SeSame Aerial Dataset
Description
A new aerial sesame weed dataset with various field conditions We used Agrocam NDVI with the Phantom 3 standard to capture a dataset of aerial sesame weed. The fields where we photographed the sesame crop are located at 31◦ 23′ 29′′ North and 72◦ 22′ 24′′ East in the Ballo Shahabal village in Punjab, Pakistan. A sesame crop covering four acres was photographed in August and September 2020 at various development phases, lighting, and soil conditions. Sesame crops in these dataset campaigns range from 16 to 45 days. Images were initially collected at 1920 × 1080 resolution, but for training and testing purposes, they are cropped to 480 × 352 pixels due to hardware and software limitations. Automatic photos are taken every 5s while flying. The drone is flown in manual mode at an average altitude of 15 feet, which equates to a ground sampling distance of 0.33 cm/ pixel, while the photographs are automatically recorded. The three channels of the Agrocam camera are NIR, G, and B, and they provide NGB composite images. Since Agrocam lacks the R (red channel), the green vegetation appears orange in the photos. Using the Image labeler app of MATLAB, we manually labeled the images. The app produces an image of labels in 8-bit unsigned grayscale format. In the labeled image, the background, crop, and weed are given the pixel values 0, 1, and 2, respectively. Citation Request: if you use these datasets in your research or projects by any means, please cite following publications. 1) Patch-wise weeds coarse segmentation mask from aerial imagery of sesame crop (Published in Computers and Electronics in Agriculture 2022, HEC Recognized W category, Impact factor 6.757, Q1) 2) Towards automated weed detection through two-stage semantic segmentation of tobacco and weed pixels in aerial Imagery (Published in Smart Agricultural Technology (A companion journal of Computers and Electronics in Agriculture)) 3) A Patch-Image Based Classification Approach for Detection of Weeds in Sugar Beet Crop (Published in IEEE Access, Impact factor 3.1, Q1) Acknowledgement Request This work is funded by the Higher Education Commission of Pakistan and the National center for Robotics and Automation (DF-1009–31). Please Acknowledge. Steps to Access Mendeley datasets 1. Click on the link 2. The link with ask you to sign in or register with institutional email. 3. Use your institutional/organization email to register and then sign in. 4. Once sign in, dataset will be visible in compressed folders 5. Download and unzip/umcompress folder 6. Use dataset in your research as you see fit (folders contains original images, and their labeled groundtruths, along with binary vegetation masks. In groundtruths background have label value of 0, crop have label 1 and weeds have label of 2. maskref subfolders shows labelled data for visualization) Find More datasets and published articles in Related Links
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Steps to reproduce
Steps to Access Mendeley datasets 1. Click on the link 2. The link with ask you to sign in or register with institutional email. 3. Use your institutional/organization email to register and then sign in. 4. Once sign in, dataset will be visible in compressed folders 5. Download and unzip/umcompress folder 6. Use dataset in your research as you see fit (folders contains original images, and their labeled groundtruths, along with binary vegetation masks. In groundtruths background have label value of 0, crop have label 1 and weeds have label of 2. maskref subfolders shows labelled data for visualization) Contact Contributor in case of confusion Dr. Syed Imran Moazzam Shah +92-313-7233382 imoazzam@ceme.nust.edu.pk drimoazz@gmail.com