Abstract
In order to spray herbicides accurately on targets, this study focused on spectral classification of weeds and crops for potential to rapidly detect weeds in crop fields. A 350 ~ 2500 nm FieldSpec-FR spectroradiometer was used to measure spectral responses of the canopies of the seedling vegetables, cabbage ‘8398’ and cabbage ‘Zhonggan 11’, and weeds, Barnyard grass, green foxtail, goosegrass, crabgrass, and Chenopodium quinoa, at five- and seven-week growth stages (WGS). First, the characteristic wavelengths (CW) were determined using Principal Component Analysis (PCA). Then, the plants were classified using Bayesian discriminant analysis with the reflectance of the CWs. The results of spectral analysis indicated that the different growth stages of cabbages had little influence on the spectral identification of cabbages and weeds. The eight CWs determined were used as the input to the model for Bayesian discriminant analysis to classify two varieties of cabbages and five weeds with the correct classification rate of 84.3% for model testing. When the two varieties of cabbages were considered as the same category, the correct classification rate was improved to 100%. It was concluded that Bayesian discriminant analysis could be used to identify weeds from seedling cabbages using leaf hyperspectral reflectance. Key words: Weed identification, spectrum analysis, visible and near-infrared, Bayesian discriminant, seedling weed, seedling cabbage.
Highlights
IntroductionIt was reported that according to a leading environmental research organization, Land Care of New Zealand, weeds cause about $95 billion every year in the lost food production at global level, compared with $85 billion for pathogens, $46 billion for insects, and $2.4 billion for vertebrates (excluding humans)
Among the 8 characteristic wavelengths (CW) for each growth stage, only two of them were different, which indicates that different growth stages of the cabbages have limited impact on the plant spectral characteristics for identification of cabbages and weeds
(2) The corresponding spectral data of the 8 CWs determined from the data at the five week growth stages (WGS) were used as the input variables of the Bayesian discriminant model to classify two varieties of cabbages and five different weeds
Summary
It was reported that according to a leading environmental research organization, Land Care of New Zealand, weeds cause about $95 billion every year in the lost food production at global level, compared with $85 billion for pathogens, $46 billion for insects, and $2.4 billion for vertebrates (excluding humans). In China, the crop yield losses annually caused by weeds sum up to about 10% of the gross grain output (Tang, 2010). Facing the severity of the crop losses caused by weeds, it is urgent to seek highly efficient methods for effective weed control. The chemical weeding method commonly adopted at present has provoked a lot of problems, such as excessive pesticide residues, growing number of pesticide-resistant weeds, destruction of ecological environment, and lower quality and safety of agricultural products (Thompson et al, 1991). In order to minimize crop damage and environmental pollution, herbicides should be sprayed accurately on targets with appropriate dose
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