Dataset Information

CitrusUAT: A dataset of orange Citrus sinensis leaves for abnormality detection using image analysis techniques.

ABSTRACT: Around the world, citrus production and quality are threatened by diseases caused by fungi, bacteria, and viruses. Citrus growers are currently demanding technological solutions to reduce the economic losses caused by citrus diseases. In this context, image analysis techniques have been widely used to detect citrus diseases, extracting discriminant features from an input image to distinguish between healthy and abnormal cases. The dataset presented in this article is helpful for training, validating, and comparing citrus abnormality detection algorithms. The data collection comprises 953 color images taken from the orange leaves of Citrus sinensis (L.) Osbeck species. There are 12 nutritional deficiencies and diseases supporting the development of automatic detection methods that can reduce economic losses in citrus production.

SUBMITTER: Gomez-Flores W

PROVIDER: S-EPMC10716766 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature

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CitrusUAT: A dataset of orange <i>Citrus sinensis</i> leaves for abnormality detection using image analysis techniques.

Gómez-Flores Wilfrido W Garza-Saldaña Juan José JJ Varela-Fuentes Sóstenes Edmundo SE

Data in brief 20231207

Around the world, citrus production and quality are threatened by diseases caused by fungi, bacteria, and viruses. Citrus growers are currently demanding technological solutions to reduce the economic losses caused by citrus diseases. In this context, image analysis techniques have been widely used to detect citrus diseases, extracting discriminant features from an input image to distinguish between healthy and abnormal cases. The dataset presented in this article is helpful for training, valida ...[more]

PMID: 38093853

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Project description:IntroductionCitrus fruits are one of the most frequently counterfeited processed products in the world. In the juice production alone, the peels, divided into flavedo and albedo, are the main waste product. The extracts of this by-product are enriched with many bioactive substances. Newer extraction techniques generally have milder extraction conditions with simultaneous improvement of the extraction process.MethodsThis study presents a combinatorial approach utilizing data-independent acquisition-based ion mobility spectrometry coupled to tandem mass spectrometry. Integrating orthogonal collision cross section (CCS) data matching simultaneously improves the confidence in metabolite identification in flavedo and albedo tissues from Citrus sinensis. Furthermore, four different extraction approaches [conventional, ultrasonic, High Hydrostatic Pressure (HHP) and Pulsed Electric Field (PEF)] with various optimized processing conditions were compared in terms of antioxidant effects and flavonoid profile particularly polymethoxy flavones (PMFs).ResultsA total number of 57 metabolites were identified, 15 of which were present in both flavedo and albedo, forming a good qualitative overlapping of distributed flavonoids. For flavedo samples, the antioxidant activity was higher for PEF and HHP treated samples compared to other extraction methods. However, ethyl acetate extract exhibited the highest antioxidant effects in albedo samples attributed to different qualitative composition content rather than various quantities of same metabolites. The optimum processing conditions for albedo extraction using HHP and PEF were 200 MPa and 15 kJ/kg at 10 kV, respectively. While, HHP at medium pressure (400 MPa) and PEF at 15 kJ/kg/3 kV were the optimum conditions for flavedo extraction.ConclusionChemometric analysis of the dataset indicated that orange flavedo can be a valid source of soluble phenolic compounds especially PMFs. In order to achieve cross-application of production, future study should concentrate on how citrus PMFs correlate with biological engineering techniques such as breeding, genetic engineering, and fermentation engineering.

Dataset Information

CitrusUAT: A dataset of orange Citrus sinensis leaves for abnormality detection using image analysis techniques.

Publications

CitrusUAT: A dataset of orange <i>Citrus sinensis</i> leaves for abnormality detection using image analysis techniques.

Similar Datasets

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