update readme file

README.md

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 ---
 license: cc-by-4.0
+language:
+- en
+tags:
+- agriculture
+- crop-disease
+- computer-vision
+- image-classification
+- deep-learning
+- cnn
+- smart-farming
+- iot
+task_categories:
+- image-classification
+task_ids:
+- multi-class-classification
+pretty_name: Crop Disease Image Dataset (5 Crops, 19 Classes)
+size_categories:
+- 10K<n<100K
 ---
+
+#  Crop Disease Image Dataset (5 Crops, 19 Classes)
+
+## Dataset Summary
+
+The **Crop Disease Image Dataset** is a curated, high-quality agricultural image dataset designed for computer vision, deep learning, and smart farming applications. It contains **22,169 RGB leaf images** spanning **5 major crops** across **19 distinct healthy and diseased classes**.
+
+This dataset was constructed by collecting, filtering, and standardizing images from multiple open-source agricultural repositories (such as the *PlantVillage Dataset* and Mendeley's *15 Crop and 45 Disease Dataset*) to create a unified resource for training models like MobileNetV3, ResNet, EfficientNet, and Vision Transformers (ViT).
+
+This dataset forms the core AI vision module for the **IoT-Based Crop Disease Detection System for Smart Agriculture** project.
+
+---
+
+## Key Overview & Metrics
+
+| Property | Description / Value |
+|---|---|
+| **Dataset Type** | Image Classification |
+| **Domain** | Agriculture / Smart Farming |
+| **Total Crops** | 5 (Corn, Potato, Rice, Tomato, Wheat) |
+| **Total Classes** | 19 (Healthy & Diseased) |
+| **Total Images** | 22,169 |
+| **Image Format** | RGB (JPG / JPEG) |
+| **Dataset Size** | ~1.36 GB |
+
+---
+
+## Crop & Class Distribution
+
+### 🌽 Corn (Maize) — *6,617 images*
+| Class Name | Image Count |
+|---|---|
+| Cercospora Leaf Spot (Gray Leaf Spot) | 513 |
+| Common Rust | 1,192 |
+| Healthy | 1,662 |
+| Northern Leaf Blight | 985 |
+| Leaf Spot | 1,261 |
+| Maize Streak Virus | 1,004 |
+
+### 🥔 Potato — *2,968 images*
+| Class Name | Image Count |
+|---|---|
+| Early Blight | 1,000 |
+| Late Blight | 1,000 |
+| Healthy | 968 |
+
+### 🌾 Rice — *3,637 images*
+| Class Name | Image Count |
+|---|---|
+| Brown Spot | 1,210 |
+| Leaf Blast | 957 |
+| Healthy | 1,470 |
+
+### 🍅 Tomato — *6,627 images*
+| Class Name | Image Count |
+|---|---|
+| Bacterial Spot | 2,127 |
+| Early Blight | 1,000 |
+| Late Blight | 1,909 |
+| Healthy | 1,591 |
+
+### 🌾 Wheat — *2,820 images*
+| Class Name | Image Count |
+|---|---|
+| Brown Rust | 860 |
+| Yellow Rust | 880 |
+| Healthy | 1,080 |
+
+---
+
+## Dataset Structure
+
+The raw image archive is organized using standard class-folder hierarchy:
+
+```text
+Crop_Disease_Image_Dataset/
+│
+├── Corn_(Maize)___Cercospora_Leaf_Spot_(Gray_Leaf_Spot)/
+├── Corn_(Maize)___Common_Rust/
+├── Corn_(Maize)___Healthy/
+├── Corn_(Maize)___Northern_Leaf_Blight/
+├── Corn_(Maize)___Leaf_Spot/
+├── Corn_(Maize)___Maize_Streak_Virus/
+│
+├── Potato___Early_Blight/
+├── Potato___Healthy/
+├── Potato___Late_Blight/
+│
+├── Rice___Brown_Spot/
+├── Rice___Healthy/
+├── Rice___Leaf_Blast/
+│
+├── Tomato___Bacterial_Spot/
+├── Tomato___Early_Blight/
+├── Tomato___Healthy/
+├── Tomato___Late_Blight/
+│
+├── Wheat___Brown_Rust/
+├── Wheat___Healthy/
+└── Wheat___Yellow_Rust/
+```
+
+---
+
+## Dataset Creation Pipeline
+
+```text
+Multiple Public Datasets (PlantVillage, Mendeley, etc.)
+                      │
+                      ▼
+               Data Collection
+                      │
+                      ▼
+            Class Standardization
+                      │
+                      ▼
+               Duplicate Removal
+                      │
+                      ▼
+            Dataset Consolidation
+                      │
+                      ▼
+  Final Cleaned Dataset (22,169 Images, 19 Classes)
+```
+
+---
+
+## Recommended Usage & Preprocessing
+
+### Data Splitting Strategy
+For robust evaluation and avoiding data leakage across classes, a **stratified split** is strongly recommended:
+* **Train:** 70%
+* **Validation:** 15%
+* **Test:** 15%
+
+### Data Augmentation & Pipelines
+When loading data into frameworks like PyTorch (`torchvision`) or TensorFlow (`tf.keras`), standard spatial transformations improve model generalization:
+* Resize & Center Crop (e.g., `224x224` or `256x256`)
+* Pixel Normalization (ImageNet mean & std)
+* Random Flips, Rotations, and Color Jittering
+
+---
+
+## Project Context: Dual-Module IoT System
+
+This vision dataset forms Module 1 of a broader smart agriculture framework:
+
+1. **Vision Module (This Dataset):** Image-based leaf diagnosis using Deep Learning models deployed on local/edge devices.
+2. **Sensor Module:** Environmental monitoring using IoT sensors (Temperature, Humidity, Soil Moisture) fed into tabular ML models to assess environmental risk.
+
+---
+
+## Source Datasets & Attributions
+
+This aggregated dataset builds upon key open-access research contributions:
+
+1. **PlantVillage Dataset**
+   * *Paper:* Mohanty, S. P., Hughes, D. P., & Salathé, M. (2016). *Using deep learning for image-based plant disease detection*. Frontiers in Plant Science, 7, 1419. [DOI: 10.3389/fpls.2016.01419](https://doi.org/10.3389/fpls.2016.01419)
+   * *Repository:* [GitHub - PlantVillage Dataset](https://github.com/spMohanty/PlantVillage-Dataset)
+
+2. **15 Crop and 45 Disease and Healthy Dataset**
+   * *Citation:* Jain, A. (2026). *15 Crop and 45 Disease and Healthy dataset* (Version 1). Mendeley Data. [DOI: 10.17632/8fr7grr73p.1](https://data.mendeley.com/datasets/8fr7grr73p/1)
+
+---
+
+## Limitations
+
+* **Lighting & Occlusion:** Images originate from controlled and semi-controlled settings; real field conditions (direct glare, severe shadows, partial leaves) might require targeted fine-tuning.
+* **Class Imbalance:** Minor imbalance exists between classes (e.g., Potato healthy vs. Tomato Bacterial spot). Class-weighted loss functions are recommended.
+
+---
+
+## Citation & License
+
+This dataset is shared under the **Creative Commons Attribution 4.0 International (CC BY 4.0)** license.
+
+If you use this dataset in your research or project, please cite it as:
+
+```bibtex
+@dataset{crop_disease_dataset_2026,
+  title     = {Crop Disease Image Dataset (5 Crops, 19 Classes)},
+  author    = {IoT Based Crop Disease Detection System Project},
+  year      = {2026},
+  publisher = {Hugging Face},
+  note      = {Created by combining PlantVillage and Mendeley datasets}
+}
+```