PlantDiseases Dataset for Edge - AI Based Real - Time Plant Disease Recognition

Description

The PlantDiseases dataset is constructed for edge - AI based real - time plant disease recognition. It encompasses 28 distinct disease types across 16 plant species, totaling 46 diagnostic categories. The dataset contains 240,211 preprocessed and augmented images, collected under varying environmental conditions using different imaging devices and from multiple perspectives. It provides essential data support for algorithm research in edge - computing scenarios for plant disease recognition

Steps to reproduce

To reproduce the results related to this dataset: Download the dataset from the provided links. Preprocess the images using the following steps: resize images to a uniform size (e.g., 224×224 pixels), perform data augmentation (such as random rotation, flipping) to increase data diversity. Use a deep learning framework (e.g., PyTorch or TensorFlow) to build a model (e.g., a convolutional neural network like ResNet or MobileNet). Train the model with the training subset of the dataset, using a specific batch size (e.g., 32), learning rate (e.g., 0.001), and optimization algorithm (e.g., Adam). Evaluate the trained model on the validation or test subset to get performance metrics like accuracy, precision, and recall.

Institutions

• Henan Normal University

Categories

Funders

• Henan Science and Technology Department

  China

  Grant ID: 242102320175
• Key Scientific Research Projects of Higher Education Institutions in Henan Province

  Grant ID: 25B413001

Licence