An ultra-specific image dataset for automated insect identification

Abstract For research in the field of illumination estimation and color constancy, there is a need for ground-truth measurement of the illumination color at many locations within multi-illuminant scenes. A practical approach to obtaining such ground-truth illumination data is presented here. The proposed method involves using a drone to carry a gray ball of known percent surface spectral reflectance throughout a scene while photographing it frequently during the flight using a calibrated camera. The captured images are then post-processed. In the post-processing step, machine vision techniques are used to detect the gray ball within each frame. The camera RGB of light reflected from the gray ball provides a measure of the illumination color at that location. In total, the dataset contains 30 scenes with 100 illumination measurements on average per scene. The dataset is available for download free of charge.

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Indian Ayurvedic Plant Identification Using Multi-Organ Image Analytics: Creation of Image Dataset of Indian Medicinal Plant Organs

SSRN Electronic Journal ◽

10.2139/ssrn.3563074 ◽

2020 ◽

Author(s):

Meera Kansara ◽

Ajay Parikh

Keyword(s):

Medicinal Plant ◽

Plant Identification ◽

Plant Organs ◽

Indian Medicinal Plant ◽

Image Dataset

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LeLePhid: An Image Dataset for Aphid Detection and Infestation Severity on Lemon Leaves

Data ◽

10.3390/data6050051 ◽

2021 ◽

Vol 6 (5) ◽

pp. 51

Author(s):

Jorge Parraga-Alava ◽

Roberth Alcivar-Cevallos ◽

Jéssica Morales Carrillo ◽

Magdalena Castro ◽

Shabely Avellán ◽

...

Keyword(s):

Image Segmentation ◽

Plant Disease ◽

Automatic Identification ◽

Health State ◽

Classification Problems ◽

Affected Area ◽

White Spots ◽

The World ◽

Image Dataset ◽

Plant Sap

Aphids are small insects that feed on plant sap, and they belong to a superfamily called Aphoidea. They are among the major pests causing damage to citrus crops in most parts of the world. Precise and automatic identification of aphids is needed to understand citrus pest dynamics and management. This article presents a dataset that contains 665 healthy and unhealthy lemon leaf images. The latter are leaves with the presence of aphids, and visible white spots characterize them. Moreover, each image includes a set of annotations that identify the leaf, its health state, and the infestation severity according to the percentage of the affected area on it. Images were collected manually in real-world conditions in a lemon plant field in Junín, Manabí, Ecuador, during the winter, by using a smartphone camera. The dataset is called LeLePhid: lemon (Le) leaf (Le) image dataset for aphid (Phid) detection and infestation severity. The data can facilitate evaluating models for image segmentation, detection, and classification problems related to plant disease recognition.

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RDD2020: An annotated image dataset for Automatic Road Damage Detection using Deep Learning

Data in Brief ◽

10.1016/j.dib.2021.107133 ◽

2021 ◽

pp. 107133

Author(s):

Deeksha Arya ◽

Hiroya Maeda ◽

Sanjay Kumar Ghosh ◽

Durga Toshniwal ◽

Yoshihide Sekimoto

Keyword(s):

Deep Learning ◽

Damage Detection ◽

Image Dataset

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Subspace-based Domain Adaptation Using Similarity Constraints for Pneumonia Diagnosis within a Small Chest X-ray Image Dataset

2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) ◽

10.1109/isbi48211.2021.9434173 ◽

2021 ◽

Author(s):

Karen Sanchez ◽

Carlos Hinojosa ◽

Henry Arguello ◽

Simon Freiss ◽

Nicolas Sans ◽

...

Keyword(s):

Domain Adaptation ◽

X Ray ◽

Image Dataset ◽

Chest X Ray ◽

Pneumonia Diagnosis

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Multichannel acoustic source and image dataset for the cocktail party effect in hearing aid and implant users

Scientific Data ◽

10.1038/s41597-020-00777-8 ◽

2020 ◽

Vol 7 (1) ◽

Author(s):

Tim Fischer ◽

Marco Caversaccio ◽

Wilhelm Wimmer

Keyword(s):

Hearing Aids ◽

Scientific Community ◽

Cocktail Party ◽

Acoustic Source ◽

Image Dataset ◽

Cocktail Party Effect ◽

Spatial Coordinates ◽

Acoustic Chamber ◽

Human Hearing ◽

Human Sense

AbstractThe Cocktail Party Effect refers to the ability of the human sense of hearing to extract a specific target sound source from a mixture of background noises in complex acoustic scenarios. The ease with which normal hearing people perform this challenging task is in stark contrast to the difficulties that hearing-impaired subjects face in these situations. To help patients with hearing aids and implants, scientists are trying to imitate this ability of human hearing, with modest success so far. To support the scientific community in its efforts, we provide the Bern Cocktail Party (BCP) dataset consisting of 55938 Cocktail Party scenarios recorded from 20 people and a head and torso simulator wearing cochlear implant audio processors. The data were collected in an acoustic chamber with 16 synchronized microphones placed at purposeful positions on the participants’ heads. In addition to the multi-channel audio source and image recordings, the spatial coordinates of the microphone positions were digitized for each participant. Python scripts were provided to facilitate data processing.

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