Sunflower Plant Health and Growth Stage Image Dataset for Agricultural Machine Learning Applications

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 (1055 images)
Healthy (1199 images)
MatureBud (827 images)
Wilted (1023 images)
YoungBud (1009 images)
Total Images: 5,113

Image Details:
Original Resolution: 3000 × 4000 pixels
Compressed Resolution: 560 × 420 pixels
Image Format: jpg