Leaf Diseases Dataset
The original dataset can be found on [this] [1] github repo. This dataset consists of about 87K rgb images of healthy and diseased crop leaves which is categorized into 38 different classes. Dataset Overview: 1.
Contains images of plant leaves affected by various diseases (bacterial, fungal, viral) commonly found in agricultural crops. 2. Captured under controlled conditions, ensuring realistic and diverse real-world scenarios.
3. Includes images from multiple plant species and varying disease stages. Types of Plant Leafs: 1.
Gourd (1147) 2. Hibiscus (1328) 3. Papaya (547) 4.
Against this background, we present PlantDoc: a dataset for visual plant disease detection. Our dataset contains 2,598 data points in total across 13 plant species and up to 17 classes of diseases, involving approximately 300 human hours of effort in annotating internet scraped images. The PlantVillage dataset, with over 54,000 images spanning 14 plant species and 26 disease types, has been widely used for leaf disease classification.
However, it is limited in both scale and diversity. To address these limitations, we developed LeafNet, a large. This survey provides a comprehensive overview of real-world and laboratory datasets, feature extraction methods, deep learning frameworks, limitations, recommendations, and future directions for deep plant leaf disease recognition.
A combined dataset was utilised, incorporating the PlantDoc dataset with web-sourced images of plants from online platforms. State-of-the-art convolutional neural network (CNN) architectures, including EfficientNet-B0, EfficientNet-B3, ResNet50, and DenseNet201, were employed and fine. Despite advancements, many studies have concentrated on limited datasets or a restricted number of diseases.
Our research aims to overcome these limitations by presenting a comprehensive approach that utilizes a multispecies dataset to identify bacterial, fungal, and pest. Plant Disease dataset: This dataset contains over 4,000 images of plant leaves, representing 38 different crop species and 14 different diseases. In this work, a YOLO-LeafNet approach is proposed for detecting diseases from leaf images of four distinct species, namely, grape, bell pepper, corn, and potato.
A leaf disease segmentation dataset is typically used in the field of computer vision and machine learning for developing and evaluating models that can automatically detect and segment plant diseases from images of plant leaves. These datasets are valuable for applications in agriculture and plant pathology.