Identification of Bacterial Blight Resistant Rice Seeds Using Terahertz Imaging and Hyperspectral Imaging Combined With Convolutional Neural Network

Zhang, Jinnuo; Yang, Yong; Feng, Xuping; Xu, Hongxia; Chen, Jianping; He, Yong

doi:10.3389/fpls.2020.00821

Cited by 48 publications

(45 citation statements)

References 48 publications

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“…The t-SNE is a new technique in ML, which has been employed in biological data analysis ( Grimes et al, 2013 ; Irish, 2014 ; Dimitriadis et al, 2018 ). It has also been successfully applied to visualize the infected rice leaf data in Zhang et al (2020) . In our work, t-SNE was used to visualize distinctions among positive (infected) and negative (healthy) samples.…”

Section: Methodsmentioning

confidence: 99%

Machine Learning Techniques for Soybean Charcoal Rot Disease Prediction

et al. 2020

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Early prediction of pathogen infestation is a key factor to reduce the disease spread in plants. Macrophomina phaseolina (Tassi) Goid, as one of the main causes of charcoal rot disease, suppresses the plant productivity significantly. Charcoal rot disease is one of the most severe threats to soybean productivity. Prediction of this disease in soybeans is very tedious and non-practical using traditional approaches. Machine learning (ML) techniques have recently gained substantial traction across numerous domains. ML methods can be applied to detect plant diseases, prior to the full appearance of symptoms. In this paper, several ML techniques were developed and examined for prediction of charcoal rot disease in soybean for a cohort of 2,000 healthy and infected plants. A hybrid set of physiological and morphological features were suggested as inputs to the ML models. All developed ML models were performed better than 90% in terms of accuracy. Gradient Tree Boosting (GBT) was the best performing classifier which obtained 96.25% and 97.33% in terms of sensitivity and specificity. Our findings supported the applicability of ML especially GBT for charcoal rot disease prediction in a real environment. Moreover, our analysis demonstrated the importance of including physiological featured in the learning. The collected dataset and source code can be found in https://github.com/Elham-khalili/Soybean-Charcoal-Rot-Disease-Prediction-Dataset-code.

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Section: Methodsmentioning

confidence: 99%

Machine Learning Techniques for Soybean Charcoal Rot Disease Prediction

et al. 2020

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“…The algorithm of modeling is a method of rapid development which may extract the information characteristics, classify, and characterize them in an efficient form. With the rapid development of algorithm, more studies focusing on the implementation of object detection, classification of images and other fields of computational vision [12].…”

Section: Electromagnetic Spectrum Region Frequency Range (Hz) Radiation Sourcementioning

confidence: 99%

“…Other potential use of THz spectrometry is the monitoring and quantification of carbohydrate molecules in plants by identifying a peak at the THz spectrum related to this carbohydrate. Transgenic rice seeds may be identified with an excellent assurance rate due to conformational alterations of their carbohydrate molecules [16]. In addition, THz may be capable to determine water status of plants under drought conditions [17].…”

Section: Applications On Food Industry and Environmentmentioning

confidence: 99%

Terahertz Spectroscopy Applied to Diagnostics in Public Health: A Review

Almeida

Schiavo

Esmanhoto

et al. 2021

Braz. arch. biol. technol.

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Terahertz (THz) spectroscopy is an emerging technology that is that is bringing a number of technical breakthroughs in several scientific applications. This review aimed to describe potential applications of THz spectroscopy at the biochemistry and molecules detection for food industry, environment monitoring and diagnostics, and present the importance of such technological platform in disease control and Public Health.

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“…Due to the significant differences in climate, soil, and water resources in different regions, breeders have pointedly developed many crop varieties to adapt to the local planting environment. Growth rules, stress resistance, and biochemical characteristics of different varieties of crops vary greatly (Du et al, 2017;Zhang et al, 2020). For varieties that are still in the breeding stage, screening a variety with specific traits often needs to observe the phenotypic traits of the offspring plants, which is time-consuming and labor-intensive.…”

Section: Introductionmentioning

confidence: 99%

Rapid and Accurate Varieties Classification of Different Crop Seeds Under Sample-Limited Condition Based on Hyperspectral Imaging and Deep Transfer Learning

Liu

Meng

et al. 2021

Front. Bioeng. Biotechnol.

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Rapid varieties classification of crop seeds is significant for breeders to screen out seeds with specific traits and market regulators to detect seed purity. However, collecting high-quality, large-scale samples takes high costs in some cases, making it difficult to build an accurate classification model. This study aimed to explore a rapid and accurate method for varieties classification of different crop seeds under the sample-limited condition based on hyperspectral imaging (HSI) and deep transfer learning. Three deep neural networks with typical structures were designed based on a sample-rich Pea dataset. Obtained the highest accuracy of 99.57%, VGG-MODEL was transferred to classify four target datasets (rice, oat, wheat, and cotton) with limited samples. Accuracies of the deep transferred model achieved 95, 99, 80.8, and 83.86% on the four datasets, respectively. Using training sets with different sizes, the deep transferred model could always obtain higher performance than other traditional methods. The visualization of the deep features and classification results confirmed the portability of the shared features of seed spectra, providing an interpreted method for rapid and accurate varieties classification of crop seeds. The overall results showed great superiority of HSI combined with deep transfer learning for seed detection under sample-limited condition. This study provided a new idea for facilitating a crop germplasm screening process under the scenario of sample scarcity and the detection of other qualities of crop seeds under sample-limited condition based on HSI.

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Identification of Bacterial Blight Resistant Rice Seeds Using Terahertz Imaging and Hyperspectral Imaging Combined With Convolutional Neural Network

Cited by 48 publications

References 48 publications

Machine Learning Techniques for Soybean Charcoal Rot Disease Prediction

Machine Learning Techniques for Soybean Charcoal Rot Disease Prediction

Terahertz Spectroscopy Applied to Diagnostics in Public Health: A Review

Rapid and Accurate Varieties Classification of Different Crop Seeds Under Sample-Limited Condition Based on Hyperspectral Imaging and Deep Transfer Learning

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