ArticleData in brief2025
Comprehensive smartphone image dataset for Aegle Marmelos, Hog plum, and lemon plant leaf disease and freshness assessment.
Article in Data in brief, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Fruits, which are packed with nutrients, vitamins, and antioxidants, have been known for their numerous health benefits and curative powers, and are utilized in conventional medicine. Aegle Marmelos, Lemon, and Hog Plum are tangy fruits widely recognized in Asian countries for containing a plentiful supply of bioactive substances. They are also highly valuable in boosting metabolism, possessing tremendous therapeutic properties, and holding financial significance. The leaves of these fruit trees are as essential as their fruits, as they contain versatile medicinal and dietary benefits of immense value. However, these leaves are often affected by various fungal and other diseases, which reduce the ability for healthy growth and productivity of both fruits and leaves. Plants infected with various leaf diseases can produce fewer fruits, which are also of lower quality due to failure to reach maturity and lack of sufficient nutritional value. For these reasons, there is a risk of an outbreak in orchards, which can lead to significant financial losses for both producers and the agricultural sector. This signifies that the early identification of leaf diseases and the management of orchards are essential to minimize the impact of leaf diseases and mitigate these issues, ensuring the healthy production of valuable medicinal fruits. In this paper, various infected leaf images are collected from different regions of Rangpur, providing a comprehensive dataset comprising 3941 images. The dataset includes images of three different plant leaves, where 1513 images of Aegle Marmelos, 1232 images of Lemon, and 1196 of Hog plum, where each of the categories encompasses several classes of common leaf diseases. Through this dataset, an early and accurate digital detection system can be employed, allowing producers to clearly identify diseases instead of relying on traditional methods. The precise and timely identification of leaf diseases enables the control of these diseases by taking necessary actions, ensuring the sustainability of plants, and promoting the healthy growth of these invaluable medicinal fruits.
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