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Found 5 papers, 0 papers with code
Selection of appropriate multispectral camera exposure settings and radiometric calibration methods for applications in phenotyping and precision agriculture
A simulation of errors from AE showed that MAPE < 5% for the blue, green, red, and NIR bands and < 7% for the red edge band for exposure settings within the determined ideal ranges and increased exponentially beyond the ideal exposure upper limit.
Plastic Contaminant Detection in Aerial Imagery of Cotton Fields with Deep Learning
Additionally, height of plastic bags had significant effect (p < 0. 0001) on overall detection accuracy.
Assessing The Performance of YOLOv5 Algorithm for Detecting Volunteer Cotton Plants in Corn Fields at Three Different Growth Stages
The boll weevil (Anthonomus grandis L.) is a serious pest that primarily feeds on cotton plants.
Computer Vision for Volunteer Cotton Detection in a Corn Field with UAS Remote Sensing Imagery and Spot Spray Applications
To control boll weevil (Anthonomus grandis L.) pest re-infestation in cotton fields, the current practices of volunteer cotton (VC) (Gossypium hirsutum L.) plant detection in fields of rotation crops like corn (Zea mays L.) and sorghum (Sorghum bicolor L.) involve manual field scouting at the edges of fields.
Detecting Volunteer Cotton Plants in a Corn Field with Deep Learning on UAV Remote-Sensing Imagery
The two-fold objectives of this paper were : (i) to determine whether YOLOv3 can be used for VC detection in a corn field using RGB (red, green, and blue) aerial images collected by UAS and (ii) to investigate the behavior of YOLOv3 on images at three different scales (320 x 320, S1; 416 x 416, S2; and 512 x 512, S3 pixels) based on average precision (AP), mean average precision (mAP) and F1-score at 95% confidence level.
