Basil/Tulsi Plant is harvested in India because of some spiritual facts behind this plant,this plant is used for essential oil and pharmaceutical purpose. There are two types of Basil plants cultivated in India as Krushna Tulsi/Black Tulsi and Ram Tulsi/Green Tulsi.

Many of the investigator working on disease detection in Basil leaves where the following diseases occur

 1) Gray Mold

2) Basal Root Rot, Damping Off

 3) Fusarium Wilt and Crown Rot

Instructions: 

Basil/Tulsi Plant is harvested in India because of some spiritual facts behind this plant,this plant is used for essential oil and pharmaceutical purpose. There are two types of Basil plants cultivated in India as Krushna Tulsi/Black Tulsi and Ram Tulsi/Green Tulsi.

Many of the investigator working on disease detection in Basil leaves where the following diseases occur

 1) Gray Mold

2) Basal Root Rot, Damping Off

 3) Fusarium Wilt and Crown Rot

4) Leaf Spot

5) Downy Mildew

The Quality parameters (Healthy/Diseased) and also classification based on the texture and color of leaves. For the object detection purpose researcher using an algorithm like Yolo,  TensorFlow, OpenCV, deep learning, CNN

I had collected a dataset from the region Amravati, Pune, Nagpur Maharashtra state the format of the images is in .jpg.

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This file includes code and data of the paper named Dynamic radiomics: a new methodology to  extract quantitative time-related features from  tomographic images

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As part of the 2018 IEEE GRSS Data Fusion Contest, the Hyperspectral Image Analysis Laboratory and the National Center for Airborne Laser Mapping (NCALM) at the University of Houston are pleased to release a unique multi-sensor optical geospatial representing challenging urban land-cover land-use classification task. The data were acquired by NCALM over the University of Houston campus and its neighborhood on February 16, 2017 between 16:31 and 18:18 GMT.

Instructions: 

Data files, as well as training and testing ground truth are provided in the enclosed zip file.

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BTH Trucks in Aerial Images Dataset contains videos of 17 flights across two industrial harbors' parking spaces over two years.

Instructions: 

If you use these provided data in a publication or a scientific paper, please cite the dataset accordingly.

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With the rapid development of augmented reality

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With the rapid development of augmented reality

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Fast optical 3D inline inspection sensors are a powerful tool to advance factory automation. Many of these visual inspection tasks require high speeds, resolutions and repeatability. Different approaches exist. Stereo vision, photometric stereo, light sectioning and structured light are the most common principles for inline imaging in the several micrometers to sub-millimeter resolution range.

Instructions: 

We compared different optical inline 3D measurement systems. Quantitative results are presented in our paper (Table 3). For a qualitative comparison we acquired the same artificial scene with al tested systems. The results are presented in this dataset. System 1: testobjekt_lightfield_ICI-50um.plySystem 2: testobjekt_strucrued_light_PhoXi-XS.plySystem 3: testobjekt_laser_triangualtion_LJ-X8400.plySystem 4: System: testobjekt_3DPIXA-30um.ply 

 

 

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Semantic segmentation is the topic of interest among deep learning researchers in the recent era.  It has many applications in different domains including, food recognition. In the case of food recognition, it removes the non-food background from the food portion. There is no large public food dataset available to train semantic segmentation models. We prepared a dataset named ’SEG-FOOD’[44] containing images of FOOD101, PFID, and Pakistani Food dataset and open-sourced the annotated dataset for future research. We annotated the images using JS Segment annotator.

Instructions: 

*  For detailed experimentation, please refer to our paper which is under review. we will update the link of that later.

* For starter code please refer to our Github repository. https://github.com/ghalib2021/SEGFOOD

* Note: This dataset contains images from Food101, PFID, and Pakistani Food Dataset. Our main contribution is the manual annotation of the food images for background removal using semantic segmentation and collection of Pakistani food dataset images. Please cite our work besides the original dataset collector if you are using a segmented dataset otherwise, cite the original dataset collector.

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