SlypNet: Spikelet-based yield prediction of wheat using advanced plant phenotyping and computer vision techniques.

ABSTRACT: The application of computer vision in agriculture has already contributed immensely to restructuring the existing field practices starting from the sowing to the harvesting. Among the different plant parts, the economic part, the yield, has the highest importance and becomes the ultimate goal for the farming community. It depends on many genetic and environmental factors, so this curiosity about knowing the yield brought several precise pre-harvest prediction methods using different ways. Out of those techniques, non-invasive yield prediction techniques using computer vision have been proved to be the most efficient and trusted platform. This study developed a novel methodology, called SlypNet, using advanced deep learning networks, i.e., Mask R-CNN and U-Net, which can extract various pla

SUBMITTER: Maji AK 

PROVIDER: S-EPMC9386505 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature