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---
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language:
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- en
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thumbnail: "/assets/image.jpg"
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tags:
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- image-classification
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- computer-vision
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- agriculture
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- maize-diseases
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license: mit
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datasets:
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- smaranjitghose/corn-or-maize-leaf-disease-dataset
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metrics:
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- accuracy
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pipeline_tag: image-classification
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---
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# Maize Disease Detection Model
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This model is designed to detect diseases in maize (corn) leaves using computer vision techniques.
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## Model description
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The Maize Disease Detection Model is a convolutional neural network (CNN) trained to classify images of maize leaves into four categories: Healthy, Gray Leaf Spot, Blight, and Common Rust. It aims to assist farmers and agricultural professionals in quickly identifying common maize diseases, potentially leading to earlier interventions and improved crop management.
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### Intended uses & limitations
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The model is intended for use as a diagnostic tool to assist in the identification of maize leaf diseases. It should be used in conjunction with expert knowledge and not as a sole means of diagnosis. The model's performance may vary depending on image quality, lighting conditions, and the presence of diseases or conditions not included in the training dataset.
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**Limitations:**
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- The model is trained on a specific dataset and may not generalize well to significantly different growing conditions or maize varieties.
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- It is not designed to detect diseases other than the four categories it was trained on.
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- Performance on images with multiple diseases present has not been extensively tested.
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- The model should not be used as a replacement for professional agricultural advice.
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### How to use
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Here's a basic example of how to use the model:
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```python
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import tensorflow as tf
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from PIL import Image
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import numpy as np
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import json
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# Load the model
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model = tf.keras.models.load_model('saved_model.pb')
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# Load and preprocess the image
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img = Image.open('image.jpg')
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img = img.resize((224, 224)) # Adjust size as needed
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img_array = np.array(img) / 255.0 # Normalize pixel values
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img_array = np.expand_dims(img_array, axis=0) # Add batch dimension
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# Make prediction
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prediction = model.predict(img_array)
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# Load class names
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with open('classes.json', 'r') as f:
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class_names = json.load(f)
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# Get the predicted class
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predicted_class = class_names[str(np.argmax(prediction[0]))]
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print(f"Predicted class: {predicted_class}")
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```
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### Training data
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The model was trained on a dataset derived from the PlantVillage and PlantDoc datasets, specifically curated for maize leaf diseases. The dataset consists of:
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- Common Rust: 1306 images
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- Gray Leaf Spot: 574 images
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- Blight: 1146 images
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- Healthy: 1162 images
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Total images: 4188
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The original dataset can be found on Kaggle: [Corn or Maize Leaf Disease Dataset](https://www.kaggle.com/datasets/smaranjitghose/corn-or-maize-leaf-disease-dataset)
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## Ethical considerations
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- The model's predictions should not be used as the sole basis for agricultural decisions that may impact food security or farmers' livelihoods.
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- There may be biases in the training data that could lead to reduced performance for certain maize varieties or growing conditions not well-represented in the dataset.
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- Users should be made aware of the model's limitations and the importance of expert validation.
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Additionally, please credit the original authors of the PlantVillage and PlantDoc datasets, as this model's training data is derived from their work.
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## Model Card Authors
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Grey
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## Model Card Contact
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eligapris |
