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README.md

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+Agriculture Pest Detection
+by Oscar Mendoza
+
+Model Description:
+The model I worked on is a computer vision model that uses object detection to help identify common agricultural pest in crop images.
+The model was trained to identify six different insects and put them into classes. Those classes were ants, aphids, beetles, caterpillars,
+grasshoppers, and weevils. The model was trained using Yolo V11 for its fast and accurate object detection.
+The model aims to support early pest detection in agriculture from images captured in field conditions like plants, soil, and foliage
+
+Training Data: 
+The dataset was found called Pest Detection Computer Vision Model 
+https://universe.roboflow.com/sams-sgift/pest-detection-qbalv
+The dataset originally came with 7.3k images, but I narrowed it down to 2.5k images
+Each class was pretty balanced for images, with ahids being the only one not in the 400s for photos
+Below are the images per class:
+Ant: 497 images
+Aphid: 283 images
+Beetle: 413 images
+Caterpillar: 409 images
+Grasshopper: 483
+Weevil: 480 images
+Classes were reduced from 19 to 6 for better focus, classes were selected on how common its found in crops, and relevance to agriculture.
+Annotation Process: 
+Dataset was reviewed and cleaned using roboflow, approximately reveiwed 50 images for annotations. Corrections like misidentified labels, or
+innacurate bounding boxes, removing images that didn't match any of the classes.
+Train: 70%
+Validation: 20%
+Split: 10%
+
+No data augmentation was applied.
+Link to my annotated refined dataset:
+https://app.roboflow.com/oscars-space-teemy/pest-detection-qbalv-a6hpf/models/pest-detection-qbalv-a6hpf/3
+Limitation in my dataset: 
+Some insects such as aphids are small and difficult to detect, natural backgrounds can make insects harder to find due to blending in with its
+environment. Dataset also may not represent all agricultural environments.
+
+Training Procedure:
+Framework: Ultralytics YOLOv11
+Hardware: Using Google Colab on A100 GPU runtime
+Epochs: 50
+Image size: 640x640
+
+Evaluation Results: 
+Overall Metrics
+mAP50: 0.676
+mAP50-95: 0.345
+Precision: 0.726
+Recall: 0.659
+
+Per Class Performance (Average Precision):
+Ant: 0.263
+Aphid: 0.176
+Beetle: 0.370
+Caterpillar: 0.172
+Grasshopper: 0.420
+Weevil: 0.675
+
+
+![val_batch0_pred (1)](https://cdn-uploads.huggingface.co/production/uploads/69b8f1e6906a9f3f1426ea5d/8rc-64dggsCYx4AAsFdoD.jpeg)
+
+![val_batch1_pred](https://cdn-uploads.huggingface.co/production/uploads/69b8f1e6906a9f3f1426ea5d/bOIqOUlM_5207Vxgof_bi.jpeg)
+
+![results (1)](https://cdn-uploads.huggingface.co/production/uploads/69b8f1e6906a9f3f1426ea5d/X4BmdYzMRiHII2pmQI7Pj.png)
+
+In my results image, you can see that the traning and validation loss curves decrease over time, meaning that the model is successfully learning
+and improving its prection across epochs. Precision and recall both increase steadily through training, meaning most predictions are correct,
+and the model detects a majority but not all of pests. Overall the trends show the model is learning effectively, but there are limitations,
+especially when it comes to detecting smaller objects.
+
+
+![confusion_matrix_normalized](https://cdn-uploads.huggingface.co/production/uploads/69b8f1e6906a9f3f1426ea5d/Uf7SIKnzgaFElGJubn8yN.png)
+
+My confusion matrix shows a nice diagnol line showing that most objects are getting detected and detected correctly, but we also see that 
+some of the best are getting confused with the background.
+
+
+![BoxF1_curve (1)](https://cdn-uploads.huggingface.co/production/uploads/69b8f1e6906a9f3f1426ea5d/-XLF9LP28q3laPRwhfbAJ.png)
+
+My F1- Confidecne Curve shows how the model's F1 score changes with different confidence thresholds for each class. We see weevil performing
+the best and maintaing a high F1 score (around 0.9) across a wide range of thresholds, while aphid and caterpillar perform the worst, with
+lower F1 scores, meaning there is some difficult detecting these two classes.
+
+Performance Analysis:
+Overall, the model performs well across most classes. The best beeing the Weevil with a 0.675 AP and lowest being Caterpillar at 0.172 AP. Some
+issues that I saw when looking at the performance was small insects like aphids were frequently missed, and some insects blend into natural 
+backgrounds, as well as some visual similarity between weevils and beetles. The recall score being 65.9% means that pest are being detected but 
+a significant portion are still missed, which wouldnt be the best for a solo agriculture detection system.
+
+Limitations and Biases: 
+Poor performing classes would include the aphids and caterpillars, since aphids are typically green, you can't expect the model to identify the 
+aphid on green backgrounds. As for caterpillars, they are always changing their appearance based on the environment which would make it harder
+for the model to detect them. As far as a dataset bias, the dataset isn't as diverse as it could be with most photos having a green background,
+instead of showing a birds view of the insect on a white background to better detect. The model shouldn't be used as a fully automated pest
+detection system since it cannot fully identify the objects. It would also not be suitable for anything that doesn't have human supervision, since
+it would most likely make a mistake or not detect an insect.