Identification of zinc pollution in rice plants based on two characteristic variables

The Classification of Rice Blast Resistant Seed Based on Ranman Spectroscopy and SVM

Rice blast is a serious threat to rice yield. Breeding disease-resistant varieties is one of the most economical and effective ways to prevent damage from rice blast. The traditional identification of resistant rice seeds has some shortcoming, such as long possession time, high cost and complex operation. The purpose of this study was to develop an optimal prediction model for determining resistant rice seeds using Ranman spectroscopy. First, the support vector machine (SVM), BP neural network (BP) and probabilistic neural network (PNN) models were initially established on the original spectral data. Second, due to the recognition accuracy of the Raw-SVM model, the running time was fast. The support vector machine model was selected for optimization, and four improved support vector machine models (ABC-SVM (artificial bee colony algorithm, ABC), IABC-SVM (improving the artificial bee colony algorithm, IABC), GSA-SVM (gravity search algorithm, GSA) and GWO-SVM (gray wolf algorithm, GWO)) were used to identify resistant rice seeds. The difference in modeling accuracy and running time between the improved support vector machine model established in feature wavelengths and full wavelengths (200–3202 cm⁻¹) was compared. Finally, five spectral preproccessing algorithms, Savitzky–Golay 1-Der (SGD), Savitzky–Golay Smoothing (SGS), baseline (Base), multivariate scatter correction (MSC) and standard normal variable (SNV), were used to preprocess the original spectra. The random forest algorithm (RF) was used to extract the characteristic wavelengths. After different spectral preproccessing algorithms and the RF feature extraction, the improved support vector machine models were established. The results show that the recognition accuracy of the optimal IABC-SVM model based on the original data was 71%. Among the five spectral preproccessing algorithms, the SNV algorithm’s accuracy was the best. The accuracy of the test set in the IABC-SVM model was 100%, and the running time was 13 s. After SNV algorithms and the RF feature extraction, the classification accuracy of the IABC-SVM model did not decrease, and the running time was shortened to 9 s. This demonstrates the feasibility and effectiveness of IABC in SVM parameter optimization, with higher prediction accuracy and better stability. Therefore, the improved support vector machine model based on Ranman spectroscopy can be applied to the fast and non-destructive identification of resistant rice seeds.

Transcriptome analysis provides new insight into the distribution and transport of selenium and its associated metals in selenium-rich rice

Selenium is an essential trace element for humans and obtained from diary diets. The consumption of selenium-rich agricultural food is an efficient way to obtain selenium, but the quality and safety of selenium-rich agro-food are always affected by their associated heavy metals, even poses a potential threaten to human health. In this research, a sampling survey of heavy metals contents in selenium-rich rice was conducted, 182 sets of selenium-rich rice samples were collected from five selenium-rich rice-producing areas of China, and the accumulation of selenium and cadmium were found to be associated in rice and soil. Subsequently, a pot experiment was performed in the greenhouse via treating the soil samples with 12 different concentrations of selenium and heavy metals, and the contents of selenium and cadmium in rice grain were confirmed to be significantly associated. Moreover, transcriptome analysis revealed that the up-regulation of transporter-coding may promote the absorption of selenium and cadmium. The expression of antioxidant-coding genes and cadmium chelator transporter coding-genes was up-regulated to reduce the toxicity of cadmium. Meanwhile, the up-regulation of key genes of the ascorbic acid-glutathione metabolic pathway were responsible for the association between selenium and cadmium in Se-rich rice. Our work suggested the correlation between selenium and cadmium accumulation in selenium-rich rice, clarified their accumulation mechanism, provides a direction for the scientific production of selenium-rich agro-foods.