Sunflower Plant Health and Growth Stage Image Dataset for Agricultural Machine Learning Applications
Description
This dataset contains high-resolution images of sunflower plants (Helianthus annuus), collected from Daffodil Smart City, Bangladesh. The sunflower is an economically important crop globally, valued for its edible oil, seeds, and ecological contributions. Accurate monitoring of sunflower plant health and growth stages is vital for optimizing agricultural yield, enhancing crop management practices, and supporting precision farming technologies. The primary purpose of this dataset is to facilitate machine learning and deep learning-based classification, detection, and monitoring of various growth stages and general health conditions of sunflower plants. Unlike disease detection datasets, this collection focuses on capturing the natural developmental phases and health states of sunflowers under normal growth environments. The dataset is organized into five visually distinct classes representing key growth stages and health conditions: EarlyBloom (1078 images) Healthy (1202 images) MatureBud (1008 images) Wilted (1037 images) YoungBud (1025 images) Total Images: 5,350 Image Details: Original Resolution: 3000 × 4000 pixels Compressed Resolution: 560 × 420 pixels Image Format: jpg