Image-based datasets have become increasingly important in plant science, where they are used to study plant morphology, growth, and development, as well as to detect and monitor plant diseases and stress. The use of image-based datasets in plant science has significantly expanded in recent years, as advances in technology have made it possible to collect and process vast amounts of visual data from various sources, including remote sensing, drones, and mobile devices. The importance of image-based datasets in plant science lies in their ability to provide high-resolution and detailed information about plants, enabling researchers to study plant traits and processes in a non-destructive and non-invasive manner. Image-based datasets are also valuable for developing machine learning models that can detect and classify plant species, identify plant diseases and stress, and predict plant growth and yield. Looking ahead, the future benefits of image-based datasets in plant science are immense. The use of image-based datasets has the potential to significantly improve our understanding of plant biology and ecology, leading to the development of more sustainable and efficient agricultural practices. Image-based datasets can also be used to monitor plant health and predict crop yields, enabling farmers to make more informed decisions about irrigation, fertilization, and pest control. However, to realize these benefits, it will be crucial to address several challenges, including the need for more standardized and high-quality image datasets, as well as the development of more accurate and robust machine learning algorithms that can effectively learn from these datasets. Despite these challenges, the use of image-based datasets in plant science is poised to revolutionize the field, providing researchers and practitioners with new tools to address some of the most pressing challenges facing agriculture and plant biology.