diff --git a/docs/color_palette.md b/docs/color_palette.md
index 6effd8041..84fd67d67 100644
--- a/docs/color_palette.md
+++ b/docs/color_palette.md
@@ -1,31 +1,50 @@
 ## Color Palette
 
-Returns a random list of RGB color values, equally spaced across a rainbow color spectrum.
-If one color is requested, a random color is returned. Otherwise, evenly spaced colors are returned.
+Returns a list of RGB color values, equally spaced across a color map. The color map used is configurable and colors
+can either be returned in sequential or randomized order.
 
-**plantcv.color_palette(*num*)**
+**plantcv.color_palette(*num, saved=False*)**
 
 **returns** colors
 
 - **Parameters:**
     - num - an integer number greater than or equal to 1
+    - saved - True/False whether a previously saved color scale should be used. Default = False
 - **Context:**
-    - Used when a random set of colors is needed.
+    - Used when a set of colors is needed.
+    - See: [Multi-plant tutorial](multi-plant_tutorial.md), [Morphology tutorial](morphology_tutorial.md),
+    [Spatial clustering](spatial_clustering.md), and [Watershed segmentation](watershed.md).
+    - Visit the [Matplotlib](https://matplotlib.org/tutorials/colors/colormaps.html#sphx-glr-tutorials-colors-colormaps-py) website for a list of available colormaps.
 
 ```python
 
 from plantcv import plantcv as pcv
 
-# Get one random color
+# Get one color
 colors = pcv.color_palette(1)
 print(colors)
-[(255, 16, 0)]
+# [[255, 0, 40]]
 
-# Get five random colors
+# The color scale is saved for use by other functions
+print(pcv.params.saved_color_scale)
+# [[255, 0, 40]]
+
+# The color scale can be changed and the order can be changed from "sequential" to "random"
+pcv.params.color_scale = "viridis"
+pcv.params.color_sequence = "random"
+
+# Get five colors (note this will be a new color scale because saved = False by default)
 colors = pcv.color_palette(5)
 print(colors)
-[(0, 0, 255), (0, 255, 205), (100, 255, 0), (255, 106, 0), (255, 0, 199)]
+# [[68, 1, 84], [94, 201, 97], [58, 82, 139], [253, 231, 36], [32, 144, 140]]
+
+# To use a saved color scale (if it exists)
+colors = pcv.color_palette(num=5, saved=True)
+print(colors)
+# [[68, 1, 84], [94, 201, 97], [58, 82, 139], [253, 231, 36], [32, 144, 140]]
 
+# To explicitly remove the saved scale, set it to None
+pcv.params.saved_color_scale = None
 ```
 
 **Source Code:** [Here](https://github.com/danforthcenter/plantcv/blob/master/plantcv/plantcv/color_palette.py)
diff --git a/docs/params.md b/docs/params.md
index db0671dd0..50bed0acd 100644
--- a/docs/params.md
+++ b/docs/params.md
@@ -27,14 +27,23 @@ Attributes are accessed as plantcv.*attribute*.
 
 **dpi**: Dots per inch for plotting debugging images. 
 
-**text_size**: Size of the text for labels in debugging plots created by [segment_angle](segment_angle.md), [segment_curvature](segment_curvature.md), [segment_euclidean_length.md),
+**text_size**: Size of the text for labels in debugging plots created by [segment_angle](segment_angle.md), [segment_curvature](segment_curvature.md), [segment_euclidean_length](segment_euclidean_length.md),
 [segment_id](segment_id.md), [segment_insertion_angle](segment_insertion_angle.md), [segment_path_length](segment_pathlength.md), and [segment_tangent_angle](segment_tangent_angle.md) from
 the morphology sub-package. 
 
-**text_thickness**: Thickness of the text for labels in debugging plots created by [segment_angle](segment_angle.md), [segment_curvature](segment_curvature.md), [segment_euclidean_length.md),
+**text_thickness**: Thickness of the text for labels in debugging plots created by [segment_angle](segment_angle.md), [segment_curvature](segment_curvature.md), [segment_euclidean_length](segment_euclidean_length.md),
 [segment_id](segment_id.md), [segment_insertion_angle](segment_insertion_angle.md), [segment_path_length](segment_pathlength.md), and [segment_tangent_angle](segment_tangent_angle.md) from
 the morphology sub-package. 
 
+**color_scale**: The name of a color scale (a Matplot lib colormap) used by [segment_angle](segment_angle.md), [segment_curvature](segment_curvature.md), 
+[segment_euclidean_length](segment_euclidean_length.md), [segment_insertion_angle](segment_insertion_angle.md), [segment_path_length](segment_pathlength.md), and [segment_skeleton](segment_skeleton.md), 
+[segment_tangent_angle](segment_tangent_angle.md) from the morphology sub-package, and [cluster_contours](cluster_contours.md), [spatial_clustering](spatial_clustering.md), and 
+[watershed_segmentation](watershed.md) from the base package. Default = "gist_rainbow". See the [Matplotlib](https://matplotlib.org/tutorials/colors/colormaps.html#sphx-glr-tutorials-colors-colormaps-py) website for available options.
+
+**color_sequence**: Set the sequence of colors from the `color_scale` created by the `color_palette` function to "sequential" or "random" order. Default = "sequential".
+
+**saved_color_scale**: Using the `color_palette` function will save the color scale here for reuse in downstream functions. Set to `None` to remove. Default = `None`.
+
 ### Example
 
 Updated PlantCV functions use `params` implicitly, so overriding the `params` defaults will alter the behavior of
diff --git a/docs/updating.md b/docs/updating.md
index 07f39cf28..705d9e40f 100644
--- a/docs/updating.md
+++ b/docs/updating.md
@@ -267,6 +267,7 @@ pages for more details on the input and output variable types.
 
 * pre v3.0: NA
 * post v3.0: colors = **plantcv.color_palette**(*num*)
+* post v3.9: colors = **plantcv.color_palette**(*num, saved=False*)
 
 #### plantcv.crop_position_mask
 
diff --git a/plantcv/plantcv/__init__.py b/plantcv/plantcv/__init__.py
index e039a5f0e..3d902bf83 100644
--- a/plantcv/plantcv/__init__.py
+++ b/plantcv/plantcv/__init__.py
@@ -8,21 +8,35 @@
 
 
 class Params:
-    """PlantCV parameters class
-    Keyword arguments/parameters:
-    device       = device number. Used to count steps in the pipeline. (default: 0)
-    debug        = None, print, or plot. Print = save to file, Plot = print to screen. (default: None)
-    debug_outdir = Debug images output directory. (default: .)
-    :param device: int
-    :param debug: str
-    :param debug_outdir: str
-    :param line_thickness: numeric
-    :param dpi: int
-    :param text_size: float
-    """
+    """PlantCV parameters class."""
 
     def __init__(self, device=0, debug=None, debug_outdir=".", line_thickness=5, dpi=100, text_size=0.55,
-                 text_thickness=2):
+                 text_thickness=2, color_scale="gist_rainbow", color_sequence="sequential", saved_color_scale=None):
+        """Initialize parameters.
+
+        Keyword arguments/parameters:
+        device            = Device number. Used to count steps in the pipeline. (default: 0)
+        debug             = None, print, or plot. Print = save to file, Plot = print to screen. (default: None)
+        debug_outdir      = Debug images output directory. (default: .)
+        line_thickness    = Width of line drawings. (default: 5)
+        dpi               = Figure plotting resolution, dots per inch. (default: 100)
+        text_size         = Size of plotting text. (default: 0.55)
+        text_thickness    = Thickness of plotting text. (default: 2)
+        color_scale       = Name of plotting color scale (matplotlib colormap). (default: gist_rainbow)
+        color_sequence    = Build color scales in "sequential" or "random" order. (default: sequential)
+        saved_color_scale = Saved color scale that will be applied next time color_palette is called. (default: None)
+
+        :param device: int
+        :param debug: str
+        :param debug_outdir: str
+        :param line_thickness: numeric
+        :param dpi: int
+        :param text_size: float
+        :param text_thickness: int
+        :param color_scale: str
+        :param color_sequence: str
+        :param saved_color_scale: list
+        """
         self.device = device
         self.debug = debug
         self.debug_outdir = debug_outdir
@@ -30,6 +44,9 @@ def __init__(self, device=0, debug=None, debug_outdir=".", line_thickness=5, dpi
         self.dpi = dpi
         self.text_size = text_size
         self.text_thickness = text_thickness
+        self.color_scale = color_scale
+        self.color_sequence = color_sequence
+        self.saved_color_scale = saved_color_scale
 
 
 class Outputs:
diff --git a/plantcv/plantcv/color_palette.py b/plantcv/plantcv/color_palette.py
index 56b0da0ac..545419dd6 100755
--- a/plantcv/plantcv/color_palette.py
+++ b/plantcv/plantcv/color_palette.py
@@ -1,70 +1,36 @@
 # Color palette returns an array of colors (rainbow)
 
-from random import randrange
+from matplotlib import pyplot as plt
+import numpy as np
+from plantcv.plantcv import params
 
 
-def color_palette(num):
+def color_palette(num, saved=False):
     """color_palette: Returns a list of colors length num
 
     Inputs:
-    num        = number of colors to return. If num = 1 a random color is returned,
-                 otherwise, evenly spaced colors are returned.
+    num        = number of colors to return.
+    saved      = use the previously stored color scale, if any (default = False).
 
     Returns:
-    colors     = a list of color tuples (RGB values)
+    colors     = a list of color lists (RGB values)
 
     :param num: int
     :return colors: list
     """
-    # Rainbow color scheme (red->red)
-    rainbow = (
-        (0, 0, 255), (0, 6, 255), (0, 12, 255), (0, 18, 255), (0, 24, 255), (0, 30, 255), (0, 36, 255), (0, 42, 255),
-        (0, 48, 255), (0, 54, 255), (0, 60, 255), (0, 66, 255), (0, 72, 255), (0, 78, 255), (0, 84, 255), (0, 90, 255),
-        (0, 96, 255), (0, 102, 255), (0, 108, 255), (0, 114, 255), (0, 120, 255), (0, 126, 255), (0, 131, 255),
-        (0, 137, 255), (0, 143, 255), (0, 149, 255), (0, 155, 255), (0, 161, 255), (0, 167, 255), (0, 173, 255),
-        (0, 179, 255), (0, 185, 255), (0, 191, 255), (0, 197, 255), (0, 203, 255), (0, 209, 255), (0, 215, 255),
-        (0, 221, 255), (0, 227, 255), (0, 233, 255), (0, 239, 255), (0, 245, 255), (0, 251, 255), (0, 255, 253),
-        (0, 255, 247), (0, 255, 241), (0, 255, 235), (0, 255, 229), (0, 255, 223), (0, 255, 217), (0, 255, 211),
-        (0, 255, 205), (0, 255, 199), (0, 255, 193), (0, 255, 187), (0, 255, 181), (0, 255, 175), (0, 255, 169),
-        (0, 255, 163), (0, 255, 157), (0, 255, 151), (0, 255, 145), (0, 255, 139), (0, 255, 133), (0, 255, 128),
-        (0, 255, 122), (0, 255, 116), (0, 255, 110), (0, 255, 104), (0, 255, 98), (0, 255, 92), (0, 255, 86),
-        (0, 255, 80),
-        (0, 255, 74), (0, 255, 68), (0, 255, 62), (0, 255, 56), (0, 255, 50), (0, 255, 44), (0, 255, 38), (0, 255, 32),
-        (0, 255, 26), (0, 255, 20), (0, 255, 14), (0, 255, 8), (0, 255, 2), (4, 255, 0), (10, 255, 0), (16, 255, 0),
-        (22, 255, 0), (28, 255, 0), (34, 255, 0), (40, 255, 0), (46, 255, 0), (52, 255, 0), (58, 255, 0), (64, 255, 0),
-        (70, 255, 0), (76, 255, 0), (82, 255, 0), (88, 255, 0), (94, 255, 0), (100, 255, 0), (106, 255, 0),
-        (112, 255, 0),
-        (118, 255, 0), (124, 255, 0), (129, 255, 0), (135, 255, 0), (141, 255, 0), (147, 255, 0), (153, 255, 0),
-        (159, 255, 0), (165, 255, 0), (171, 255, 0), (177, 255, 0), (183, 255, 0), (189, 255, 0), (195, 255, 0),
-        (201, 255, 0), (207, 255, 0), (213, 255, 0), (219, 255, 0), (225, 255, 0), (231, 255, 0), (237, 255, 0),
-        (243, 255, 0), (249, 255, 0), (255, 255, 0), (255, 249, 0), (255, 243, 0), (255, 237, 0), (255, 231, 0),
-        (255, 225, 0), (255, 219, 0), (255, 213, 0), (255, 207, 0), (255, 201, 0), (255, 195, 0), (255, 189, 0),
-        (255, 183, 0), (255, 177, 0), (255, 171, 0), (255, 165, 0), (255, 159, 0), (255, 153, 0), (255, 147, 0),
-        (255, 141, 0), (255, 135, 0), (255, 129, 0), (255, 124, 0), (255, 118, 0), (255, 112, 0), (255, 106, 0),
-        (255, 100, 0), (255, 94, 0), (255, 88, 0), (255, 82, 0), (255, 76, 0), (255, 70, 0), (255, 64, 0), (255, 58, 0),
-        (255, 52, 0), (255, 46, 0), (255, 40, 0), (255, 34, 0), (255, 28, 0), (255, 22, 0), (255, 16, 0), (255, 10, 0),
-        (255, 4, 0), (255, 0, 2), (255, 0, 8), (255, 0, 14), (255, 0, 20), (255, 0, 26), (255, 0, 32), (255, 0, 38),
-        (255, 0, 44), (255, 0, 50), (255, 0, 56), (255, 0, 62), (255, 0, 68), (255, 0, 74), (255, 0, 80), (255, 0, 86),
-        (255, 0, 92), (255, 0, 98), (255, 0, 104), (255, 0, 110), (255, 0, 116), (255, 0, 122), (255, 0, 128),
-        (255, 0, 133), (255, 0, 139), (255, 0, 145), (255, 0, 151), (255, 0, 157), (255, 0, 163), (255, 0, 169),
-        (255, 0, 175), (255, 0, 181), (255, 0, 187), (255, 0, 193), (255, 0, 199), (255, 0, 205), (255, 0, 211),
-        (255, 0, 217), (255, 0, 223), (255, 0, 229), (255, 0, 235), (255, 0, 241), (255, 0, 247), (255, 0, 253),
-        (251, 0, 255), (245, 0, 255), (239, 0, 255), (233, 0, 255), (227, 0, 255), (221, 0, 255), (215, 0, 255),
-        (209, 0, 255), (203, 0, 255), (197, 0, 255), (191, 0, 255), (185, 0, 255), (179, 0, 255), (173, 0, 255),
-        (167, 0, 255), (161, 0, 255), (155, 0, 255), (149, 0, 255), (143, 0, 255), (137, 0, 255), (131, 0, 255),
-        (126, 0, 255), (120, 0, 255), (114, 0, 255), (108, 0, 255), (102, 0, 255), (96, 0, 255), (90, 0, 255),
-        (84, 0, 255),
-        (78, 0, 255), (72, 0, 255), (66, 0, 255), (60, 0, 255), (54, 0, 255), (48, 0, 255), (42, 0, 255), (36, 0, 255),
-        (30, 0, 255), (24, 0, 255), (18, 0, 255), (12, 0, 255), (6, 0, 255))
-
-    if num == 1:
-        color = rainbow[randrange(0, 255)]
-        return [color]
+    # If a previous palette is saved and saved = True, return it
+    if params.saved_color_scale is not None and saved is True:
+        return params.saved_color_scale
     else:
-        dist = int(len(rainbow) / num)
-        colors = []
-        index = 0
-        for i in range(1, num + 1):
-            colors.append(rainbow[index])
-            index += dist
+        # Retrieve the matplotlib colormap
+        cmap = plt.get_cmap(params.color_scale)
+        # Get num evenly spaced colors
+        colors = cmap(np.linspace(0, 1, num), bytes=True)
+        colors = colors[:, 0:3].tolist()
+        # colors are sequential, if params.color_sequence is random then shuffle the colors
+        if params.color_sequence == "random":
+            np.random.shuffle(colors)
+        # Save the color scale for further use
+        params.saved_color_scale = colors
+
         return colors
diff --git a/plantcv/plantcv/morphology/check_cycles.py b/plantcv/plantcv/morphology/check_cycles.py
index ce19385ec..099b7e888 100644
--- a/plantcv/plantcv/morphology/check_cycles.py
+++ b/plantcv/plantcv/morphology/check_cycles.py
@@ -54,7 +54,8 @@ def check_cycles(skel_img):
     cycle_img = dilate(cycle_img, params.line_thickness, 1)
     cycle_img = cv2.cvtColor(cycle_img, cv2.COLOR_GRAY2RGB)
     if num_cycles > 0:
-        rand_color = color_palette(num_cycles)
+        # Get a new color scale
+        rand_color = color_palette(num=num_cycles, saved=False)
         for i, cnt in enumerate(cycle_objects):
             cv2.drawContours(cycle_img, cycle_objects, i, rand_color[i], params.line_thickness, lineType=8,
                              hierarchy=cycle_hierarchies)
diff --git a/plantcv/plantcv/morphology/segment_angle.py b/plantcv/plantcv/morphology/segment_angle.py
index c311875b5..e8aac9cfa 100644
--- a/plantcv/plantcv/morphology/segment_angle.py
+++ b/plantcv/plantcv/morphology/segment_angle.py
@@ -33,7 +33,8 @@ def segment_angle(segmented_img, objects):
 
     labeled_img = segmented_img.copy()
 
-    rand_color = color_palette(len(objects))
+    # Use a previously saved color scale if available
+    rand_color = color_palette(num=len(objects), saved=True)
 
     for i, cnt in enumerate(objects):
         # Find bounds for regression lines to get drawn
diff --git a/plantcv/plantcv/morphology/segment_combine.py b/plantcv/plantcv/morphology/segment_combine.py
index ecba4009f..e0ff864d4 100644
--- a/plantcv/plantcv/morphology/segment_combine.py
+++ b/plantcv/plantcv/morphology/segment_combine.py
@@ -78,8 +78,8 @@ def segment_combine(segment_list, objects, mask):
     labeled_img = mask.copy()
     labeled_img = cv2.cvtColor(labeled_img, cv2.COLOR_GRAY2RGB)
 
-    # Color each segment a different color
-    rand_color = color_palette(len(all_objects))
+    # Color each segment a different color, use a previously saved scale if available
+    rand_color = color_palette(num=len(all_objects), saved=True)
 
     # Plot all segment contours
     for i, cnt in enumerate(all_objects):
diff --git a/plantcv/plantcv/morphology/segment_curvature.py b/plantcv/plantcv/morphology/segment_curvature.py
index c84cadc78..8d038ba6e 100644
--- a/plantcv/plantcv/morphology/segment_curvature.py
+++ b/plantcv/plantcv/morphology/segment_curvature.py
@@ -45,7 +45,8 @@ def segment_curvature(segmented_img, objects):
     eu_lengths = outputs.observations['segment_eu_length']['value']
     path_lengths = outputs.observations['segment_path_length']['value']
     curvature_measure = [float(x / y) for x, y in zip(path_lengths, eu_lengths)]
-    rand_color = color_palette(len(objects))
+    # Create a color scale, use a previously stored scale if available
+    rand_color = color_palette(num=len(objects), saved=True)
 
     for i, cnt in enumerate(objects):
         # Store coordinates for labels
diff --git a/plantcv/plantcv/morphology/segment_euclidean_length.py b/plantcv/plantcv/morphology/segment_euclidean_length.py
index ae846ca38..6ccbb8edd 100644
--- a/plantcv/plantcv/morphology/segment_euclidean_length.py
+++ b/plantcv/plantcv/morphology/segment_euclidean_length.py
@@ -32,7 +32,8 @@ def segment_euclidean_length(segmented_img, objects):
     x_list = []
     y_list = []
     segment_lengths = []
-    rand_color = color_palette(len(objects))
+    # Create a color scale, use a previously stored scale if available
+    rand_color = color_palette(num=len(objects), saved=True)
 
     labeled_img = segmented_img.copy()
     # Store debug
diff --git a/plantcv/plantcv/morphology/segment_id.py b/plantcv/plantcv/morphology/segment_id.py
index d8d065c63..e2bff486b 100644
--- a/plantcv/plantcv/morphology/segment_id.py
+++ b/plantcv/plantcv/morphology/segment_id.py
@@ -36,8 +36,8 @@ def segment_id(skel_img, objects, mask=None):
 
     segmented_img = cv2.cvtColor(segmented_img, cv2.COLOR_GRAY2RGB)
 
-    # Color each segment a different color
-    rand_color = color_palette(len(objects))
+    # Create a color scale, use a previously stored scale if available
+    rand_color = color_palette(num=len(objects), saved=True)
 
     # Plot all segment contours
     for i, cnt in enumerate(objects):
diff --git a/plantcv/plantcv/morphology/segment_insertion_angle.py b/plantcv/plantcv/morphology/segment_insertion_angle.py
index ca67be26a..62030e1ff 100644
--- a/plantcv/plantcv/morphology/segment_insertion_angle.py
+++ b/plantcv/plantcv/morphology/segment_insertion_angle.py
@@ -105,7 +105,8 @@ def segment_insertion_angle(skel_img, segmented_img, leaf_objects, stem_objects,
             label_coord_x.append(leaf_objects[i][0][0][0])
             label_coord_y.append(leaf_objects[i][0][0][1])
 
-    rand_color = color_palette(len(valid_segment))
+    # Create a color scale, use a previously stored scale if available
+    rand_color = color_palette(num=len(valid_segment), saved=True)
 
     for i, cnt in enumerate(valid_segment):
         cv2.drawContours(labeled_img, valid_segment, i, rand_color[i], params.line_thickness, lineType=8)
diff --git a/plantcv/plantcv/morphology/segment_skeleton.py b/plantcv/plantcv/morphology/segment_skeleton.py
index 74e780e8b..5f3307923 100644
--- a/plantcv/plantcv/morphology/segment_skeleton.py
+++ b/plantcv/plantcv/morphology/segment_skeleton.py
@@ -16,19 +16,17 @@ def segment_skeleton(skel_img, mask=None):
     """ Segment a skeleton image into pieces
 
         Inputs:
-        skel_img      = Skeletonized image
-        mask          = (Optional) binary mask for debugging. If provided, debug image will be overlaid on the mask.
+        skel_img         = Skeletonized image
+        mask             = (Optional) binary mask for debugging. If provided, debug image will be overlaid on the mask.
 
         Returns:
         segmented_img       = Segmented debugging image
         segment_objects     = list of contours
-        segment_hierarchies = contour hierarchy list
 
         :param skel_img: numpy.ndarray
         :param mask: numpy.ndarray
         :return segmented_img: numpy.ndarray
         :return segment_objects: list
-        "return segment_hierarchies: numpy.ndarray
         """
 
     # Store debug
@@ -48,8 +46,8 @@ def segment_skeleton(skel_img, mask=None):
     # Reset debug mode
     params.debug = debug
 
-    # Color each segment a different color
-    rand_color = color_palette(len(segment_objects))
+    # Color each segment a different color, do not used a previously saved color scale
+    rand_color = color_palette(num=len(segment_objects), saved=False)
 
     if mask is None:
         segmented_img = skel_img.copy()
diff --git a/plantcv/plantcv/morphology/segment_tangent_angle.py b/plantcv/plantcv/morphology/segment_tangent_angle.py
index 1e3b88b58..89294cb84 100644
--- a/plantcv/plantcv/morphology/segment_tangent_angle.py
+++ b/plantcv/plantcv/morphology/segment_tangent_angle.py
@@ -58,7 +58,8 @@ def segment_tangent_angle(segmented_img, objects, size):
     label_coord_x = []
     label_coord_y = []
 
-    rand_color = color_palette(len(objects))
+    # Create a color scale, use a previously stored scale if available
+    rand_color = color_palette(num=len(objects), saved=True)
 
     for i, cnt in enumerate(objects):
         find_tangents = np.zeros(segmented_img.shape[:2], np.uint8)
diff --git a/tests/tests.py b/tests/tests.py
index f66ba399c..d0f0193bd 100755
--- a/tests/tests.py
+++ b/tests/tests.py
@@ -556,7 +556,7 @@ def test_plantcv_parallel_process_results_invalid_json():
 
 # ####################################################################################################################
 # ########################################### PLANTCV MAIN PACKAGE ###################################################
-matplotlib.use('Template', warn=False)
+matplotlib.use('Template')
 
 TEST_DATA = os.path.join(os.path.dirname(os.path.abspath(__file__)), "data")
 HYPERSPECTRAL_TEST_DATA = os.path.join(os.path.dirname(os.path.abspath(__file__)), "hyperspectral_data")
@@ -1366,21 +1366,23 @@ def test_plantcv_cluster_contours_splitimg():
 
 
 def test_plantcv_color_palette():
-    # Collect assertions
-    truths = []
+    # Return a color palette
+    colors = pcv.color_palette(num=10, saved=False)
+    assert np.shape(colors) == (10, 3)
 
-    # Return one random color
-    colors = pcv.color_palette(1)
-    # Colors should be a list of length 1, containing a tuple of length 3
-    truths.append(len(colors) == 1)
-    truths.append(len(colors[0]) == 3)
 
-    # Return ten random colors
-    colors = pcv.color_palette(10)
-    # Colors should be a list of length 10
-    truths.append(len(colors) == 10)
-    # All of these should be true for the function to pass testing.
-    assert (all(truths))
+def test_plantcv_color_palette_random():
+    # Return a color palette in random order
+    pcv.params.color_sequence = "random"
+    colors = pcv.color_palette(num=10, saved=False)
+    assert np.shape(colors) == (10, 3)
+
+
+def test_plantcv_color_palette_saved():
+    # Return a color palette that was saved
+    pcv.params.saved_color_scale = [[0, 0, 0], [255, 255, 255]]
+    colors = pcv.color_palette(num=2, saved=True)
+    assert colors == [[0, 0, 0], [255, 255, 255]]
 
 
 def test_plantcv_crop():
@@ -5564,13 +5566,17 @@ def test_plantcv_visualize_clustered_contours():
     cluster = [cluster_i[arr_n] for arr_n in cluster_i]
     # Test in print mode
     pcv.params.debug = "print"
+    # Reset the saved color scale (can be saved between tests)
+    pcv.params.saved_color_scale = None
     _ = pcv.visualize.clustered_contours(img=img, grouped_contour_indices=cluster, roi_objects=objs,
                                          roi_obj_hierarchy=obj_hierarchy, nrow=2, ncol=2)
     # Test in plot mode
     pcv.params.debug = "plot"
+    # Reset the saved color scale (can be saved between tests)
+    pcv.params.saved_color_scale = None
     cluster_img = pcv.visualize.clustered_contours(img=img1, grouped_contour_indices=cluster, roi_objects=objs,
                                                    roi_obj_hierarchy=obj_hierarchy)
-    assert len(np.unique(cluster_img)) == 37
+    assert len(np.unique(cluster_img.reshape(-1, cluster_img.shape[2]), axis=0)) == 37
 
 
 def test_plantcv_visualize_colorspaces():