Hyperspectral classification of Cyperus esculentus clones and morphologically similar weeds

Cyperus esculentus (yellow nutsedge) is one of the world's worst weeds as it can cause great damage to crops and crop production. To eradicate C. esculentus, early detection is key-a challenging task as it is often confused with other Cyperaceae and displays wide genetic variability. In this study, the objective was to classify C. esculentus clones and morphologically similar weeds. Hyperspectral reflectance between 500 and 800 nm was tested as a measure to discriminate between (I) C. esculentus and morphologically similar Cyperaceae weeds, and between (II) different clonal populations of C. esculentus using three classification models: random forest (RF), regularized logistic regression (RLR) and partial least squares-discriminant analysis (PLS-DA). RLR performed better than RF and PLS-DA, and was able to adequately classify the samples. The possibility of creating an affordable multispectral sensing tool, for precise in-field recognition of C. esculentus plants based on fewer spectral bands, was tested. Results of this study were compared against simulated results from a commercially available multispectral camera with four spectral bands. The model created with customized bands performed almost equally well as the original PLS-DA or RLR model, and much better than the model describing multispectral image data from a commercially available camera. These results open up the opportunity to develop a dedicated robust tool for C. esculentus recognition based on four spectral bands and an appropriate classification model

Hyperspectral classification of poisonous solanaceous weeds in processing Phaseolus vulgaris L. and Spinacia oleracea L.

Poisonous weeds can occasionally unintentionally be co-harvested and pose a threat to human health as sepa-ration techniques during processing are not sufficient. Hence, elimination prior to harvest is required. For this reason, an exploratory study is performed to investigate the possibilities of an automatic detection system. The objective of this article is, firstly, to know if Phaseolus vulgaris and Spinacia oleracea are hyperspectrally separable from Solanum nigrum, Solanum tuberosum and Datura stramonium using spectrometer measurements. Secondly, the influence of different varieties/populations and of different pedohydrological and climatic conditions on this classification is investigated. Finally, it is examined whether it is possible to appoint discriminative wavelengths. To this means, the following analyses were performed: I and II) crop and weed species, and different populations or varieties of these species in varying conditions, were classified using hyperspectral spectrometer measure-ments and regularized logistic regression (RLR), III) data of consecutive years were investigated for similarities in order to indicate robust important regions in the electromagnetic spectrum with the use of RLR and IV) a subset of commercial off-the-shelf (COTS) filters was created for further research in the field. Results showed that the poisonous weed species D. stramonium, S. nigrum and S. tuberosum are hyperspectrally separable from the investigated crops. The accuracy of the two-class classification of poisonous weeds with S. oleracea and of these weeds with P. vulgaris was 0.982 and 0.977, respectively (I). Inclusion of different crop varieties, weed pop-ulations or different growing conditions in the model with S. oleracea and poisonous weeds resulted in a small decrease in weed recall (0.95 vs. 0.99) and crop precision (0.93 vs. 0.97). In future research, care must be taken to proper sample fields to cover the genetic variation present within weed populations and crop varieties, and diverse growing conditions (II). The bands selected using RLR did not show any consistency when using data of consecutive years and, therefore, RLR is not a suitable method to select robust wavelength regions for detection of poisonous weeds in vegetable crops to guide future research (III). With the use of COTS filters it was possible to select ten filters that worked sufficiently for both crops and are recommended for further research in the field. In addition, the authors recommend the use of a high resolution RGB camera to benefit from object-based image analysis to increase classification accuracy (IV)