Okra DiseaseNet Dataset

Published: 19 December 2024| Version 1 | DOI: 10.17632/nh7zk4hv8z.1
Contributors:
Sowmiya Kumarakuru,

Description

Crop Name : Okra or Lady’s finger (Scientific Name : Abelmoschus esculentus) Dataset : Okra leaf disease image dataset Problem : To address the segmentation and classification problems in the field of AI researchers due to data scarcity in Indian agricultural field. Motivation : To diagnose the okra leaf diseases segmentation with severity level estimation and classification at early stage using computer vision algorithms Dataset Description : This dataset comprises of 1500 high resolution okra leaf healthy and diseased images and also mainly focus on collection of various types of leaf diseases according to two locations and nature of soil (Chengalpattu and Thanjavur) includes 1500 images with 6 different classes namely Class 0 : Healthy, Class 1 : Alternaria leaf spot, Class 2: Cercospora leaf spot, Class 3: Downy mildew, Class 4: Leaf curly virus and Class 5: Phyllosticta leaf spot. Segmentation : It will be also used in segmentation to measure the severity level estimation for exactly localize and segments the diseased lesion spots in each leaf. Classification : It used to classify the images as healthy and different types of diseased okra leaves. Image Acquired by : Canon EOS 3000D DSLR Camera with specifications, 18-55 mm lens (Black), 9 Auto focus points, Wifi, Full HD, Optimal zoom 35x, CMOS Sensor type and 18MP effective pixels. Image type : RGB Image Format : jpg format Image size : 224x224x3 Dataset Split Ratio : Training - 75%, Testing - 15%and Validation -10% Time Span for data acquisition : Chengalpattu -> March and April, 2023 and Thanjavur -> May, 2023 Location : SRM Urban Farm, Chengalpattu and Agricultural Lands, Thanjavur Why dataset are needed :  Okra dataset is the first crop acquired for segmenting and classifying their diseases in Indian agricultural lands with different light illumination.  It mainly focus on aiding the farmers to diagnose leaf diseases at early stage and prevent food insecurity issues for avoiding crop yield losses.  It also aids to the researchers also to build AI models for superior efficient results. Citation: If you find this dataset helpful and use it in your work, kindly cite this dataset using “Kumarakuru, Sowmiya; M, Thenmozhi (2024), “Okra DiseaseNet Dataset”, Mendeley Data, V1, doi: 10.17632/nh7zk4hv8z.1 ”

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Institutions

SRM University

Categories

Artificial Intelligence, Computer Vision, Data Science, Machine Learning, Data Collection in Agriculture, Deep Learning

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