metadata
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
🚀 Example Usage
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)