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The Changbai Mountains comprise one of the main distribution areas of A. victorialis in China, and this species is endangered owing to habitat changes and overexploitation. However, A. victorialis germplasms have not been systematically collected and studied. The aims of this study were to obtain some detailed genetic information, analyze the genetic diversity, and further promote the protection of A. victorialis germplasms from the Changbai Mountains. Transcriptomic analysis was performed with six A. victorialis samples collected from the Changbai Mountains. At least 146,759 genes for each sample were obtained after performing de novo assembly of the RNA-seq data, and at least 92% of these genes were found to have only one mRNA isoform. These sequences and their functional annotations provided a large-scale genetic resource of this species. Phylogenetic analysis showed that A. victorialis was genetically distant from some related species, e.g. Allium sativum, Allium fistulosum, and Allium cepa, but genetically close to Allium tuberosum. The two A. victorialis var. listera samples were phylogenetically separated from the other four samples, and these two samples should be regarded as Allium listera. In addition, two KASP markers for discriminating the Dongfeng samples from the other four A. victorialis samples were successfully developed. This study lays the foundation for future studies on the genetic diversity and evolution of Allium species, as well as for the conservation of A. victorialis germplasms from the Changbai Mountains and other populations of this species.

To explore the relationship between the dry matter contents of potato tubers and metabolic enzymes, an experiment was designed. Ten individual indicators, including metabolic enzymes, could be reduced to 4 comprehensive indicators by using a principal component analysis. Using these 4 components as independent variables (X) and the dry matter content as the dependent variable (Y), the following linear regression equation was obtained: Y = ‒51.802 ‒ 1.022X5 ‒ 0.034X6 + 0.872X9 + 0.286X10 (R2 = 0.889, P = 0.012). In a comparison between the dry matter contents (22.74, 16.58 and 20.72%) calculated according to the equation and the measured value (22.96, 17.09 and 19.75%), the absolute error was < 1% and the estimation accuracy was > 95%. These results indicated that the linear regression equation can be used to predict accurately and quickly the dry matter contents during the tuber enlargement stage. Bangladesh J. Bot. 50(2): 253-259, 2021 (June)

Potato, a vegetable crop grown worldwide, has many uses, a short growth period, a large market demand and high economic benefits. The loss of potato seediness due to traditional potato growing methods is becoming increasingly evident, and research on new ways of growing potatoes is particularly important. Bud planting technology has the advantages of more reproduction, faster growth, and simplified maintenance of crop plants under cultivation.In this study, a bud planting method was adopted for the cultivation of potato seedlings. Specifically, we assessed different types of treatments for the production of high-quality buds and seedlings of potato. A total of four disease-free potato varieties (Fujin, Youjin, Zhongshu 4, and Feiwuruita) were selected, potato buds with three different lengths (3 cm, 5 cm, and 7 cm) were considered the TCultivation of seedlings from potato buds of different lengths increased the reproduction coefficient and reduced the number of seed potatoes needed for cultivation. All morphological, physiological, and yield indices showed positive trends. A potato bud length of 7 cm was optimal for raising seedlings. Moreover, buds located at the terminal of the potato yielded seedlings with the best quality. In conclusion, we recommend that our proven bud planting technique be adopted at the commercial level, which could support good crop production with maximum yield.

With the development of industrialization and urbanization, the consumption and pollution of water resources are becoming more and more serious. Water quality monitoring is an extremely important technical means to protect water resources. However, the current popular water quality monitoring methods have their shortcomings, such as a low signal-to-noise ratio of satellites, poor time continuity of unmanned aerial vehicles, and frequent maintenance of in situ underwater probes. A non-contact near-surface system that can continuously monitor water quality fluctuation is urgently needed. This study proposes an automatic near-surface water quality monitoring system, which can complete the physical equipment construction, data collection, and processing of the application scenario, prove the feasibility of the self-developed equipment and methods and obtain high-performance retrieval results of four water quality parameters, namely chemical oxygen demand (COD), turbidity, ammoniacal nitrogen (NH3-N), and dissolved oxygen (DO). For each water quality parameter, fourteen machine learning algorithms were compared and evaluated with five assessment indexes. Because the ensemble learning models combine the prediction results of multiple basic learners, they have higher robustness in the prediction of water quality parameters. The optimal determination coefficients (R2) of COD, turbidity, NH3-N, and DO in the test dataset are 0.92, 0.98, 0.95, and 0.91, respectively. The results show the superiority of near-surface remote sensing, which has potential application value in inland, coastal, and various water bodies in the future.

As an important part of the "air–ground" integrated water quality monitoring system, the inversion of water quality from unmanned airborne hyperspectral image has attracted more and more attention. Meanwhile, unmanned aerial vehicles (UAVs) have the characteristics of small size, flexibility and quick response, and can complete the task of water environment detection in a large area, thus avoiding the difficulty in obtaining satellite data and the limitation of single-point monitoring by ground stations. Most researchers use UAV for water quality monitoring, they take water samples back to library or directly use portable sensors for measurement while flying drones at the same time. Due to the UAV speed and route planning, the actual sampling time and the UAV passing time cannot be guaranteed to be completely synchronized, and there will be a difference of a few minutes. For water quality parameters such as chromaticity (chroma), chlorophyll-a (chl-a), chemical oxygen demand (COD), etc., the changes in a few minutes are small and negligible. However, for the turbidity, especially in flowing water body, this value of it will change within a certain range. This phenomenon will lead to noise error in the measured suspended matter or turbidity, which will affect the performance of regression model and retrieval accuracy. In this study, to solve the quality problem of label data in a flowing water body, an unmanned airborne hyperspectral water quality retrieval experiment was carried out in the Xiao River in Xi’an, China, which verified the rationality and effectiveness of label denoising analysis of different water quality parameters. To identify noisy label instances efficiently, we proposed an instance selection scheme. Furthermore, considering the limitation of the dataset samples and the characteristic of regression task, we build a 1DCNN model combining a self attention mechanism (SAM) and the network achieves the best retrieving performance on turbidity and chroma data. The experiment results show that, for flowing water body, the noisy-label instance selection method can improve retrieval performance slightly on the COD parameter, but improve greatly on turbidity and chroma data.

Background Potato, a vegetable crop grown worldwide, has many uses, a short growth period, a large market demand and high economic benefits. The loss of potato seediness due to traditional potato growing methods is becoming increasingly evident, and research on new ways of growing potatoes is particularly important. Bud planting technology has the advantages of more reproduction, faster growth, and simplified maintenance of crop plants under cultivation. Methods In this study, a bud planting method was adopted for the cultivation of potato seedlings. Specifically, we assessed different types of treatments for the production of high-quality buds and seedlings of potato. A total of four disease-free potato varieties (Fujin, Youjin, Zhongshu 4, and Feiwuruita) were selected, potato buds with three different lengths (3 cm, 5 cm, and 7 cm) were considered the T1, T2, and T3 treatments, and terminal buds, middle buds, and tail buds were used as the T4, T5, and T6 treatments. A nutrient pot experiment was performed following a randomized complete block design (RCBD) with three replicates and a natural control (CK) treatment. Cultivation was performed with the common horticultural practices of weeding and hoeing applied as needed. The photosynthetic indices, physiological indices, growth indices and quality of potato seedlings and quality of potato buds were measured at two-week intervals, and yield indices were measured when the final crop was harvested 14 weeks after planting. Results and Conclusions Cultivation of seedlings from potato buds of different lengths increased the reproduction coefficient and reduced the number of seed potatoes needed for cultivation. All morphological, physiological, and yield indices showed positive trends. A potato bud length of 7 cm was optimal for raising seedlings. Moreover, buds located at the terminal of the potato yielded seedlings with the best quality. In conclusion, we recommend that our proven bud planting technique be adopted at the commercial level, which could support good crop production with maximum yield.

The diamondback moth, Plutella xylostella, is one of the most serious insect pests of cruciferous crops all over the world. Although chemical insecticides are used to control this pest, it develops resistance to almost all kinds of insecticides. In recent, triterpenoid saponins have been reported to show potent antifeedant or synergize insecticidal activity on several pests. Therefore, to assess the antifeedant activity of ginsenosides against P. xylostella is benefit to biological control of this pests. Total ginsenosides were extracted from ginseng leaves and stems, and the total content of 9 ginsenosides was 72.91 mg/mL measured by high-performance liquid chromatography (HPLC) method. Total ginsenosides extracted from ginseng leaves and stems showed significant antifeedant activity on P. xylostella larvae in both non-choice and choice assays. The concentration of median antifeedant (AFC50) of total ginsenosides for the second instar larval of P. xylostella were 4.98 and 5.03 mg/mL at 24 h and 48 h respectively in non-choice assay, and they were 2.74 and 4.14 mg/mL respectively in choice assay. The residue rate of 9 ginsenosides in the pest became gradually higher as applied concentration increased. Feeding with ginsenosides generally resulted in decrease of glutathione S-transferase (GST), acetylcholine esterase (AChE), and carboxylesterase (CarE) activities, but increase of mixed-functional oxidase (MFO) activity in P. xylostella. These results indicate that ginsenosides are suitable for developing it into a botanical pesticide.

Tomato (Solanum lycopersicum L.) is native to Peru and Mexico. It is an important vegetable crop of the world, which ranks next to potato in importance. In addition, tomato is one of the most widel...

Potato (Solanum tuberosum L.) is one of the top five staple foods in China. It has high nutritional value, adaptability, and large yield. It is the third most important food crop in the world, seco...