Rajbongshi Aditya, Sazzad Sadia, Shakil Rashiduzzaman, Akter Bonna, Sara Umme
Department of Computer Science and Engineering, National Institute of Textile Engineering and Research, Dhaka, Bangladesh.
Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh.
Data Brief. 2022 Apr 12;42:108174. doi: 10.1016/j.dib.2022.108174. eCollection 2022 Jun.
Guava (Psidium guajava) is a delicious fruit native to Mexico, Central or South America, and the Caribbean region. It's high in vitamin C, Calcium, Pectins and is a good source of fiber. Due to concerns with natural and environmental resources, technical issues, and other impediments, the production level decreases day-to-day. However, we'll concentrate on the most critical challenges, such as infections that affect guava plants, fruits, and disease outbreak prevention through early identification. Besides, the early recognition of guava disease using the expert system will lead to higher yields that will eventually help guava farmers reduce their economic losses. In the recent era, image processing and computer vision have been broadly applied to recognize multiple diseases that are not identified with the naked eyes. This article presents a dataset of guava images containing both leaves and fruit images (diseases affected and disease-free) are classified into six classes: for guava fruits-Phytophthora, Scab, Styler end Rot, and Disease-free fruit, and for guava leaves-Red Rust, and diseases-free leave. All images are basically captured from the guava garden located at Bangladesh Agricultural University in July when the guava fruits are almost ripened, and the infections are found in guava plants. This dataset is mainly for those researchers who work with computer vision, machine learning, and deep learning to develop a system that recognizes the guava disease to assist guava farmers in their cultivation.
番石榴（Psidium guajava）是一种美味的水果，原产于墨西哥、中美洲或南美洲以及加勒比地区。它富含维生素C、钙、果胶，是膳食纤维的良好来源。由于自然资源、环境问题、技术问题以及其他阻碍，番石榴的产量日益下降。然而，我们将专注于最关键的挑战，比如影响番石榴植株和果实的感染问题，以及通过早期识别预防疾病爆发。此外，利用专家系统早期识别番石榴病害将带来更高的产量，最终帮助番石榴种植户减少经济损失。近年来，图像处理和计算机视觉已被广泛应用于识别多种肉眼无法识别的病害。本文展示了一个番石榴图像数据集，其中包含叶片和果实图像（有病和无病的），分为六个类别：对于番石榴果实——疫霉病、疮痂病、蒂腐病和无病果实，对于番石榴叶片——赤锈病和无病叶片。所有图像基本上都是在7月从位于孟加拉国农业大学的番石榴园拍摄的，此时番石榴果实几乎成熟，且番石榴植株上发现了感染情况。这个数据集主要供那些从事计算机视觉、机器学习和深度学习工作的研究人员使用，以开发一个识别番石榴病害的系统，帮助番石榴种植户进行种植。
