| | --- |
| | 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) |
