A Benchmark Dataset for Detecting Disease in Plant Leaves: An Essential Resource for Deep Learning Models

This dataset has been developed for research on plant leaf disease detection, specifically targeting Gourd, Zucchini, Bitter melon, Bean, Aubergine and Yardlong bean leaves. It contains high-resolution images capturing various disease symptoms to support the development and evaluation of deep learning models in agriculture. The dataset is ideal for use in image classification, segmentation, and disease diagnosis applications. Dataset Overview: Contains images of plant leaves affected by various diseases (Bacterial, YMV, Fungal, Viral infections) commonly found in agricultural crops. Captured under controlled conditions, ensuring realistic and diverse real-world scenarios. Includes images from multiple plant species and varying disease stages. Types of Plant Leafs: 1. Gourd (537) 2. Zucchini (186) 3. Bitter melon (300) 4. Bean (517) 5. Aubergine (242) 6. Yardlong bean (500) Key Features: Number of Images: 2820 Number of Augmented Images: 2561(to increase dataset variability and model robustness) File Formats: JPEG Disease Types: Bacterial, YMV, Fungal, and Viral infections. Applications: Deep Learning: This dataset is specifically designed for the development and evaluation of deep learning models for plant disease detection. It can be used to train convolutional neural networks (CNNs) and other advanced deep learning architectures for tasks such as image classification, object detection, and segmentation, providing insights into plant health status and disease severity. Machine Learning: In addition to deep learning, this dataset can also be utilised in traditional machine learning methods for disease recognition, but its primary value lies in enabling the use of cutting-edge neural networks for automated, scalable disease detection. Agricultural Technology: The dataset supports the development of mobile applications and automated systems for real-time plant health monitoring. Deep learning models trained on this data can be integrated into mobile platforms or drone-based systems to provide instant, accurate disease diagnosis in agricultural settings, aiding farmers in timely interventions. Agricultural Research: Researchers can use this dataset to better understand the impact of diseases on various plant species and their visual symptoms. The rich diversity of images will also help in studying disease progression, improving the design of predictive models, and contributing to better crop protection practices. Dataset Collection: 1. Compiled from publicly