ones(labeled_img.shape)
-            mask = np.logical_and(mask, labeled_img != i)
-            mask[:, 0:mg] = False
-            mask[:, w-mg:w] = False
-            mask[0:mg, :] = False
-            mask[h-mg:h, :] = False
-            mask_light = abs(1-mask)
+            mask_light = np.where(labeled_img == i, 255, 0)
             _debug(visual=mask_light, filename=os.path.join(params.debug_outdir, "_kmeans_mask_"+str(i)+".png"))
             mask_dict[str(i)] = mask_light
         return mask_dict
-    mask = np.ones(labeled_img.shape)
-    for label in cat_list:
-        mask = np.logical_and(mask, labeled_img != label)
-    mask[:, 0:mg] = False
-    mask[:, w-mg:w] = False
-    mask[0:mg, :] = False
-    mask[h-mg:h, :] = False
-    mask_light = abs(1-mask)
+    # Store debug
+    debug = params.debug
+    # Change to None so that logical_or does not plot each stepwise addition
+    params.debug = None
+    for idx, i in enumerate(cat_list):
+        if idx == 0:
+            mask_light = np.where(labeled_img == i, 255, 0)
+        else:
+            mask_light = pcv.logical_or(mask_light, np.where(labeled_img == i, 255, 0))
+    params.debug = debug
     _debug(visual=mask_light, filename=os.path.join(params.debug_outdir, "_kmeans_combined_mask.png"))
     return mask_light
diff --git a/tests/conftest.py b/tests/conftest.py
index 3654b7193..39322809e 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -82,6 +82,8 @@ def __init__(self):
         self.cluster_names_too_many = os.path.join(self.datadir, "cluster_names_too_many.txt")
         # Kmeans classifier directory
         self.kmeans_classifier_dir = os.path.join(self.datadir, "kmeans_classifier_dir")
+        # Kmeans classifier grayscale directory
+        self.kmeans_classifier_gray_dir = os.path.join(self.datadir, "kmeans_classifier_gray_dir")
 
     @staticmethod
     def load_hsi(pkl_file):
diff --git a/tests/learn/conftest.py b/tests/learn/conftest.py
index 5acdfd73b..3203efb2a 100644
--- a/tests/learn/conftest.py
+++ b/tests/learn/conftest.py
@@ -17,6 +17,8 @@ def __init__(self):
         self.rgb_values_table = os.path.join(self.datadir, "rgb_values_table.txt")
         # Kmeans training directory
         self.kmeans_train_dir = os.path.join(self.datadir, "kmeans_train_dir")
+        # Kmeans training grayscale directory
+        self.kmeans_train_gray_dir = os.path.join(self.datadir, "kmeans_train_gray_dir")
 
 
 @pytest.fixture(scope="session")
diff --git a/tests/learn/test_kmeans_train.py b/tests/learn/test_kmeans_train.py
index b6358ee46..ec039792b 100644
--- a/tests/learn/test_kmeans_train.py
+++ b/tests/learn/test_kmeans_train.py
@@ -7,12 +7,21 @@ def test_train_kmeans(learn_test_data, tmpdir):
     # Create a test tmp directory
     cache_dir = tmpdir.mkdir("cache")
     training_dir = learn_test_data.kmeans_train_dir
+    training_dir_gray = learn_test_data.kmeans_train_gray_dir
     outfile_subset = os.path.join(str(cache_dir), "kmeansout_subset.fit")
     outfile_full = os.path.join(str(cache_dir), "kmeansout_full.fit")
+    outfile_subset_gray = os.path.join(str(cache_dir), "kmeansout_subset_gray.fit")
+    outfile_full_gray = os.path.join(str(cache_dir), "kmeansout_full_gray.fit")
     # Train full model and partial model
     train_kmeans(img_dir=training_dir, prefix="kmeans_train",
-                 out_path=outfile_subset, k=5, num_imgs=3)
+                 out_path=outfile_subset, k=5, patch_size=4, num_imgs=3)
     train_kmeans(img_dir=training_dir, prefix="kmeans_train",
-                 out_path=outfile_full, k=5)
+                 out_path=outfile_full, k=5, patch_size=4)
+    train_kmeans(img_dir=training_dir_gray, prefix="kmeans_train",
+                 out_path=outfile_subset_gray, k=5, patch_size=4, num_imgs=3)
+    train_kmeans(img_dir=training_dir_gray, prefix="kmeans_train",
+                 out_path=outfile_full_gray, k=5, patch_size=4)
     assert os.path.exists(outfile_subset)
     assert os.path.exists(outfile_full)
+    assert os.path.exists(outfile_subset_gray)
+    assert os.path.exists(outfile_full_gray)
diff --git a/tests/plantcv/test_kmeans_classifier.py b/tests/plantcv/test_kmeans_classifier.py
index 89b0eeff4..e940f5909 100644
--- a/tests/plantcv/test_kmeans_classifier.py
+++ b/tests/plantcv/test_kmeans_classifier.py
@@ -6,14 +6,25 @@
 def test_kmeans_classifier(test_data):
     """Test for PlantCV."""
     input_dir = test_data.kmeans_classifier_dir
-    labeled_img = predict_kmeans(img=input_dir+"/test_image.jpg", model_path=input_dir+"/kmeans_out.fit", patch_size=5)
+    input_dir_gray = test_data.kmeans_classifier_gray_dir
+    labeled_img = predict_kmeans(img=input_dir+"/test_image.jpg", model_path=input_dir+"/kmeans_out.fit", patch_size=4)
+    labeled_img_gray = predict_kmeans(img=input_dir_gray+"/test_image_gray.jpg",
+                                      model_path=input_dir_gray+"/kmeans_out_gray.fit", patch_size=4)
     test_labeled, _, _ = readimage(input_dir+"/labeled_image.png")
+    test_labeled_gray, _, _ = readimage(input_dir_gray+"/labeled_image_gray.png")
     assert (labeled_img == test_labeled).all()
+    assert (labeled_img_gray == test_labeled_gray).all()
 
-    mask_dict = mask_kmeans(labeled_img=labeled_img, k=4, patch_size=5)
+    mask_dict = mask_kmeans(labeled_img=labeled_img, k=4)
+    mask_dict_gray = mask_kmeans(labeled_img=labeled_img_gray, k=4)
     for i in range(4):
         assert (readimage(input_dir+"/label_example_"+str(i)+".png")[0] == mask_dict[str(i)]).all()
+    for i in range(4):
+        assert (readimage(input_dir_gray+"/label_example_gray_"+str(i)+".png")[0] == mask_dict_gray[str(i)]).all()
 
-    combo_mask = mask_kmeans(labeled_img=labeled_img, k=4, patch_size=5, cat_list=[1, 2])
+    combo_mask = mask_kmeans(labeled_img=labeled_img, k=4, cat_list=[1, 2])
+    combo_mask_gray = mask_kmeans(labeled_img=labeled_img_gray, k=4, cat_list=[1, 2])
     combo_example, _, _ = readimage(input_dir+"/combo_mask_example.png")
+    combo_example_gray, _, _ = readimage(input_dir_gray+"/combo_mask_example_gray.png")
     assert (combo_mask == combo_example).all()
+    assert (combo_mask_gray == combo_example_gray).all()
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