@@ -475,6 +475,37 @@ def __iter__(self) -> Iterator["Image"]:
         """Iterate over image planes."""
         return self.planes()
 
+    def __array__(self, dtype: Dtype | None = None, copy: bool | None = None) -> np.ndarray:
+        """
+        Convert to a plain NumPy array
+
+        :param dtype: dtype of the returned array, defaults to the image's own dtype
+        :type dtype: numpy dtype, optional
+        :param copy: force a copy of the underlying data, defaults to None
+        :type copy: bool, optional
+        :return: image pixel data
+        :rtype: ndarray(H,W) or ndarray(H,W,3)
+
+        Implements the NumPy array protocol, used by ``np.asarray(img)`` and by
+        any NumPy/SciPy/Matplotlib function that isn't ufunc- or array-function-
+        aware (those instead go through :meth:`__array_ufunc__` /
+        :meth:`__array_function__`) and calls ``np.asarray()`` internally on
+        whatever it's given. Without this, such a call would silently coerce an
+        :class:`Image` into a useless 0-d object array instead of its pixel data.
+
+        Returns the same read-only view as the :attr:`array` property unless a
+        dtype conversion or an explicit copy is requested, either of which
+        produces a new, independent (writeable) array.
+
+        :seealso: :attr:`array`
+        """
+        arr = self.array
+        if dtype is not None:
+            arr = arr.astype(dtype)
+        if copy:
+            arr = arr.copy()
+        return arr
+
     def __array_ufunc__(self, ufunc, method: str, *inputs, **kwargs):
         """
         Support NumPy ufunc calls on image data.
 
@@ -115,6 +115,35 @@ def T(self) -> "Kernel":
     def shape(self) -> tuple[int, int]:
         return self.K.shape
 
+    @property
+    def ndim(self) -> int:
+        return 2
+
+    def __array__(self, dtype: Dtype | None = None, copy: bool | None = None) -> np.ndarray:
+        """
+        Convert to a plain NumPy array
+
+        :param dtype: dtype of the returned array, defaults to the kernel's own dtype
+        :type dtype: numpy dtype, optional
+        :param copy: force a copy of the underlying data, defaults to None
+        :type copy: bool, optional
+        :return: kernel weighting matrix
+        :rtype: ndarray(N,M)
+
+        Implements the NumPy array protocol, used by ``np.asarray(K)`` and by
+        any NumPy/SciPy/Matplotlib function that calls ``np.asarray()``
+        internally on whatever it's given (e.g. ``scipy.signal.convolve2d``,
+        ``Axes3D.plot_surface``, ``np.linalg.svd``). Without this, such a call
+        would silently coerce a :class:`Kernel` into a useless 0-d object
+        array instead of its weighting matrix.
+        """
+        arr = self.K
+        if dtype is not None:
+            arr = arr.astype(dtype)
+        if copy:
+            arr = arr.copy()
+        return arr
+
     def print(
         self, fmt: str | None = None, separator: str = " ", precision: int = 2
     ) -> None:
 
@@ -67,6 +67,51 @@ def test_array_ufunc_out_rejected(self):
         ):
             np.ceil(im, out=im)
 
+    def test_array_dunder_asarray(self):
+        """Regression test: np.asarray(img) used to silently coerce to a
+        useless 0-d object array (no __array__ implemented) instead of
+        exposing the pixel data -- same bug class found in Kernel's
+        missing __array__."""
+        im = Image([[1, 2], [3, 4]], dtype="uint8")
+
+        arr = np.asarray(im)
+
+        self.assertEqual(arr.shape, im.array.shape)
+        nt.assert_array_equal(arr, im.array)
+
+    def test_array_dunder_scipy_interop(self):
+        """Regression test: functions that aren't ufunc-/array-function-
+        aware (e.g. scipy.signal.convolve2d) call np.asarray() internally
+        and used to receive a useless 0-d object array."""
+        from scipy.signal import convolve2d
+
+        im = Image.Random(size=(10, 10), dtype="float64")
+
+        out = convolve2d(im, np.ones((3, 3)))
+
+        self.assertEqual(out.ndim, 2)
+
+    def test_array_dunder_dtype_conversion(self):
+        im = Image([[1, 2], [3, 4]], dtype="uint8")
+
+        arr = np.asarray(im, dtype="float32")
+
+        self.assertEqual(arr.dtype, np.float32)
+
+    def test_array_dunder_returns_readonly_view_by_default(self):
+        im = Image([[1, 2], [3, 4]], dtype="uint8")
+
+        arr = np.asarray(im)
+
+        self.assertFalse(arr.flags.writeable)
+
+    def test_array_dunder_copy_true_returns_writeable(self):
+        im = Image([[1, 2], [3, 4]], dtype="uint8")
+
+        arr = np.array(im, copy=True)
+
+        self.assertTrue(arr.flags.writeable)
+
     def test_pixel(self):
         im = Image(np.arange(80).reshape((10, 8)), dtype="int64")  # 8x10 image
         pix = im.pixel(5, 6)
 
@@ -174,6 +174,29 @@ def test_kernel_shape_property(self):
         K = self.Kernel.Gauss(sigma=2, h=3)
         self.assertEqual(K.shape, (7, 7))
 
+    def test_kernel_ndim_property(self):
+        K = self.Kernel.Gauss(sigma=2, h=3)
+        self.assertEqual(K.ndim, 2)
+
+    def test_kernel_array_protocol(self):
+        """Regression test: Kernel objects used to silently coerce to a
+        useless 0-d object array under np.asarray()/SciPy/Matplotlib calls
+        that aren't ufunc-aware, instead of exposing the real weight
+        matrix -- e.g. scipy.signal.convolve2d raised "inputs must both be
+        2-D arrays" and np.linalg.svd raised "0-dimensional array given"."""
+        K = self.Kernel.Box(2, normalize=False)
+        arr = np.asarray(K)
+        self.assertEqual(arr.shape, K.shape)
+        nt.assert_array_equal(arr, K.K)
+
+        U, s, Vh = np.linalg.svd(K, full_matrices=True)
+        self.assertEqual(U.shape[0], K.shape[0])
+
+        from scipy.signal import convolve2d
+
+        out = convolve2d(K, self.Kernel.Box(1, normalize=False))
+        self.assertEqual(out.ndim, 2)
+
     def test_kernel_box(self):
         K = self.Kernel.Box(2)
         self.assertEqual(K.shape, (5, 5))