文献类型：期刊文献

英文题名：Identification of Weeds Based on Hyperspectral Imaging and Machine Learning

作者：Li, Yanjie[1,5] Al-Sarayreh, Mahmoud[1] Irie, Kenji[2] Hackell, Deborah[3] Bourdot, Graeme[4] Reis, Marlon M.[1] Ghamkhar, Kioumars[1]

第一作者：李彦杰;Li, Yanjie

通信作者：Ghamkhar, K[1]

机构：[1]AgResearch Ltd, Grasslands Res Ctr, Palmerston North, New Zealand;[2]Red Fern Solut Ltd, Christchurch, New Zealand;[3]AgResearch Ltd, Ruakura Res Ctr, Hamilton, New Zealand;[4]AgResearch Ltd, Christchurch, New Zealand;[5]Chinese Acad Forestry, Res Inst Subtrop Forestry, Hangzhou, Peoples R China

年份：2021

卷号：11

外文期刊名：FRONTIERS IN PLANT SCIENCE

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000615722300001)】；

基金：Funding was provided by the Ministry of Business, Innovation, and Employment through the "Smart Ideas" scheme.

语种：英文

外文关键词：hyperspectral imaging; weeds classification; superpixel; PLS-DA; multilayer perceptron

摘要：Weeds can be major environmental and economic burdens in New Zealand. Traditional methods of weed control including manual and chemical approaches can be time consuming and costly. Some chemical herbicides may have negative environmental and human health impacts. One of the proposed important steps for providing alternatives to these traditional approaches is the automated identification and mapping of weeds. We used hyperspectral imaging data and machine learning to explore the possibility of fast, accurate and automated discrimination of weeds in pastures where ryegrass and clovers are the sown species. Hyperspectral images from two grasses (Setaria pumila [yellow bristle grass] and Stipa arundinacea [wind grass]) and two broad leaf weed species (Ranunculus acris [giant buttercup] and Cirsium arvense [Californian thistle]) were acquired and pre-processed using the standard normal variate method. We trained three classification models, namely partial least squares-discriminant analysis, support vector machine, and Multilayer Perceptron (MLP) using whole plant averaged (Av) spectra and superpixels (Sp) averaged spectra from each weed sample. All three classification models showed repeatable identification of four weeds using both Av and Sp spectra with a range of overall accuracy of 70-100%. However, MLP based on the Sp method produced the most reliable and robust prediction result (89.1% accuracy). Four significant spectral regions were found as highly informative for characterizing the four weed species and could form the basis for a rapid and efficient methodology for identifying weeds in ryegrass/clover pastures.