Rice bacterial blight resistant cultivar selection based on visible/near-infrared spectrum and deep learning

Abstract

Background Rice bacterial blight (BB) has caused serious damage in rice yield and quality leading to huge economic loss and food safety problems. Breeding disease resistant cultivar becomes the eco-friendliest and most effective alternative to regulate its outburst, since the propagation of pathogenic bacteria is restrained. However, the BB resistance cultivar selection suffers tremendous labor cost, low efficiency, and subjective human error. And dynamic rice BB phenotyping study is absent from exploring the pattern of BB growth with different genotypes. Results In this paper, with the aim of alleviating the labor burden of plant breeding experts in the resistant cultivar screening processing and exploring the disease resistance phenotyping variation pattern, visible/near-infrared (VIS–NIR) hyperspectral images of rice leaves from three varieties after inoculation were collected and sent into a self-built deep learning model LPnet for disease severity assessment. The growth status of BB lesion at the time scale was fully revealed. On the strength of the attention mechanism inside LPnet, the most informative spectral features related to lesion proportion were further extracted and combined into a novel and refined leaf spectral index. The effectiveness and feasibility of the proposed wavelength combination were verified by identifying the resistant cultivar, assessing the resistant ability, and spectral image visualization. Conclusions This study illustrated that informative VIS–NIR spectrums coupled with attention deep learning had great potential to not only directly assess disease severity but also excavate spectral characteristics for rapid screening disease resistant cultivars in high-throughput phenotyping.

Evaluation of rice bacterial blight severity from lab to field with hyperspectral imaging technique

“…The ATT-LSTM model performed better than the SVM model. Compared with Zhang et al (2022)'s study on rice BB severity evaluation based on attention mechanism and convolutional neural network model, the accuracy of this study increased by 4.82%. The proposed ATT-LSTM is suitable for evaluating rice BB severity, and the attention block is conducive to feature selection.…”

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

“…However, it should be pointed out that the high dimension of spectral data limits the calculation speed to some extent, while the spectral index is a combination of several bands, which can obtain similar results while reducing the dimension ( Bloem et al, 2020 ). In this work, living plants were used to achieve non-destructive identification, which was different from in vitro leaves reported in previous study ( Zhang et al, 2022a ). The model constructed based on spectral index could accurately classify glyphosate resistant cultivars at 6 DAT (accuracy = 100% in Table 1 ), which was higher than previous study ( Feng et al, 2018 ), indicating the feasibility and effectiveness of spectral index in the identification of glyphosate resistant cultivar, and the detection performance was better than the sensitive wavelengths and sensitive chlorophyll fluorescence parameters.…”

“…First of all, it is worth noting that the treatment group of R, the control group of R, the treatment group of S, and the control group of S are designated as RT, RW, ST, and SW, respectively. Time-series visible/near-infrared hyperspectral images of alive maize plants were collected at 2, 4, 6, and 8 days after treatment (DAT) by a line-scan HSI system in the visible/near-infrared range (380–1,030 nm), which was reported in detail in previous study ( Zhang et al, 2022a ). Over the image acquisition, in order to facilitate the extraction of the spectrum of each leaf, it was necessary to ensure that leaves did not overlap with each other and the leaves were as flat as possible.…”

Novel Vegetation Indices to Identify Broccoli Plants Infected With Xanthomonas campestris pv. campestris

“…Similarly, NDVI values correlated well with the extension of the lesions caused by X. campestris pv. oryzae on rice leaves (Zhang et al, 2022). Moreover, several works have compared the association between climate change and the interannual variability registered on NDVI in several locations around the world (Kalisa et al, 2019;Bagherzadeh et al, 2020;Zhao et al, 2021).…”