Comprehensive Guava Leaf Disease Dataset for Advanced Detection and Sustainable Agriculture
Description
The Guava Leaf and Fruit Condition Dataset, sourced from the Vimruli Guava Garden and Floating Market in Jhalakathi, Barisal, is a comprehensive resource aimed at improving crop management through machine learning applications. This dataset includes images of healthy and diseased guava leaves and fruits, providing essential data for the classification and detection of various plant conditions. It serves as a valuable tool for researchers and practitioners working to develop automated systems for disease identification and agricultural management. The dataset is categorized into six distinct classes: algal leaves spot, dry leaves, healthy fruit, healthy leaves, insect-eaten leaves, and red rust. Each class represents a unique condition, such as fungal spots, environmental or nutrient-related dryness, insect damage, and fungal infections, alongside healthy samples of leaves and fruits. The distribution includes 100 original and 1,300 augmented images for algal leaves spot, 52 original and 676 augmented images for dry leaves, 50 original and 650 augmented images for healthy fruits, 150 original and 1,920 augmented images for healthy leaves, 164 original and 2,132 augmented images for insect-eaten leaves, and 90 original and 1,170 augmented images for red rust. This results in a total of 606 original images and 7,294 augmented images, culminating in a dataset of 7,900 samples. The images were captured between August and October 2024 using a iPhone 13 camera to ensure high-quality visual data. To enhance the diversity of the dataset and improve model performance, various augmentation techniques, including flipping, rotation, zooming, brightness adjustment, and shearing, were applied. This dataset is an excellent resource for training and evaluating machine learning models, enabling the accurate detection and classification of guava plant conditions. By leveraging this well-structured and diversified dataset, researchers can contribute to the development of precision agriculture systems that support sustainable practices and improve crop yield outcomes. 1. Original Dataset: Number of datasets: 606 Data format: .jpg 2. Augmented Dataset: Number of datasets: 7900 Data format: .jpg