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language:
- en
pipeline_tag: image-classification
license: mit
datasets:
- ssharma2020/Plant-Seedlings-Dataset
metrics:
- accuracy
library_name: keras
tags:
- biology
---
# 🌱 Plant Seedlings Classification — AI for Smarter Agriculture
## 🧩 Overview
Agriculture remains one of the most vital yet labor-intensive industries.
Farmers and agronomists often spend countless hours identifying seedlings, weeds, and crop health manually.
This model leverages **Deep Learning and Computer Vision** to automatically recognize plant species from seedling images —
helping accelerate early-stage crop monitoring and enabling precision agriculture.
---
## 🤖 Model Details
- **Model Type:** CNN-based Image Classifier
- **Framework:** TensorFlow / Keras
- **Dataset:** Plant Seedlings Dataset (12 plant categories)
- **Input:** RGB image of a seedling
- **Output:** Predicted plant species label
---
## 🌾 Why It Matters
✅ Reduces manual effort in plant identification
✅ Boosts accuracy and consistency in seedling detection
✅ Supports sustainable, data-driven agriculture
✅ Enables automation in large-scale farming and greenhouse monitoring
📘 **Full Source Notebook:**
The complete training and evaluation notebook is available on GitHub:
👉 [View on GitHub](https://github.com/joyjitroy/Machine_Learning/blob/main/Bank_Customer_Churn_Prediction_using_Artificial_Neural_Networks.ipynb)
---
## 🚀 Example Usage
```python
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image
import numpy as np
model = load_model("plant_seedlings_model.h5")
img = image.load_img("seedling.jpg", target_size=(128, 128))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0) / 255.0
pred = np.argmax(model.predict(x), axis=1)
print("Predicted plant category:", pred) |