Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+tags:
+  - crop-disease-detection
+  - computer-vision
+  - pytorch
+  - efficientnet
+  - agriculture
+  - plant-diseases
+datasets:
+  - plant-village
+  - dhan-shomadhan
+  - custom
+language: en
+---
+
+# Crop Disease Detection Model (EfficientNet-B3)
+
+## Overview
+
+This model detects and classifies crop diseases using computer vision and deep learning. Built on **EfficientNet-B3** and trained on a curated dataset of 13,000+ images, the model can recognize 17 disease classes across **five major crops**:
+
+- **Corn** (Common Rust, Gray Leaf Spot, Northern Leaf Blight, Healthy)  
+- **Potato** (Early Blight, Late Blight, Healthy)  
+- **Rice** (Brown Spot, Leaf Blast, Neck Blast, Healthy)  
+- **Wheat** (Yellow Rust, Brown Rust, Healthy)  
+- **Sugarcane** (Red Rot, Bacterial Blight, Healthy)
+
+✅ **Accuracy:** 94.8%  
+✅ **Precision:** 95.4%  
+✅ **Recall:** 94.5%
+
+The model contributes to **SDG 2 – Zero Hunger**, **SDG 12 – Responsible Consumption**, and **SDG 13 – Climate Action** by enabling early intervention and sustainable agriculture practices.
+
+---
+
+## Model Details
+
+- **Architecture**: EfficientNet-B3 (pretrained on ImageNet)
+- **Classifier Head**: Replaced with `Linear(1536 → 17)`
+- **Framework**: PyTorch
+- **Total Parameters**: ~10.7M
+- **Training**:
+  - 5-fold cross-validation
+  - Early stopping (best at epoch 29)
+  - Augmentation & normalization
+
+---
+
+## How to Use
+
+> 💡 This model requires preprocessing consistent with training (image resizing, normalization). For ready-to-use prediction.
+
+### Inference Example (PyTorch)
+```python
+import torch
+from torchvision import transforms
+from PIL import Image
+import requests
+from huggingface_hub import hf_hub_download
+
+# Download the model file from Hugging Face
+model_path = hf_hub_download(repo_id="VisionaryQuant/5_Crop_Disease_Detection", filename="efficientnet_b3_model.pt")
+
+# Load the model (make sure your architecture matches)
+model = torch.load(model_path, map_location=torch.device('cpu'))
+model.eval()
+
+# Preprocess input image
+image = Image.open("your_crop_image.jpg").convert("RGB")
+transform = transforms.Compose([
+    transforms.Resize((300, 300)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                         std=[0.229, 0.224, 0.225])
+])
+input_tensor = transform(image).unsqueeze(0)
+
+# Run inference
+with torch.no_grad():
+    logits = model(input_tensor)
+    probs = torch.nn.functional.softmax(logits, dim=1)
+    predicted_idx = torch.argmax(probs, dim=1).item()
+
+# Map class index to label
+idx2label = {0: "Corn___Common_Rust", 1: "Corn___Gray_Leaf_Spot", ..., 16: "Sugarcane___Healthy"}  # Add full mapping
+print("Prediction:", idx2label[predicted_class])
+```
+
+## Real-World Applications
+- Smart Farming: Disease detection via mobile/drones
+
+- Scalable Monitoring: Surveying across large farmlands
+
+- Yield Optimization: Early diagnosis = lower crop loss
+
+## Citation
+If you use this model, please cite it as:
+
+**BibTeX:**
+```
+@misc{5cropdiseasedetection2025,
+  title  = {Crop Disease Detection using EfficientNet-B3},
+  author = {Abdullahi Olalekan Abdulmumeen},
+  year   = {2025},
+  url    = {https://huggingface.co/VisionaryQuant/5_Crop_Disease_Detection}
+}
+```
+
+**APA:**
+```
+Abdulmumeen, A. O. (2025). Crop disease detection using EfficientNet-B3 [Model]. Hugging Face. https://huggingface.co/VisionaryQuant/5_Crop_Disease_Detection
+```
+
+## Contact & Credits
+Developed by Abdullahi Olalekan Abdulmumeen <br/>
+For the NaijaFarmConsultAI 3MTT Knowledge Showcase project