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. 2019 Aug 22:26:104340.
doi: 10.1016/j.dib.2019.104340. eCollection 2019 Oct.

A citrus fruits and leaves dataset for detection and classification of citrus diseases through machine learning

Hafiz Tayyab Rauf  1 , Basharat Ali Saleem  2 , M Ikram Ullah Lali  1 , Muhammad Attique Khan  3 , Muhammad Sharif  4 , Syed Ahmad Chan Bukhari  5
Affiliations

A citrus fruits and leaves dataset for detection and classification of citrus diseases through machine learning

Hafiz Tayyab Rauf et al. Data Brief. .

Abstract

Plants are as vulnerable by diseases as animals. Citrus is a major plant grown mainly in the tropical areas of the world due to its richness in vitamin C and other important nutrients. The production of the citrus fruit has been widely affected by citrus diseases which ultimately degrades the fruit quality and causes financial loss to the growers. During the past decade, image processing and computer vision methods have been broadly adopted for the detection and classification of plant diseases. Early detection of diseases in citrus plants helps in preventing them to spread in the orchards which minimize the financial loss to the farmers. In this article, an image dataset citrus fruits, leaves, and stem is presented. The dataset holds citrus fruits and leaves images of healthy and infected plants with diseases such as Black spot, Canker, Scab, Greening, and Melanose. Most of the images were captured in December from the Orchards in Sargodha region of Pakistan when the fruit was about to ripen and maximum diseases were found on citrus plants. The dataset is hosted by the Department of Computer Science, University of Gujrat and acquired under the mutual cooperation of the University of Gujrat and the Citrus Research Center, Government of Punjab, Pakistan. The dataset would potentially be helpful to researchers who use machine learning and computer vision algorithms to develop computer applications to help farmers in early detection of plant diseases. The dataset is freely available at https://data.mendeley.com/datasets/3f83gxmv57/2.

Keywords: Feature extraction; Feature selection; Image classification.

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Figures

Fig. 1
Fig. 1
Samples of self-annotated images of healthy citrus fruits and leaves taken from the own collected dataset.
Fig. 2
Fig. 2
Samples of self-annotated images of citrus leaves and fruit with Black Spot diseases taken from the own collected dataset.
Fig. 3
Fig. 3
Samples of self-annotated images of citrus fruit with a) Canker b) Scab taken from the own collected dataset.
Fig. 4
Fig. 4
Samples of self-annotated images of citrus fruit and leaves with a) Greening and b) Melanose diseases taken from the own collected dataset.
Fig. 5
Fig. 5
System architecture for identification and classification of infected plants diseases directly taken from Ref. .
See this image and copyright information in PMC

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References

    1. Wen T., Zheng L., Dong S., Gong Z., Sang M., Long X., Peng H. Rapid detection and classification of citrus fruits infestation by Bactrocera dorsalis (Hendel) based on electronic nose. Postharvest Biol. Technol. 2019;147:156–165.
    1. El Kahlout M.I., Abu-Naser S.S. 2019. An Expert System for Citrus Diseases Diagnosis.
    1. Febrinanto F.G., Dewi C., Triwiratno A. vol. 243. IOP Publishing; 2019, March. The implementation of K-Means algorithm as image segmenting method in identifying the citrus leaves disease. (IOP Conference Series: Earth and Environmental Science). No. 1, p012024.
    1. Sharif M., Khan M.A., Iqbal Z., Azam M.F., Lali M.I.U., Javed M.Y. Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selection. Comput. Electron. Agric. 2018;150:220–234.
    1. Ali H., Lali M.I., Nawaz M.Z., Sharif M., Saleem B.A. Symptom based automated detection of citrus diseases using color histogram and textural descriptors. Comput. Electron. Agric. 2017;138:92–104.

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