Instance segmentation for the fine detection of crop and weed plants by precision agricultural robots.

ABSTRACT:

Premise

Weed removal in agriculture is typically achieved using herbicides. The use of autonomous robots to reduce weeds is a promising alternative solution, although their implementation requires the precise detection and identification of crops and weeds to allow an efficient action.

Methods

We trained and evaluated an instance segmentation convolutional neural network aimed at segmenting and identifying each plant specimen visible in images produced by agricultural robots. The resulting data set comprised field images on which the outlines of 2489 specimens from two crop species and four weed species were manually drawn. We adjusted the hyperparameters of a mask region-based convolutional neural network (R-CNN) to this specific task and evaluated the resulting trained mod

SUBMITTER: Champ J 

PROVIDER: S-EPMC7394709 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature