Detection and coverage estimation of purple nutsedge in turf with image classification neural networks

Abstract

BACKGROUNDAccurate detection of weeds and estimation of their coverage is crucial for implementing precision herbicide applications. Deep learning (DL) techniques are typically used for weed detection and coverage estimation by analyzing information at the pixel or individual plant level, which requires a substantial amount of annotated data for training. This study aims to evaluate the effectiveness of using image classification neural networks (NNs) for detecting and estimating weed coverage in bermudagrass turf.RESULTSThe weed detection NNs, including DenseNet, GoogLeNet, and ResNet, exhibited high overall accuracy and F1 scores (≥0.971) throughout the k‐fold cross‐validation. DenseNet outperformed GoogLeNet and ResNet with the highest overall accuracy and F1 scores (0.977). Among the evaluated NNs, the DenseNet showed the highest overall accuracy and F1 scores (0.996) in the validation and testing datasets for estimating weed coverage. The inference speed of ResNet was similar to GoogLeNet but noticeably faster than DenseNet. ResNet was the most efficient and accurate deep convolution neural network (DCNN) for weed detection and coverage estimation.CONCLUSIONThese results demonstrated that the developed NNs could effectively detect weeds and estimate their coverage in bermudagrass turf, allowing the calculation of herbicide requirements for variable‐rate herbicide applications. The proposed method can be employed in a machine vision‐based autonomous site‐specific spraying system of smart sprayers.This article is protected by copyright. All rights reserved.