File size: 1,344 Bytes
5a1aeed
 
 
 
 
 
6ad4590
5a1aeed
 
 
 
3c7a8b5
 
5a1aeed
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3c7a8b5
5a1aeed
 
 
 
 
 
3c7a8b5
5a1aeed
 
 
 
 
3c7a8b5
5a1aeed
3c7a8b5
6ad4590
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
---
title: Plant Disease Detector
emoji: πŸƒ
colorFrom: green
colorTo: green
sdk: gradio
sdk_version: 5.34.2
app_file: app.py
pinned: false
---

# πŸƒ Plant Disease Detector

This is a Gradio-based web app for detecting fruit and leaf diseases using an EfficientNetB0-based CNN model.

🧠 **Model Overview**
- **Architecture:** EfficientNetB0 (transfer learning)
- **Input Size:** 160Γ—160 RGB
- **Classes:** 21 fruit/leaf disease types (mango, guava, lime, pomegranate)
- **Framework:** TensorFlow / Keras
- **Performance:** ~99.5% train accuracy, ~98.5% validation accuracy

πŸ–ΌοΈ **How to Use**
- Upload a fruit/leaf image via the interface
- The model will return the predicted disease name and confidence %
- You can also try it using the provided example images

πŸ“‚ **Example Images**
Sample images are available in the `examples/` folder:
- `Phytopthora.jpg`
- `RedRust.jpg`
- `HealthyMangoLeaf.jpg`
- `LimeLeafSpotted.jpg`

πŸ› οΈ **Dependencies**
All dependencies are listed in `requirements.txt`:
- `tensorflow`
- `gradio`
- `numpy`
- `pillow`

πŸ™‹β€β™‚οΈ **Author**
Aarzoo Singh  
Research Intern, Machine Learning  
B.Tech CSE, NIT Patna  
πŸ”— [Hugging Face Profile](https://huggingface.co/Aarzoo-Singh2206)

---

🌐 **Live App:** [Click here to run this app](https://huggingface.co/spaces/Aarzoo-Singh2206/codeblock)