README.md

---

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}

}

```