Combination of spectral index and transfer learning strategy for glyphosate-resistant cultivar identification

Abstract

Glyphosate is one of the most widely used non-selective herbicides, and the creation of glyphosate-resistant cultivars solves the problem of limited spraying area. Therefore, it is of great significance to quickly identify resistant cultivars without destruction during the development of superior cultivars. This work took maize seedlings as the experimental object, and the spectral indices of leaves were calculated to construct a model with good robustness that could be used in different experiments. Compared with no transfer strategies, transferability of support vector machine learning model was improved by randomly selecting 14% of source domain from target domain to train and applying transfer component analysis algorithm, the accuracy on target domain reached 83% (increased by 71%), recall increased from 10 to 100%, and F1-score increased from 0.17 to 0.86. The overall results showed that both transfer component analysis algorithm and updating source domain could improve the transferability of model among experiments, and these two transfer strategies could complement each other’s advantages to achieve the best classification performance. Therefore, this work is beneficial to timely understanding of the physiological status of plants, identifying glyphosate resistant cultivars, and ultimately provides theoretical basis and technical support for new cultivar creation and high-throughput selection.

Estimating the frost damage index in lettuce using UAV-based RGB and multispectral images

“…Frost damage to lettuce not only leads to changes of appearance, but also affects its physiological and biochemical indexes. Spectral imagers have an advantage in responding to changes in the physiological and biochemical indexes of crops due to their ability to detect reflectance spectra in the visible and near infrared wavelengths( Tao et al., 2022 ). This is the reason why spectral imagers were chosen in the most existing researches on frost damage of field crops( Macedo-Cruz et al., 2011 ; Wang et al., 2014 ; Yang et al., 2019 ; Lassalle, 2021 ).…”