diff --git a/src/deepstream/helpers/load_class_labels.py b/src/deepstream/helpers/load_class_labels.py
new file mode 100644
index 0000000..1b34721
--- /dev/null
+++ b/src/deepstream/helpers/load_class_labels.py
@@ -0,0 +1,11 @@
+from typing import List
+
+PLANT_LABELS = "/workspace/configs/crop_and_weed_83_classes.txt"
+
+# Load class labels
+def load_class_labels() -> List[str]:
+    try:
+        with open(PLANT_LABELS, "r") as f:
+            return [line.strip() for line in f.readlines()]
+    except:
+        return [f"class_{i}" for i in range(83)]
diff --git a/src/deepstream/helpers/plant_msg_meta_builder.py b/src/deepstream/helpers/plant_msg_meta_builder.py
new file mode 100644
index 0000000..e886768
--- /dev/null
+++ b/src/deepstream/helpers/plant_msg_meta_builder.py
@@ -0,0 +1,68 @@
+import sys
+from pydantic import BaseModel
+
+import gi
+gi.require_version("Gst", "1.0")
+
+from gi.repository import Gst
+import pyds
+
+from src.deepstream.helpers.softmax_topk_classifier import ClassificationPrediction
+
+class PlantEvent(BaseModel):
+    frame_id: int
+    plant_id: str
+    prediction: ClassificationPrediction
+
+
+class NvdsPlantEventBuilder:
+    """
+    For the reference, please check
+    https://github.com/NVIDIA-AI-IOT/deepstream_python_apps/blob/9b27f02ffea46a3ded2ad26b3eea27ef3e2dfded/apps/deepstream-test4/deepstream_test_4.py
+    """
+
+    def build(self, batch_meta, frame_meta, event: PlantEvent) -> None:
+        user_event_meta = pyds.nvds_acquire_user_meta_from_pool(batch_meta)
+        msg_meta = pyds.NvDsEventMsgMeta.cast(pyds.alloc_nvds_event_msg_meta(user_event_meta))
+
+        # Generic IDs / bookkeeping
+        msg_meta.frameId = event.frame_id
+        msg_meta.objectId = event.plant_id
+        msg_meta.trackingId = event.frame_id
+        msg_meta.objClassId = event.prediction.class_id
+        msg_meta.confidence = float(event.prediction.confidence)
+
+        # Link to sensor0 / place0 / analytics0 from msgconv config
+        msg_meta.sensorId = 0
+        msg_meta.placeId = 0
+        msg_meta.moduleId = 0
+
+        msg_meta.type = pyds.NvDsEventType.NVDS_EVENT_CUSTOM
+        msg_meta.objType = pyds.NvDsObjectType.NVDS_OBJECT_TYPE_PERSON
+
+        # Timestamp
+        msg_meta.ts = pyds.alloc_buffer(32)
+        pyds.generate_ts_rfc3339(msg_meta.ts, 32)
+
+        # Dummy bbox (positive; you can plug in real ROIs later)
+        msg_meta.bbox.top = -1
+        msg_meta.bbox.left = -1
+        msg_meta.bbox.width = 0
+        msg_meta.bbox.height = 0
+
+        # Attach person object repurposed to carry plant info
+        obj = pyds.alloc_nvds_person_object()
+        person = pyds.NvDsPersonObject.cast(obj)
+
+        person.age = 0
+        person.gender = "plant"
+        person.hair = "none"
+        person.cap = "none"
+        person.apparel = event.prediction.class_name
+
+        msg_meta.extMsg = obj
+        msg_meta.extMsgSize = sys.getsizeof(pyds.NvDsPersonObject)
+
+        user_event_meta.user_meta_data = msg_meta
+        user_event_meta.base_meta.meta_type = pyds.NvDsMetaType.NVDS_EVENT_MSG_META
+        pyds.nvds_add_user_meta_to_frame(frame_meta, user_event_meta)
diff --git a/src/deepstream/helpers/remove_background.py b/src/deepstream/helpers/remove_background.py
new file mode 100644
index 0000000..f5e08d3
--- /dev/null
+++ b/src/deepstream/helpers/remove_background.py
@@ -0,0 +1,23 @@
+import numpy as np
+import cv2
+
+MORPH_KERNEL = 34
+
+def remove_background(frame_bgr: np.ndarray) -> np.ndarray:
+    rgb = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB).astype(np.float32)
+    R, G, B = rgb[..., 0], rgb[..., 1], rgb[..., 2]
+
+    exg = 2.0 * G - R - B
+    exg_norm = cv2.normalize(exg, None, 0, 255, cv2.NORM_MINMAX).astype(np.uint8)
+
+    _, mask = cv2.threshold(exg_norm, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
+
+    kernel = cv2.getStructuringElement(
+        cv2.MORPH_ELLIPSE,
+        (MORPH_KERNEL, MORPH_KERNEL)
+    )
+    mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)
+
+    out = np.zeros_like(frame_bgr)
+    out[mask > 0] = frame_bgr[mask > 0]
+    return out
diff --git a/src/deepstream/pipelines/deepstream_pipeline_cpu.py b/src/deepstream/pipelines/deepstream_pipeline_cpu.py
new file mode 100644
index 0000000..9f4b15b
--- /dev/null
+++ b/src/deepstream/pipelines/deepstream_pipeline_cpu.py
@@ -0,0 +1,134 @@
+import os
+from datetime import datetime
+from typing import Any, Dict, List
+import gi
+import numpy as np
+import cv2
+import pyds
+
+gi.require_version("Gst", "1.0")
+from gi.repository import GLib, Gst
+
+from src.frame_comparison.frame_change_detector import FrameChangeDetector
+from src.model_conversion.onnx_to_trt import build_engine_if_missing
+
+from src.deepstream.helpers.load_class_labels import load_class_labels
+from src.deepstream.helpers.meta_tensor_extractor import TensorExtractor
+from src.deepstream.helpers.softmax_topk_classifier import ClassificationPrediction, SoftmaxTopKClassifier
+from src.deepstream.helpers.plant_msg_meta_builder import NvdsPlantEventBuilder, PlantEvent
+from src.deepstream.helpers.pipeline_runner import run_pipeline
+
+from src.deepstream.probes.background_removal.cpu.background_removal_probe import remove_background_probe
+from src.deepstream.probes.db_message_meta_probe import DbMessageMetaProbe
+from src.deepstream.probes.frame_comparison.cpu.frame_skipping_probe import frame_skip_probe
+
+# Configuration
+RTSP_PORT = os.environ.get("RTSP_PORT", "8554")
+RTSP_URL = f"rtsp://127.0.0.1:{RTSP_PORT}/test"
+
+CONFIG_FILE: str = "/workspace/configs/resnet18.txt"
+MSGCONV_CONFIG: str = "/workspace/configs/nvmsgbroker_msgconv_config.txt"
+MQTT_CONN_STR = "172.17.0.1;1883;agstream-client"
+MQTT_TOPIC = "deepstream/predictions"
+
+CLASS_LABELS = load_class_labels()
+
+def build_pipeline() -> Gst.Pipeline:
+    """Build DeepStream pipeline with background removal, frame skipping, and message broker."""
+    pipeline = Gst.Pipeline.new("final-cpu-pipeline")
+
+    # Elements
+    rtspsrc = Gst.ElementFactory.make("rtspsrc", "source")
+    depay = Gst.ElementFactory.make("rtph264depay", "depay")
+    parse = Gst.ElementFactory.make("h264parse", "parse")
+    decode = Gst.ElementFactory.make("decodebin", "decode")
+    convert = Gst.ElementFactory.make("videoconvert", "convert")
+    nvvideoconvert = Gst.ElementFactory.make("nvvideoconvert", "nvvideoconvert")
+    capsfilter = Gst.ElementFactory.make("capsfilter", "capsfilter")
+    streammux = Gst.ElementFactory.make("nvstreammux", "streammux")
+    nvinfer = Gst.ElementFactory.make("nvinfer", "nvinfer")
+    nvmsgconv = Gst.ElementFactory.make("nvmsgconv", "nvmsgconv")
+    nvmsgbroker = Gst.ElementFactory.make("nvmsgbroker", "nvmsgbroker")
+
+    for e in [
+        rtspsrc,
+        depay,
+        parse,
+        decode,
+        convert,
+        nvvideoconvert,
+        capsfilter,
+        streammux,
+        nvinfer,
+        nvmsgconv,
+        nvmsgbroker,
+    ]:
+        assert e is not None, f"Failed to create element {e}"
+        pipeline.add(e)
+
+    # Configure elements
+    rtspsrc.set_property("location", RTSP_URL)
+    rtspsrc.set_property("latency", 200)
+    streammux.set_property("batch-size", 1)
+    streammux.set_property("width", 256)
+    streammux.set_property("height", 256)
+    nvinfer.set_property("config-file-path", CONFIG_FILE)
+    caps = Gst.Caps.from_string("video/x-raw(memory:NVMM), format=RGBA")
+    capsfilter.set_property("caps", caps)
+    nvmsgconv.set_property("config", MSGCONV_CONFIG)
+    nvmsgconv.set_property("payload-type", 0)
+    nvmsgbroker.set_property("proto-lib", "/opt/nvidia/deepstream/deepstream-6.4/lib/libnvds_mqtt_proto.so")
+    nvmsgbroker.set_property("conn-str", MQTT_CONN_STR)
+    nvmsgbroker.set_property("topic", MQTT_TOPIC)
+    nvmsgbroker.set_property("sync", False)
+
+    # Dynamic pad linking
+    def on_pad_added_rtspsrc(src: Any, pad: Any) -> None:
+        sinkpad = depay.get_static_pad("sink")
+        if not sinkpad.is_linked():
+            pad.link(sinkpad)
+
+    rtspsrc.connect("pad-added", on_pad_added_rtspsrc)
+
+    def on_pad_added_decode(src: Any, pad: Any) -> None:
+        sinkpad = convert.get_static_pad("sink")
+        if not sinkpad.is_linked():
+            pad.link(sinkpad)
+
+    decode.connect("pad-added", on_pad_added_decode)
+
+    # Link capsfilter → streammux
+    depay.link(parse)
+    parse.link(decode)
+    convert.link(nvvideoconvert)
+    nvvideoconvert.link(capsfilter)
+    srcpad = capsfilter.get_static_pad("src")
+    sinkpad = streammux.get_request_pad("sink_0")
+    srcpad.link(sinkpad)
+
+    streammux.link(nvinfer)
+    nvinfer.link(nvmsgconv)
+    nvmsgconv.link(nvmsgbroker)
+
+    frame_change_detector = FrameChangeDetector()
+
+    streammux_src_pad = streammux.get_static_pad("src")
+    streammux_src_pad.add_probe(Gst.PadProbeType.BUFFER, frame_skip_probe, frame_change_detector)
+
+    streammux_src_pad.add_probe(Gst.PadProbeType.BUFFER, remove_background_probe)
+
+    tensor_extractor = TensorExtractor()
+    classifier = SoftmaxTopKClassifier(CLASS_LABELS)
+    plant_event_builder = NvdsPlantEventBuilder()
+    db_message_meta_probe = DbMessageMetaProbe(tensor_extractor, classifier, plant_event_builder)
+
+    nvinfer_src_pad = nvinfer.get_static_pad("src")
+    nvinfer_src_pad.add_probe(Gst.PadProbeType.BUFFER, db_message_meta_probe.pad_probe)
+
+    return pipeline
+
+if __name__ == "__main__":
+    Gst.init(None)
+    build_engine_if_missing(CONFIG_FILE)
+    pipeline = build_pipeline()
+    run_pipeline(pipeline)
diff --git a/src/deepstream/pipelines/nvmsgbroker_pipeline.py b/src/deepstream/pipelines/nvmsgbroker_pipeline.py
index 45d7e7a..606e9b0 100644
--- a/src/deepstream/pipelines/nvmsgbroker_pipeline.py
+++ b/src/deepstream/pipelines/nvmsgbroker_pipeline.py
@@ -10,6 +10,7 @@
 gi.require_version("Gst", "1.0")
 
 
+from src.deepstream.helpers.load_class_labels import load_class_labels
 from src.deepstream.helpers.meta_tensor_extractor import TensorExtractor
 from src.deepstream.helpers.softmax_topk_classifier import ClassificationPrediction, SoftmaxTopKClassifier
 from src.model_conversion.onnx_to_trt import build_engine_if_missing
@@ -19,17 +20,6 @@
 
 CONFIG_FILE = "/workspace/configs/mobilenet.txt"
 MSGCONV_CONFIG = "/workspace/configs/nvmsgbroker_msgconv_config.txt"
-CLASS_LABELS_FILE = "/workspace/configs/crop_and_weed_83_classes.txt"
-
-
-def load_class_labels() -> List[str]:
-    try:
-        with open(CLASS_LABELS_FILE) as f:
-            return [line.strip() for line in f]
-    except Exception:
-        # Fallback to dummy labels if file is missing
-        return [f"class_{i}" for i in range(83)]
-
 
 CLASS_LABELS = load_class_labels()
 
diff --git a/src/deepstream/pipelines/pipeline_onnx_real_input.py b/src/deepstream/pipelines/pipeline_onnx_real_input.py
index 2e09ae5..c0ea1bb 100644
--- a/src/deepstream/pipelines/pipeline_onnx_real_input.py
+++ b/src/deepstream/pipelines/pipeline_onnx_real_input.py
@@ -8,21 +8,12 @@
 gi.require_version("Gst", "1.0")
 from gi.repository import GLib, Gst
 
+from src.deepstream.helpers.load_class_labels import load_class_labels
 from src.deepstream.helpers.meta_tensor_extractor import TensorExtractor
 from src.deepstream.helpers.softmax_topk_classifier import (
     SoftmaxTopKClassifier,
 )
 
-CLASS_LABELS_FILE = "/workspace/configs/crop_and_weed_83_classes.txt"
-
-
-def load_class_labels() -> List[str]:
-    try:
-        with open(CLASS_LABELS_FILE) as f:
-            return [line.strip() for line in f]
-    except Exception:
-        return [f"class_{i}" for i in range(83)]
-
 
 def list_image_files(folder: str) -> List[str]:
     exts = (".jpg", ".jpeg", ".png")
diff --git a/src/deepstream/pipelines/pipeline_onnx_test.py b/src/deepstream/pipelines/pipeline_onnx_test.py
index 787902c..9407b17 100755
--- a/src/deepstream/pipelines/pipeline_onnx_test.py
+++ b/src/deepstream/pipelines/pipeline_onnx_test.py
@@ -8,6 +8,7 @@
 import pyds
 from gi.repository import Gst  # noqa: E402
 
+from src.deepstream.helpers.load_class_labels import load_class_labels
 from src.deepstream.helpers.meta_tensor_extractor import TensorExtractor
 from src.deepstream.helpers.softmax_topk_classifier import (
     ClassificationPrediction,
@@ -15,18 +16,6 @@
 )
 from src.model_conversion.onnx_to_trt import build_engine_if_missing
 
-CLASS_LABELS_FILE = "/workspace/configs/crop_and_weed_83_classes.txt"
-
-
-def load_class_labels() -> List[str]:
-    try:
-        with open(CLASS_LABELS_FILE) as f:
-            return [line.strip() for line in f]
-    except Exception:
-        # Fallback to dummy labels if file is missing
-        return [f"class_{i}" for i in range(83)]
-
-
 CLASS_LABELS = load_class_labels()
 
 # Reuse the tensor extractor + softmax top-k classifier from your RTSP pipeline
diff --git a/src/deepstream/probes/background_removal/cpu/background_removal_probe.py b/src/deepstream/probes/background_removal/cpu/background_removal_probe.py
new file mode 100644
index 0000000..4ba4391
--- /dev/null
+++ b/src/deepstream/probes/background_removal/cpu/background_removal_probe.py
@@ -0,0 +1,26 @@
+import cv2
+import numpy as np
+import pyds
+from gi.repository import Gst
+
+from src.deepstream.helpers.remove_background import remove_background
+
+
+def remove_background_probe(pad: Gst.Pad, info: Gst.PadProbeInfo) -> Gst.PadProbeReturn:
+    gst_buffer = info.get_buffer()
+    if not gst_buffer:
+        return Gst.PadProbeReturn.OK
+
+    try:
+        surface = pyds.get_nvds_buf_surface(hash(gst_buffer), 0)
+        frame_rgba = np.array(surface, copy=False, order="C")
+        frame_bgr = cv2.cvtColor(frame_rgba, cv2.COLOR_RGBA2BGR)
+
+        masked_bgr = remove_background(frame_bgr)
+        frame_rgba_new = cv2.cvtColor(masked_bgr, cv2.COLOR_BGR2RGBA)
+
+        np.copyto(frame_rgba, frame_rgba_new)
+    except Exception as e:
+        raise RuntimeError("BackgroundRemovalProbe: failed to fetch/map NvBufSurface") from e
+
+    return Gst.PadProbeReturn.OK
diff --git a/src/deepstream/probes/db_message_meta_probe.py b/src/deepstream/probes/db_message_meta_probe.py
new file mode 100644
index 0000000..0edf235
--- /dev/null
+++ b/src/deepstream/probes/db_message_meta_probe.py
@@ -0,0 +1,57 @@
+from typing import Any, List
+import pyds
+from gi.repository import Gst
+
+from src.deepstream.helpers.meta_tensor_extractor import TensorExtractor
+from src.deepstream.helpers.softmax_topk_classifier import ClassificationPrediction, SoftmaxTopKClassifier
+from src.deepstream.helpers.plant_msg_meta_builder import NvdsPlantEventBuilder, PlantEvent
+
+
+class DbMessageMetaProbe:
+    def __init__(self, tensor_extractor : TensorExtractor, classifier: SoftmaxTopKClassifier, plant_event_builder: NvdsPlantEventBuilder):
+        self.tensor_extractor = tensor_extractor
+        self.classifier = classifier
+        self.plant_event_builder = plant_event_builder
+
+    def extract_tensor_output(self, frame_meta) -> List[ClassificationPrediction]:
+        tensor_meta = self.tensor_extractor.find_tensor_meta(frame_meta)
+        if tensor_meta is None:
+            return []
+
+        logits = self.tensor_extractor.extract_logits(tensor_meta)
+        return self.classifier.predict_from_logits(logits)
+
+    def attach_msgmeta(self, batch_meta, frame_meta, frame_number: int, predictions: List[ClassificationPrediction]) -> None:
+        best = predictions[0]
+        event = PlantEvent(
+            frame_id=frame_number,
+            plant_id=f"plant-{frame_number}",
+            prediction=best,
+        )
+        self.plant_event_builder.build(batch_meta, frame_meta, event)
+
+    def pad_probe(self, pad: Gst.Pad, info: Gst.PadProbeInfo) -> Gst.PadProbeReturn:
+        buffer = info.get_buffer()
+        if not buffer:
+            return Gst.PadProbeReturn.OK
+
+        batch_meta = pyds.gst_buffer_get_nvds_batch_meta(hash(buffer))
+        l_frame = batch_meta.frame_meta_list
+
+        while l_frame:
+            frame_meta = pyds.NvDsFrameMeta.cast(l_frame.data)
+            frame_number = frame_meta.frame_num
+
+            predictions = self.extract_tensor_output(frame_meta)
+            if predictions:
+                top1 = predictions[0]
+                print(
+                    f"[frame {frame_number}] "
+                    f"plant='{top1.class_name}' "
+                    f"(id={top1.class_id}, conf={top1.confidence:.3f})"
+                )
+                self.attach_msgmeta(batch_meta, frame_meta, frame_number, predictions)
+
+            l_frame = l_frame.next
+
+        return Gst.PadProbeReturn.OK
diff --git a/src/deepstream/probes/frame_comparison/cpu/frame_skipping_probe.py b/src/deepstream/probes/frame_comparison/cpu/frame_skipping_probe.py
index f13c689..8f769b7 100644
--- a/src/deepstream/probes/frame_comparison/cpu/frame_skipping_probe.py
+++ b/src/deepstream/probes/frame_comparison/cpu/frame_skipping_probe.py
@@ -1,7 +1,3 @@
-import sys
-import os
-sys.path.append(os.path.join(os.path.dirname(__file__), '../../../..'))
-
 from typing import Any
 import gi
 import numpy as np