Soil And Plant Analyzer Development Research Articles

Impact of Duckweed (Lemna minor L.) Growing in Paddy Fields on Rice Yield and Its Underlying Causes

Duckweed growing in paddy fields (DGP) has substantially increased because of the effects of climate warming and/or eutrophication in irrigated water. Previous studies have primarily focused on investigating the effects of DGP as a nonchemical agent for enhancing rice productivity on nitrogen utilization in rice paddy fields. However, how DGP impacts rice yield remains poorly understood. Therefore, a field experiment with three representative rice cultivars was conducted to determine the effects of DGP on rice yield, considering ecological factors, photosynthetic capacity, spectral changes, and plant growth. The results showed that DGP significantly reduced the pH value by 0.6 and the daily water temperature by 0.6 °C, accelerated rice heading by 1.6 days and increased the soil and plant analyzer development (SPAD) and photosynthetic rate of leaves by 10.8% and 14.4% on average, respectively. DGP also markedly enhanced the values of various vegetation indices such as RARSc, MTCI, GCI, NDVI705, CI, CIrededge, mND705, SR705, and GM, and the first derivative curve of the rice canopy reflectance spectrum exhibited a ‘red shift’ phenomenon upon DGP treatment. Changes in the aforementioned factors may lead to average increases of 4.7% in plant height, 15.0% in dry matter weight, 10.6% in panicles m−2, 2.3% in 1000-grain weight, and ultimately a 10.2% increase in grain yield. The correlation observed suggested that the DGP-induced enhancement in grain yield can be achieved by reducing the pH and temperature of the paddy water, thus enhancing the SPAD value and photosynthesis of leaves and stimulating rice plant growth. These results could offer valuable theoretical support for the future sustainable development of agriculture and the environment through the biological synergy between rice and duckweed.

Transcriptome Analysis of Intermittent Light Induced Early Bolting in Flowering Chinese Cabbage.

In flowering Chinese cabbage, early booting is one of the most important characteristics that is linked with quality and production. Through fixed light intensity (280 μmol·m-2·s-1) and fixed intermittent lighting in flowering Chinese cabbage, there was early bolting, bud emergence, and flowering. Moreover, the aboveground fresh weight, blade area, dry weight of blade, and quantification of the leaves in flowering Chinese cabbage were significantly reduced, while the thickness of tillers, tillers height, dry weight of tillers, and tillers weight were significantly increased. The chlorophyll contents and soil-plant analysis and development (SPAD) value decreased in the early stage and increased in the later stage. The nitrate content decreased, while the photosynthetic rate, vitamin C content, soluble sugar content, soluble protein content, phenolic content, and flavonoid content increased, and mineral elements also accumulated. In order to explore the mechanism of intermittent light promoting the early bolting and flowering of '49d' flowering Chinese cabbage, this study analyzed the transcriptional regulation from a global perspective using RNA sequencing. A total of 17,086 differentially expressed genes (DEGs) were obtained and 396 DEGs were selected that were closely related to early bolting. These DEGs were mainly involved in pollen wall assembly and plant circadian rhythm pathways, light action (34 DEGs), hormone biosynthesis and regulation (26 DEGs), development (21 DEGs), and carbohydrate synthesis and transport (6 DEGs). Three hub genes with the highest connectivity were identified through weighted gene co-expression network analysis (WGCNA): BrRVE, BrLHY, and BrRVE1. It is speculated that they may be involved in the intermittent light regulation of early bolting in flowering Chinese cabbage. In conclusion, intermittent light can be used as a useful tool to regulate plant growth structure, increase planting density, enhance photosynthesis, increase mineral accumulation, accelerate growth, and shorten the breeding cycle.

Estimating the SPAD of Litchi in the Growth Period and Autumn Shoot Period Based on UAV Multi-Spectrum

The relative content of chlorophyll, assessed through the soil and plant analyzer development (SPAD), serves as a reliable indicator reflecting crop photosynthesis and the nutritional status during crop growth and development. In this study, we employed machine learning methods utilizing unmanned aerial vehicle (UAV) multi-spectrum remote sensing to predict the SPAD value of litchi fruit. Input features consisted of various vegetation indices and texture features during distinct growth periods, and to streamline the feature set, the full subset regression algorithm was applied for dimensionality reduction. Our findings revealed the superiority of stacking models over individual models. During the litchi fruit development period, the stacking model, incorporating vegetation indices and texture features, demonstrated a validation set coefficient of determination (R2) of 0.94, a root mean square error (RMSE) of 2.4, and a relative percent deviation (RPD) of 3.0. Similarly, in the combined litchi growing period and autumn shoot period, the optimal model for estimating litchi SPAD was the stacking model based on vegetation indices and texture features, yielding a validation set R2, RMSE, and RPD of 0.84, 3.9, and 1.9, respectively. This study furnishes data support for the precise estimation of litchi SPAD across different periods through varied combinations of independent variables.

CatBoost algorithm for estimating maize above-ground biomass using unmanned aerial vehicle-based multi-source sensor data and SPAD values

The rapid and accurate estimation of maize above-ground biomass (AGB) is pivotal for precise agricultural management. The rapid evolution of unmanned aerial vehicles (UAVs) and sensor technology has introduced a novel method for obtaining AGB information. Nevertheless, individual sensors may lack comprehensive data, leading to reduced AGB estimation accuracy in certain scenarios. This study collected UAV multi-spectral (MS) and thermal infrared (TIR) data, alongside soil and plant analyzer development (SPAD) values, from maize across multiple growth stages (jointing, trumpet, and big trumpet) during 2022 and 2023. Diverse data fusion programs were devised to explore the potential of combining multi-source sensor data with SPAD values to estimate AGB. The efficacy of CatBoost was evaluated and benchmarked against Support Vector Regression (SVR) and Random Forest Regression (RFR) algorithms. For the entire growth, findings reveal that the fusion of multi-source sensor data (MS + TIR) can mitigate the data insufficiency in single-sensor estimations. The resulting R2 values range from 0.608 to 0.817. Optimal estimation outcomes were achieved by the fusion of multi-source sensor data with SPAD values (MS + TIR + SPAD), yielding R2 values ranging from 0.685 to 0.872. For a single growth stage, there are variations in the estimation accuracy across different growth stages. From the jointing stage to the big trumpet stage, the estimation accuracy consistently increases, with the highest accuracy observed during the big trumpet stage, with R2 ranging from 0.721 to 0.901. Additionally, in alignment with the results for the entire growth stage, the fusion of multi-source sensor data with SPAD values still yields the highest estimation accuracy during different growth stages. In a comparison of different machine learning algorithms, for both the entire growth stage and single growth stages, SVR, RFR, and CatBoost achieved R2 values ranging from 0.305 to 0.824, 0.368 to 0.881, and 0.451 to 0.901, respectively. Notably, the CatBoost algorithm exhibited heightened estimation accuracy. The fusion of multi-source sensor data with SPAD values combined with the CatBoost algorithm results in accurate and reliable maize AGB estimation accuracy. This high-throughput approach to crop phenotyping is characterized by speed and accuracy and serves as a valuable reference for rapidly acquiring AGB information in this geographical region.

Integrated Transcriptomic and Metabolomics Analyses Reveal Molecular Responses to Cold Stress in Coconut (Cocos nucifera L.) Seedlings.

Coconut is an important tropical and subtropical fruit and oil crop severely affected by cold temperature, limiting its distribution and application. Thus, studying its low-temperature reaction mechanism is required to expand its cultivation range. We used growth morphology and physiological analyses to characterize the response of coconuts to 10, 20, and 30 d of low temperatures, combined with transcriptome and metabolome analysis. Low-temperature treatment significantly reduced the plant height and dry weight of coconut seedlings. The contents of soil and plant analyzer development (SPAD), soluble sugar (SS), soluble protein (SP), proline (Pro), and malondialdehyde (MDA) in leaves were significantly increased, along with the activities of superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT), and the endogenous hormones abscisic acid (ABA), auxin (IAA), zeatin (ZR), and gibberellin (GA) contents. A large number of differentially expressed genes (DEGs) (9968) were detected under low-temperature conditions. Most DEGs were involved in mitogen-activated protein kinase (MAPK) signaling pathway-plant, plant hormone signal transduction, plant-pathogen interaction, biosynthesis of amino acids, amino sugar and nucleotide sugar metabolism, carbon metabolism, starch and sucrose metabolism, purine metabolism, and phenylpropanoid biosynthesis pathways. Transcription factors (TFs), including WRKY, AP2/ERF, HSF, bZIP, MYB, and bHLH families, were induced to significantly differentially express under cold stress. In addition, most genes associated with major cold-tolerance pathways, such as the ICE-CBF-COR, MAPK signaling, and endogenous hormones and their signaling pathways, were significantly up-regulated. Under low temperatures, a total of 205 differentially accumulated metabolites (DAMs) were enriched; 206 DAMs were in positive-ion mode and 97 in negative-ion mode, mainly including phenylpropanoids and polyketides, lipids and lipid-like molecules, benzenoids, organoheterocyclic compounds, organic oxygen compounds, organic acids and derivatives, nucleosides, nucleotides, and analogues. Comprehensive metabolome and transcriptome analysis revealed that the related genes and metabolites were mainly enriched in amino acid, flavonoid, carbohydrate, lipid, and nucleotide metabolism pathways under cold stress. Together, the results of this study provide important insights into the response of coconuts to cold stress, which will reveal the underlying molecular mechanisms and help in coconut screening and breeding.

UAV Hyperspectral Data Combined with Machine Learning for Winter Wheat Canopy SPAD Values Estimation

Chlorophyll is an important indicator for monitoring crop growth and is vital for agricultural management. Therefore, rapid and accurate estimation of chlorophyll content is important for decision support in precision agriculture to accurately monitor the SPAD (Soil and Plant Analyzer Development) values of winter wheat. This study used winter wheat to obtain canopy reflectance based on UAV hyperspectral data and to calculate different vegetation indices and red-edge parameters. The best-performing vegetation indices and red-edge parameters were selected by Pearson correlation analysis and multiple stepwise regression (MSR). SPAD values were estimated using a combination of vegetation indices, vegetation indices and red-edge parameters as model factors, two types of machine learning (ML), a support vector machine (SVM), and a backward propagation neural network (BPNN), and partial least squares regression (PLSR) for four growth stages of winter wheat, and validated using independent samples. The results show that for the same data source, the best vegetation indices or red-edge parameters for estimating SPAD values differed at different growth stages and that combining vegetation indices with red-edge parameters gave better estimates than using only vegetation indices as an input factor for estimating SPAD values. There is no significant difference between PLSR, SVM, and BPNN methods in estimating SPAD values, with better stability of the estimated models using machine learning methods. Different growth stages have a large impact on winter wheat SPAD values estimates, with the accuracy of the four growth stage models increasing in the following order: booting < heading < filling < flowering. This study shows that using a combination of vegetation indices and red-edge parameters can improve SPAD values estimates compared to using vegetation indices alone. In the future, the choice of appropriate factors and methods will need to be considered when constructing models to estimate crop SPAD values.

Estimation of Winter Wheat SPAD Values Based on UAV Multispectral Remote Sensing

Unmanned aerial vehicle (UAV) multispectral imagery has been applied in the remote sensing of wheat SPAD (Soil and Plant Analyzer Development) values. However, existing research has yet to consider the influence of different growth stages and UAV flight altitudes on the accuracy of SPAD estimation. This study aims to optimize UAV flight strategies and incorporate multiple feature selection techniques and machine learning algorithms to enhance the accuracy of the SPAD value estimation of different wheat varieties across growth stages. This study sets two flight altitudes (20 and 40 m). Multispectral images were collected for four winter wheat varieties during the green-up and jointing stages. Three feature selection methods (Pearson, recursive feature elimination (RFE), and correlation-based feature selection (CFS)) and four machine learning regression models (elastic net, random forest (RF), backpropagation neural network (BPNN), and extreme gradient boosting (XGBoost)) were combined to construct SPAD value estimation models for individual growth stages as well as across growth stages. The CFS-RF (40 m) model achieved satisfactory results (green-up stage: R2 = 0.7270, RPD = 2.0672, RMSE = 1.1835, RRMSE = 0.0259; jointing stage: R2 = 0.8092, RPD = 2.3698, RMSE = 2.3650, RRMSE = 0.0487). For cross-growth stage modeling, the optimal prediction results for SPAD values were achieved at a flight altitude of 40 m using the Pearson-XGBoost model (R2 = 0.8069, RPD = 2.3135, RMSE = 2.0911, RRMSE = 0.0442). These demonstrate that the flight altitude of UAVs significantly impacts the estimation accuracy, and the flight altitude of 40 m (with a spatial resolution of 2.12 cm) achieves better SPAD value estimation than that of 20 m (with a spatial resolution of 1.06 cm). This study also showed that the optimal combination of feature selection methods and machine learning algorithms can more accurately estimate winter wheat SPAD values. In addition, this study includes multiple winter wheat varieties, enhancing the generalizability of the research results and facilitating future real-time and rapid monitoring of winter wheat growth.

Estimating Relative Chlorophyll Content in Rice Leaves Using Unmanned Aerial Vehicle Multi-Spectral Images and Spectral–Textural Analysis

Leaf chlorophyll content is crucial for monitoring plant growth and photosynthetic capacity. The Soil and Plant Analysis Development (SPAD) values are widely utilized as a relative chlorophyll content index in ecological agricultural surveys and vegetation remote sensing applications. Multi-spectral cameras are a cost-effective alternative to hyperspectral cameras for agricultural monitoring. However, the limited spectral bands of multi-spectral cameras restrict the number of vegetation indices (VIs) that can be synthesized, necessitating the exploration of other options for SPAD estimation. This study evaluated the impact of using texture indices (TIs) and VIs, alone or in combination, for estimating rice SPAD values during different growth stages. A multi-spectral camera was attached to an unmanned aerial vehicle (UAV) to collect remote sensing images of the rice canopy, with manual SPAD measurements taken immediately after each flight. Random forest (RF) was employed as the regression method, and evaluation metrics included coefficient of determination (R2) and root mean squared error (RMSE). The study found that textural information extracted from multi-spectral images could effectively assess the SPAD values of rice. Constructing TIs by combining two textural feature values (TFVs) further improved the correlation of textural information with SPAD. Utilizing both VIs and TIs demonstrated superior performance throughout all growth stages. The model works well in estimating the rice SPAD in an independent experiment in 2022, proving that the model has good generalization ability. The results suggest that incorporating both spectral and textural data can enhance the precision of rice SPAD estimation throughout all growth stages, compared to using spectral data alone. These findings are of significant importance in the fields of precision agriculture and environmental protection.

The prediction model of nitrogen nutrition in cotton canopy leaves based on hyperspectral visible-near infrared band feature fusion.

Hyperspectral remote sensing technology is becoming increasingly popular in various fields due to its ability to provide detailed information about crop growth and nutritional status. The use of hyperspectral technology to predict SPAD (Soil and Plant Analyzer Development) values during cotton growth and adopt precise fertilization management measures is crucial for achieving high yield and fertilizer efficiency. To detect the nitrogen nutrition in cotton canopy leaves quickly, a non-destructive nitrogen nutrition retrieval model was proposed based on the spectral fusion features of the cotton canopy. The hyperspectral vegetation index and multifractal features were fused to predict the SPAD value and identify the amount of fertilizer applied at different levels. The random decision forest algorithm was used as the model predictor and classifier. A method was introduced which was widely used in the fields of finance and stocks (MF-DFA) into the field of agriculture to extract fractal features of cotton spectral reflectance. Comparing the fusion feature with multi-fractal feature and vegetation index, the results showed that the fusion feature parameters had higher accuracy and better stability than using a single feature or feature combination. The R2 was as high as 0.8363, and the RMSE was 1.8767%. Our intelligent model provides a new idea for detecting nitrogen nutrition in cotton canopy leaves rapidly.

Study on Monitoring SPAD Values for Multispatial Spatial Vertical Scales of Summer Maize Based on UAV Multispectral Remote Sensing

Rapid acquisition of chlorophyll content in maize leaves is of great significance for timely monitoring of maize plant health and guiding field management. In order to accurately detect the relative chlorophyll content of summer maize and study the responsiveness of vegetation indices to SPAD (soil and plant analyzer development) values of summer maize at different spatial vertical scales, this paper established a prediction model for SPAD values of summer maize leaves at different spatial scales based on UAV multispectral images. The experiment collected multispectral image data from summer maize at the jointing stage and selected eight vegetation indices. By using the sparrow search optimized kernel limit learning machine (SSA-KELM), the prediction models for canopy leaf (CL) SPADCL and ear leaf (EL) SPADEL were established, and a linear fitting analysis was conducted combining the measured SPADCL values and SPADEL values on the ground. The results showed that for SPADCL, the R2 of the linear fitting between the predicted values and measured values was 0.899, and the RMSE was 1.068. For SPADEL, the R2 of linear fitting between the predicted values and the measured values was 0.837, and the RMSE was 0.89. Compared with the model established by the partial least squares method (PLSR), it is found that the sparrow search optimized kernel limit learning machine (SSA-KELM) has more precise prediction results with better stability and adaptability for small sample prediction. The research results can provide technical support for remote sensing monitoring of the chlorophyll content of summer maize at different spatial scales.

Multi-Model Rice Canopy Chlorophyll Content Inversion Based on UAV Hyperspectral Images

Rice is China’s main crop and its output accounts for 30% of the world’s total annual rice production. Rice growth status is closely related to chlorophyll content (called Soil and Plant Analyzer Development (SPAD) values). The determination of a SPAD value is of great significance to the health status of rice, agricultural irrigation and regulated fertilization. The traditional SPAD value measurement method is not only time-consuming, laborious and expensive but also causes irreparable damage to vegetation. The main aim of the present study is to obtain a SPAD value through the inversion of hyperspectral remote sensing images. In order to achieve this purpose, the hyperspectral image of rice at different growth stages at the canopy scale was first acquired using a hyperspectral imaging instrument equipped with a drone; the spectral characteristics of the rice canopy at different growth stages were analyzed and combined with a ground-level measured SPAD value, the bands with high correlation between the SPAD values and the spectra of the rice canopy at different fertility stages were selected. Subsequently, we combined the spectral characteristics with the continuous projection algorithm to extract the characteristic band and used the PLS method in MATLAB software to analyze and calculate the weight of each type of spectral value and the corresponding canopy SPAD value; we then used the wavelength corresponding to the spectral value with the highest weight as the used band. Secondly, the four methods of univariate regression, partial least squares (PLS) regression, support vector machine (SVM) regression and back propagation (BP) neural network regression are integrated to establish the estimation model of the SPAD value of rice canopy. Finally, the models are used to map the SPAD values of the rice canopy. Research shows that the model with the highest decision coefficient among the four booting stage models is “booting stage-SVR” (R2 = 0.6258), and the model with the highest decision coefficient among the four dairy maturity models is “milk-ripe stage-BP” (R2 = 0.6716), all of which can meet the requirement of accurately retrieving the SPAD value of rice canopy. The above results can provide a technical reference for the accurate, rapid and non-destructive monitoring of chlorophyll content in rice leaves and provide a core band selection basis for large-scale hyperspectral remote sensing monitoring of rice.

Quantification of Physiological Parameters of Rice Varieties Based on Multi-Spectral Remote Sensing and Machine Learning Models

Estimating plant physiological indicators with remote sensing technology is critical for ensuring precise field management. Compared with other remote sensing platforms, low-altitude unmanned aerial vehicles (UAVs) produce images with high spatial resolution that can be used to clearly identify vegetation. However, the information of UAV image data is relatively complex and difficult to analyze, which is the main problem limiting its large-scale use at present. In order to monitor plant physiological indexes from the multi-spectral data, a new method based on machine learning is studied in this paper. Using UAV for deriving the absorption coefficients of plant canopies and whole leaf area, this paper quantifies the effects of plant physiological indicators such as the soil and plant analyzer development (SPAD) value, whole leaf area, and dry matter accumulation on the relationship between the reflectance spectra. Nine vegetation indexes were then extracted as the sensitive vegetation indexes of the rice physiological indicators. Using the SVM model to predict the SPAD value of the plant, the mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and symmetric mean absolute percentage error (SMAPE) values of the model were 1.90, 1.38, 0.13, 0.86, and 4.13, respectively. The results demonstrate that the rice plants display a considerable biochemical and spectral correlation. Using SVM to predict the SPAD value has a better effect because of a better adaptation and a higher accuracy than other models. This study suggests that the multi-spectral data acquired using UAV can quickly estimate field physiological indicators, which has potential in the pre-visual detection of SPAD value information in the field. At the same time, it can also be extended to the detection and inversion of other key variables of crops.

Optimal Irrigation Levels Can Improve Maize Growth, Yield, and Water Use Efficiency under Drip Irrigation in Northwest China

Drip irrigation systems are becoming more and more mature, and are presently extensively applied to increase crop yield and water use efficiency. In order to investigate the effects of irrigation quota on maize growth, the grain yield, and the water use efficiency (WUE), a field experiment with four irrigation quotas (T1 420 mm, T2 480 mm, T3 540 mm, and T4 600 mm) was conducted from 2013 to 2021 in Xinjiang, China. The results showed significant changes in maize growth, yield, and WUE in response to different irrigation quotas. The plant height, leaf area index, soil and plant analyzer development (SPAD), dry matter accumulation, yield, and harvest index of maize at different irrigation quotas all showed a ‘single peak curve’, and its change was closely related to the irrigation level. The growth index, dry matter accumulation, yield, and irrigation water use efficiency with T3 were the highest. The dry matter transfer efficiency, contribution of dry matter translocation to grain, and the harvest index with T3 showed a significant increase of 13.86%, 26.06%, 29.93%, and 7.62% compared to T1, respectively. In comparison to T1, T2, and T4, the yield of T3 increased by 32.17%, 13.54%, and 11.27%, respectively, and the WUE increased by 16.56%, 6.49%, and 23.70%, respectively. The significant correlations established between the maize yield and irrigation quotas could be simulated by a Kuznets-style relation. The maize yield was negatively correlated with irrigation quotas. When the irrigation quota (x) was 539.12 mm, the maize yield (y) was 16043.92 kg·hm−2. These results demonstrate that the optimized irrigation quota (540 mm) can effectively improve the growth, yield, and WUE of drip irrigation maize in northwest China. Meanwhile, it can provide a theoretical reference and data support for the optimal amount of irrigation for drip irrigation maize in Xinjiang China.

Effects of hydrogen peroxide priming on yield, photosynthetic capacity and chlorophyll fluorescence of waterlogged summer maize.

Extreme rainfall events during the summer maize growth and development periods, which have induced losses in summer maize production. There was a completely randomized block experiment being designed with four treatments: waterlogging for 6 days at the V3 stage (C-W), H2O2-priming + non-waterlogging (H-CK), H2O2-priming + waterlogging for 6 days at the V3 stage (H-W) and control (C-CK). This study investigated the effects of H2O2 priming on yield and photosynthetic parameters of (Zea mays. L) summer maize hybrid DengHai605 (DH605) by measuring the leaf area index (LAI), soil and plant analyzer development (SPAD) value, stomatal morphology, gas exchange parameters, and chlorophyll fluorescence parameters. The results showed that the net photosynthetic rate (Pn) was decreased after waterlogging through the stomatal limitation of CO2 supply and reduction of PSII photochemical efficiency, which led to the decrease in dry matter accumulation and grain yield. H2O2 priming increased the number of opening stomas, the stomatal length, and width, thus increasing Ci by 12.1%, which enhanced the Pn by 37.5%. Additionally, H2O2 priming could improve the energy of dark reaction carbohydrates by increasing the light energy absorption and utilization, alleviating the function of PSII reaction centers, protecting the PSII receptor and donor side, and the electron transport chain. The φEo, φPo, φRo, and Ψo of H-W were increased by 89.9%, 16.2%, 55.4%, and 63.9% respectively, and the φDo was decreased by 23.5%, compared with C-W. Therefore, H2O2 priming significantly enhanced the PSII photochemical efficiency, and increased the CO2 supply in dark reactions to promote carbon assimilation, alleviating the waterlogging-induced damage to maize plant growth and grain yield.

Comparative Transcriptome Analysis of Two Peach Rootstocks Uncovers the Effect of Gene Differential Expression on Nitrogen Use Efficiency.

Nitrogen is an important nutrient element that limits plant growth and yield formation, but excessive nitrogen has negative effects on plants and the environment. It is important to reveal the molecular mechanism of high NUE (nitrogen use efficiency) for breeding peach rootstock and variety with high NUE. In this study, two peach rootstocks, Shannong–1 (S) and Maotao (M), with different NUE were used as materials and treated with 0.1 mM KNO3 for transcriptome sequencing together with the control group. From the results of comparison between groups, we found that the two rootstocks had different responses to KNO3, and 2151 (KCL_S vs. KCL_M), 327 (KNO3_S vs. KCL_S), 2200 (KNO3_S vs. KNO3_M) and 146 (KNO3_M vs. KCL_M) differentially expressed genes (DEGs) were identified, respectively, which included multiple transcription factor families. These DEGs were enriched in many biological processes and signal transduction pathways, including nitrogen metabolism and plant hormone signal transduction. The function of PpNRT2.1, which showed up-regulated expression under KNO3 treatment, was verified by heterologous expression in Arabidopsis. The plant height, SPAD (soil and plant analyzer development) of leaf and primary root length of the transgenic plants were increased compared with those of WT, indicating the roles of PpNRT2.1 in nitrogen metabolism. The study uncovered for the first time the different molecular regulatory pathways involved in nitrogen metabolism between two peach rootstocks and provided gene reserve for studying the molecular mechanism of nitrogen metabolism and theoretical basis for screening peach rootstock or variety with high NUE.

Estimation of chlorophyll distribution in banana canopy based on RGB-NIR image correction for uneven illumination

Chlorophyll generally measured by Soil and Plant Analysis Development (SPAD) meter is an important indicator for the evaluation of plant photosynthesis ability and growth status. In view of the uneven distribution of illumination on broad-leaved banana canopy in the field, a correction method based on dark channel prior (DCP) is proposed to enhance the image quality, and improve the estimating accuracy of chlorophyll content. First, RGB (Red, Green, Blue) and NIR (near-infrared band) images of banana plant were separately obtained, and the two images were matched and aligned after preprocessing. Second, the channels in the region of interest (ROI) were split and restructured. The restructured image was corrected based on the DCP principle. Then, the optimal channels were selected according to the correlation analysis to form a new RGB (NewRGB) image, to simplify that OriRGB-NIR and NewRGB-NIR indicated the multispectral image before and after correction. The image features related to color features (CFs) and vegetation indices (VIs) were extracted and optimized as input variables for the chlorophyll estimation modeling. Finally, the random forest regression (RFR) was used to build the SPAD value estimation models, in which OriRGB-NIR-RFR model and the NewRGB-NIR-RFR model were named to show the results before and after image correction. Results show that the proposed correction method of uneven illumination reduced the image contrast and obtained more uniform values. At the same time, the accuracy of the model after correction was higher than that prior to correction. The modeling Rv2 of NewRGB-NIR-RFR with 11 variables reached 0.692. As a result, the visual distribution map of chlorophyll SPAD values was established, thereby providing the basis for the dynamic monitoring of crop nutrition.

MsTHI1 overexpression improves drought tolerance in transgenic alfalfa (Medicago sativa L.).

In recent years, drought stress caused by global warming has become a major constraint on agriculture. The thiamine thiazole synthase (THI1) is responsible for controlling thiamine production in plants displaying a response to various abiotic stresses. Nonetheless, most of the THI1 activities in plants remain largely unknown. In this study, we extracted MsTHI1 from alfalfa and demonstrated its beneficial impact on improving the resistance of plants to stress conditions. The highest levels of MsTHI1 expression were identified in alfalfa leaves, triggered by exposure to cold, drought, salt, or alkaline conditions. The upregulation of MsTHI1 in drought-stressed transgenic plants resulted in enhanced accumulation of vitamin B1 (VB1), chlorophyll a (Chl a), chlorophyll b (Chl b), soluble protein, higher soil and plant analyzer development (SPAD) value, and the activity of peroxidase (POD), maintained Fv/Fm, and decreased lipid peroxidation. Moreover, overexpression of MsTHI1 upregulated the transcription of THI4, TPK1, RbcX2, Cu/Zn-SOD, CPK13, and CPK32 and downregulated the transcription of TH1 and CPK17 in transgenic alfalfa under drought stress. These results suggested that MsTHI1 enhances drought tolerance by strengthening photosynthesis, regulating the antioxidant defense system, maintaining osmotic homeostasis, and mediating plant signal transduction.

Spraying Ethephon Effectively Increased Canopy Light Transmittance of Densely Planted Summer Maize, Thus Achieving Synergistic Improvement in Stalk Lodging Resistance and Grain Yield.

Increasing planting density is an effective way to improve maize yield, but high plant populations often cause a lodging problem. This experiment was conducted to investigate the effect of increasing planting density on stalk lodging resistance and grain yield, and to explore the effects on stalk and yield properties of spraying ethephon in densely planted summer maize. The summer maize hybrid, Xundan20 (XD20), was used as experimental material. It was grown by spraying water (CK) or ethephon (E) at BBCH (BASF, Bayer, Ciba-Geigy and Hoechst) 17 under three different planting densities of 60,000 plants ha−1 (L), 75,000 plants ha−1 (M) and 90,000 plants ha−1 (H) in order to explore the possibility of synergistic improvement in stalk lodging resistance and grain yield. The results from this experiment suggested that the gravity center height of densely planted summer maize was significantly increased, the stem diameter, area and number of vascular bundles were significantly decreased and the dry weight per unit internode was significantly decreased, thereby weakening the stalk rind penetration strength and bending performance, resulting in a significant increase in lodging percentage. The ear height was significantly decreased and the SPAD (soil and plant analysis development) and canopy light transmittance were increased after spraying ethephon; then, the internode dry weight per unit length was increased and the stalk rind penetration strength and bending performance were enhanced so as to significantly reduce the lodging percentage and increase the grain yield. The correlation analysis further showed that lodging percentage was significantly negatively correlated with stem diameter, area and number of vascular bundles and stalk bending performance, but there were no strong relationships with grain yield. This suggested that the synergistic improvement in stalk lodging resistance and grain yield was not contradictory. Under the experiment conditions, the effect of spraying ethephon was most significant when the planting density was 90,000 plants ha−1. At the time, the lodging percentage and grain yield were 12.2% and 11,137.5 kg ha−1, which were decreased by 44.6% and increased by 8.0% compared with the control treatment. Scientific chemical regulation could significantly improve the stalk lodging resistance and grain yield of densely planted summer maize.

Grain Dehydration Characteristics of Old and Modern Maize Hybrids and Their Response to Different Planting Densities

High grain water content at harvest stage is the main limiting factor for realizing mechanized maize grain harvest in China. Under the background of yield gain by density increase, it is necessary to clarify the effect of planting density increase on maize grain dehydration characteristics, which would provide theoretical support for realizing mechanized grain harvest under high planting density conditions. Therefore, this study selects five main hybrids, ZD2, DY13, YD13, XY335, and DH618, released in different eras that were widely promoted in Inner Mongolia from the 1970s to 2010s. The experiment was conducted in the Chilechuan Modern Agriculture Expo Park, Tumed Right Banner, Baotou city, Inner Mongolia, in 2018 and 2019. Under the three densities of 45,000 plants ha−1 (low density), 75,000 plants ha−1 (medium density) and 105,000 plants ha−1 (high density), the indexes of grain dehydration, leaf stay-green, bract and cob dehydration of the different maize hybrids were measured and analyzed. The results show that MCpm (moisture content at physiological maturity) of hybrids in the 1970s and 1990s was significantly reduced by 1.57 and 1.14 percentage points, respectively, and MCh (moisture content at harvest time) in the harvest period of hybrids in the 1970s was significantly reduced by 0.99 percentage points, from a low to medium density. The GDRbm (rate of grain dehydration before maturation) and the GDRam (rate of grain dehydration after maturation) showed an increasing trend from a low to medium density. From a medium to high density, the MCpm from the 1980s to 2000s could be significantly reduced by 1.78, 1.53 and 1.88 percentage points; the MCh from the 1980s could be significantly reduced by 1.77 percentage points; and the GDRbm from the 1970s was significantly increased by 0.101%/d, but the improvement of GDRam was not significant. With the planting density increase, the decreased ratios of relative GLAD (green leaf area duration) and leaf SPAD (soil and plant analyzer development) per plant of old maize hybrids were more than that of modern maize hybrids, which promoted the decrease in grain water content and the rate increase in grain dehydration for old maize hybrids. There was a direct positive correlation between the bract and grain dehydration rates, but the cob dehydration rate had no significant effect on the grain dehydration rate. With the increase in planting density, the relative GLAD and leaf SPAD values of plants decreased, and the stay-green of plants worsened, and a significant increase in the dehydration rate of bracts in old and modern eras was an important reason for the decrease in grain moisture content and increase in dehydration rate.

GmDNAJC7 from Soybean Is Involved in Plant Tolerance to Alkaline-Salt, Salt, and Drought Stresses

Soybean [Glycine max (L.) Merri.] is an important oilseed and food crop. In recent years, environmental degradation has accelerated soil alkalization, salinization, and water deficit, which have seriously threatened the soybean quality and yield. Chaperone DNAJ proteins play important roles in plant response to a number of abiotic and biotic stresses. Here, we investigated the function of a soybean DNAJ gene, GmDNAJC7, in plant tolerance to abiotic stresses. GmDNAJC7 gene expression was induced by alkaline-salt, salt, and drought treatments in soybean roots, suggesting its possible role in soybean response to these stresses. GmDNAJC7 overexpression improved the alkaline-salt tolerance of soybean composite plants, which showed a higher SPAD (Soil and Plant Analysis Development) value for chlorophyll content and leaf relative water content than the control plants after NaHCO3 treatment. Moreover, the GmDNAJC7 overexpressing Arabidopsis had a higher germination rate and average root length than the wild type and dnajc7 mutant, under NaHCO3, NaCl, and mannitol stresses, indicating that the ectopic expression of the GmDNAJC7 gene enhanced the alkaline-salt, salt, and drought tolerance in Arabidopsis. These findings suggest that GmDNAJC7 is involved in the alkaline-salt, salt, and drought tolerance in Arabidopsis and soybean. This study provides new insights into the role of DNAJ proteins in plant tolerance to abiotic stress.