Mulberry Leaf Dataset
Description
Data collection: We collected the mulberry leaf cultivars from three regions of Thailand (northern, central, and northeast) that included five provinces in total (Chiang Mai, Phitsanulok, Nakhon Ratchasima, Burriram, and Mahasarakham). DSLR and phone cameras were used to take images with different perspectives from ten mulberry leaf cultivars recorded in the natural environments with different perspectives. The mulberry leaf dataset includes 5,262 images of 10 mulberry leaf cultivars: King Red, King White, Taiwan Maechor, Taiwan Strawberry, Black Austurkey, Black Australia, Chiang Mai 60, Buriram 60, Kamphaeng Saen 42, and Mixed Chiang Mai 60+Buriram 60. Data description: The mulberry leaf dataset was curated in 2020 using digital single-lens reflex (DSLR) and smartphone cameras to collect 5,262 images categorized into ten classes existing in diverse environmental conditions. No seasonal considerations were factored in during the data collection; however, all data was collected on the sunny days. There is no existence of external plants in the samples of the mulberry leaves, but there are some natural backgrounds of the brown soil and the mulberry tree, which exist in small proportion relative to the mulberry leaves. The image format of the mulberry leaf dataset exists in JPEG format and has varying resolution sizes. The researcher captured mulberry leaf images from various regions and provinces in Thailand for eight months. The image captures of mulberry leaves were taken from five Thai areas, as shown in Fig. 1. Further, the dataset was annotated by a domain expert responsible for classifying each of the mulberry leaves into their respective classes or categories. The leaves with similar features or properties were stored in a specific folder (class), resulting in ten possible classes. Data source location: The mulberry leaf dataset was collected from three regions of Thailand: northern (Chiang Mai), central (Phitsanulok), and northest (Nakhon Ratchasima, Burriram, and Mahasarakham). Related research article: Chompookham, T. & Surinta, O. (2021). Ensemble methods with deep convolutional neural networks for plant leaf recognition. ICIC Express Letters, 15(6), 553-565. DOI: 10.24507/icicel.15.06.553
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Funding
This research project was financially supported by Mahasarakham University, Thailand