Lemon Leaf Classification Data Set
Description
Lemon Leaf Classification Dataset: A carefully curated dataset consisting of five distinct classes of lemon leaves, designed for robust image classification tasks. Each class represents unique variations in leaf characteristics, including shape, texture, and disease conditions. This dataset is ideal for developing and testing machine learning and deep learning models, contributing to the advancement of agricultural research. The balanced class distribution ensures a reliable foundation for classification models, enhancing precision in identifying different types of lemon leaves and promoting disease detection.
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Steps to reproduce
This dataset consists of high-resolution images of lemon leaves, categorized into five distinct classes. The images capture a range of variations in leaf characteristics such as shape, size, texture, color, and visible health conditions, including disease symptoms. The dataset is curated to represent diverse leaf conditions, making it suitable for tasks like image classification, disease detection, and health monitoring in agriculture. Each class is well-balanced to ensure effective training and evaluation of machine learning and deep learning models, particularly for tasks focused on precision agriculture and plant health diagnostics. The dataset can be used for agricultural technology applications, including automated plant disease identification and leaf condition monitoring.