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diff --git a/ai_ta_backend/pest-detection-plugin.py b/ai_ta_backend/pest-detection-plugin.py
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+# AIFARMS CropWizard Plugin for Pest Detection and Classification
+# By Aditya Sengupta
+
+# Google Colab Implementation:
+# https://colab.research.google.com/drive/1GO-lw2PJtVewlA-xhBfgBLId8v4v-BE2?usp=sharing
+
+# Import the required libraries
+from PIL import Image
+from ultralytics import YOLO
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.image as mpimg
+
+# Load a custom trained YOLOv8n model for pest detection and classification
+# The model I trained can be found below:
+# https://www.dropbox.com/scl/fi/xf8wi0jy72kuk3xl47dnx/Aditya-Pest-Detection-YOLO-V1.pt
+
+model = YOLO('/Users/adityasengupta/Downloads/pest-detection/Aditya-Pest-Detection-YOLO-V1.pt')
+
+# If an image of a pest is input into CropWizard, it can call the below function to get the annotated image with bounding boxes and class labels
+
+def pest_detection_plugin(image_path):
+    # Run inference
+    results = model(image_path)  # results object with inference results
+    
+    annotated_images = []
+
+   # Show the results (multiple will be returned if there are multiple images)
+    for r in results[0]:
+        im_array = r.plot()  # plot a BGR numpy array of predictions
+        im = Image.fromarray(im_array[..., ::-1])  # RGB PIL image (annotated with bounding boxes and class labels)
+        annotated_images.append(im)
+    
+    # See Ultralytics YOLOv8 documentation for more details on extracting other information from the model results object
+    # https://docs.ultralytics.com/modes/predict/
+
+    return annotated_images
+
+# Sample usage with a web image
+image_url = 'https://www.arborday.org/trees/health/pests/images/figure-japanese-beetle-3.jpg'
+output = pest_detection_plugin(image_url)
+
+# Print annotated images
+for image in output:
+    image.show()
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