---
model_name: Crop Anomaly Detection Model - 3 Class
tags:
  - pytorch
  - resnet50
  - agriculture
  - anomaly-detection
  - wheat
  - plant-disease
license: apache-2.0
library_name: transformers
datasets:
  - Crop-disease-dataset
model_type: resnet50
num_classes: 3
classes:
  - fall armyworm
  - p_def
  - blb
preprocessing:
  resize: 256
  center_crop: 224
  normalize:
    - 0.485
    - 0.456
    - 0.406
  normalize_std:
    - 0.229
    - 0.224
    - 0.225
framework: pytorch
task: image-classification
pipeline_tag: image-classification

---


Wheat Anomaly Detection Model

Overview

This is a ResNet50-based image classification model designed for wheat anomaly detection. It classifies images into three categories:

Fall Armyworm (fa)

Phosphorus Deficiency (p_def)

Bacterial Leaf Blight (blb)

The model has been fine-tuned on an agricultural dataset and optimized for accurate detection of these anomalies.

Model Performance

Validation Accuracy: 93.82%

Class-wise Accuracy:

Fall Armyworm: 100.00%

Phosphorus Deficiency: 86.21%

Bacterial Leaf Blight: 95.00%

Installation

Ensure you have transformers, torch, and gradio installed:

Usage

Here is an example of how to load and use the model for prediction using PyTorch:

Dataset

The model was trained using the Wheat Disease Dataset with balanced classes. Ensure your images are resized to 224x224 and normalized using the provided values.

License

This model is licensed under the Apache-2.0 License. You are free to use, modify, and distribute it under the terms of the license.