Unmanned Aerial System-Based Weed Mapping in Sod Production Using a Convolutional Neural Network.

ABSTRACT: Weeds are a persistent problem on sod farms, and herbicides to control different weed species are one of the largest chemical inputs. Recent advances in unmanned aerial systems (UAS) and artificial intelligence provide opportunities for weed mapping on sod farms. This study investigates the weed type composition and area through both ground and UAS-based weed surveys and trains a convolutional neural network (CNN) for identifying and mapping weeds in sod fields using UAS-based imagery and a high-level application programming interface (API) implementation (Fastai) of the PyTorch deep learning library. The performance of the CNN was overall similar to, and in some classes (broadleaf and spurge) better than, human eyes indicated by the metric recall. In general, the CNN detected broadleaf, g

SUBMITTER: Zhang J 

PROVIDER: S-EPMC8660967 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature