Identification of Weeds Based on Hyperspectral Imaging and Machine Learning

Frontiers in Plant Science

Graeme Bourdot¹  Marlon M. Reis²  Kioumars Ghamkhar²  Yanjie Li²  Mahmoud Al-Sarayreh²  Deborah Hackell³  Kenji Irie⁴ 

[1] AgResearch Ltd., Christchurch, New Zealand;AgResearch Ltd., Grasslands Research Centre, Palmerston North, New Zealand;AgResearch Ltd., Ruakura Research Centre, Hamilton, New Zealand;Red Fern Solutions Ltd, Christchurch, New Zealand;

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

DOI  :  10.3389/fpls.2020.611622

来源: DOAJ

【 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.

【 授权许可】

Unknown