Potassium (K) is one of the most important elements influencing cotton metabolism, quality, and yield. Due to the characteristics of strong fluidity and fast redistribution of the K in plants, it ...leads to rapid transformation of the K lack or abundance in plant leaves; therefore, rapid and accurate estimation of potassium content in leaves (LKC, %) is a necessary prerequisite to solve the regulation of plant potassium. In this study, we concentrated on the LKC of cotton in different growth stages, an estimation model based on the combined characteristics of wavelet decomposition spectra and image was proposed, and discussed the potential of different combined features in accurate estimation of the LKC. We collected hyperspectral imaging data of 60 main-stem leaves at the budding, flowering, and boll setting stages of cotton, respectively. The original spectrum (R) is decomposed by continuous wavelet transform (CWT). The competitive adaptive reweighted sampling (CARS) and random frog (RF) algorithms combined with partial least squares regression (PLSR) model were used to determine the optimal decomposition scale and characteristic wavelengths at three growth stages. Based on the best “CWT spectra” model, the grayscale image databases were constructed, and the image features were extracted by using color moment and gray level co-occurrence matrix (GLCM). The results showed that the best decomposition scales of the three growth stages were CWT-1, 3, and 9. The best growth stage for estimating LKC in cotton was the boll setting stage, with the feature combination of “CWT-9 spectra + texture,” and its determination coefficients (
R
2
val) and root mean squared error (RMSEval) values were 0.90 and 0.20. Compared with the single R model (
R
2
val = 0.66, RMSEval = 0.34), the
R
2
val increased by 0.24. Different from our hypothesis, the combined feature based on “CWT spectra + color + texture” cannot significantly improve the estimation accuracy of the model, it means that the performance of the estimation model established with more feature information is not correspondingly better. Moreover, the texture features contributed more to the improvement of model performance than color features did. These results provide a reference for rapid and non-destructive monitoring of the LKC in cotton.
Precise monitoring of cotton leaves' nitrogen content is important for increasing yield and reducing fertilizer application. Spectra and images are used to monitor crop nitrogen information. However, ...the information expressed using nitrogen monitoring based on a single data source is limited and cannot consider the expression of various phenotypic and physiological parameters simultaneously, which can affect the accuracy of inversion. Introducing a multi-source data-fusion mechanism can improve the accuracy and stability of cotton nitrogen content monitoring from the perspective of information complementarity.
Five nitrogen treatments were applied to the test crop, Xinluzao No. 53 cotton, grown indoors. Cotton leaf hyperspectral, chlorophyll fluorescence, and digital image data were collected and screened. A multilevel data-fusion model combining multiple machine learning and stacking integration learning was built from three dimensions: feature-level fusion, decision-level fusion, and hybrid fusion.
The determination coefficients (R
) of the feature-level fusion, decision-level fusion, and hybrid-fusion models were 0.752, 0.771, and 0.848, and the root-mean-square errors (RMSE) were 3.806, 3.558, and 2.898, respectively. Compared with the nitrogen estimation models of the three single data sources, R
increased by 5.0%, 6.8%, and 14.6%, and the RMSE decreased by 3.2%, 9.5%, and 26.3%, respectively.
The multilevel fusion model can improve accuracy to varying degrees, and the accuracy and stability were highest with the hybrid-fusion model; these results provide theoretical and technical support for optimizing an accurate method of monitoring cotton leaf nitrogen content.
Cotton yield estimation is of great practical significance to producers, allowing them to make rational management decisions. At present, crop yield estimation methods mainly comprise traditional ...agricultural yield estimation methods, which have many shortcomings. As an ideal “probe” for detecting crop photosynthesis, sun-induced chlorophyll fluorescence (SIF) can directly reflect the dynamics of actual crop photosynthesis and has the potential to predict crop yield, in order to realize cotton yield estimation based on canopy SIF. In this study, we set up field trials with different nitrogen fertilizer gradients. The changes of canopy SIF and the physiological parameters of cotton in different growth periods were analyzed. To investigate the effects of LAI and AGB on canopy SIF estimation of cotton yield, four algorithms, Ada Boost (Adaptive Boosting), Bagging (Bootstrap Aggregating), RF (Random Forest), and BPNN (Backpropagation Neural Network), were used to construct cotton yield estimation models based on the SIF and SIFy (the normalization of SIF by incident photosynthetically active radiation) for different time and growth periods. The results include the following: (1) The effects of the leaf area index (LAI) and aboveground biomass (AGB) on cotton canopy SIF and cotton yield were similar. The correlation coefficients of LAI and AGB with cotton yield and SIF were significantly positively correlated with each other starting from the budding period, reaching the maximum at the flowering and boll period, and decreasing at the boll period; (2) In different monitoring time periods, the R2 of the cotton yield estimation model established based on SIF and SIFy showed a gradual increase from 10:00 to 14:00 and a gradual decrease from 15:00 to 19:00, while the optimal observation time was from 14:00 to 15:00. The R2 increased with the progression of growth from the budding period to the flowering and boll period and decreased at the boll period, while the optimum growth period was the flowering and boll period; (3) Compared to SIF, SIFy has a superior estimation of yield. The best yield estimation model based on the RF algorithm (R2 = 0.9612, RMSE = 66.27 kg·ha−1, RPD = 4.264) was found in the canopy SIFy of the flowering and boll period at 14:00–15:00, followed by the model utilizing the Bagging algorithm (R2 = 0.8898) and Ada Boost algorithm (R2 = 0.8796). In summary, SIFy eliminates the effect of PAR (photosynthetically active radiation) on SIF and can further improve the estimation of SIF production. This study provides empirical support for SIF estimation of cotton yield and methodological and modeling support for the accurate estimation of cotton yield.
Nitrogen fertilizer levels significantly affect crop growth and development, necessitating precision management. Most studies focus on nitrogen nutrient estimation using vegetation indices and ...textural features, overlooking the diagnostic potential of color features. Hence, we investigated cotton nitrogen nutrition status using unmanned aerial vehicle (UAV) image features and the nitrogen nutrient index (NNI). Random frog algorithm - and random forest-screened image feature sets significantly correlated with the NNI, which were substituted into four machine learning algorithms for NNI estimation modeling. The composite scores (F) of optimal image feature sets were calculated using the coefficient of variation method for comprehensive cotton nitrogen nutrient diagnosis. Validation of the model for determining the critical nitrogen concentration in cotton yielded a coefficient of determination R2 = 0.89, root mean square error RMSE = 0.50 g (100 g)-1, and mean absolute error MAE = 0.44, demonstrating improved performance. Additionally, our novel NNI estimation model constructed based on the optimal image feature sets exhibited R2c = 0.97, RMSEc = 0.02, MAEc = 0.02, R2v = 0.85, RMSEv = 0.05, and MAEv = 0.04. Polynomial fitting of the composite index with NNI indicated that the model was reliable and yielded the following diagnostic criterion: 0.48 < F2 < 0.67 indicated nitrogen overapplication, whereas F2 < 0.48 or F2 > 0.67 indicated nitrogen deficiency. This study demonstrates the superior effectiveness of using UAV RGB image feature sets for NNI estimation and the quick, accurate diagnosis of cotton nitrogen levels, which will help guide nitrogen fertilizer application.
As an important index of a plant’s N nutrition, leaf nitrogen content (LNC) can be quickly monitored in real time with hyperspectral information, which is helpful to guide the precise application of ...N in cotton leaves. In this study, taking cotton dripping in Xinjiang, China, as the object of study, five N application treatments (0, 120, 240, 360, 480 kg·ha-1) were set up, and the hyperspectral data and the N content of main stem functional leaves at the cotton flower and boll stage were collected. The results showed that (1) comparing the correlations of the three types of spectral data from the original spectra, first derivative spectra, and second derivative spectra with the LNC of cotton, the first derivative spectra increased the correlation between the reflectance in the peak and valley ranges of the spectral curves and the LNC of cotton; (2) in the three hyperspectral regions of VIS, NIR, and SWIR, all R2 values of the estimation model for the LNC of cotton established based on the characteristic wavelengths of the original and the first derivative spectra were greater than 0.8, and the model accuracy was better than that of the second derivative spectra; and (3) the normalized root mean square error (n-RMSE) values of the validated model using MLR, PCR, and PLSR regression methods were all in the range of 10–20%, indicating that the established model could well estimate the nitrogen content of cotton leaves. The results of this study demonstrate the potential of the three hyperspectral domains of VIR, NIR, and SWIR to estimate the LNC of cotton and provide a new basis for hyperspectral data application in crop nutrient monitoring.
Crop nitrogen is an efficient index for estimating crop yield. Using hyperspectral information to monitor nitrogen in cotton information in real time can help guide cotton cultivation. In this study, ...we used drip-irrigation cotton in Xinjiang as the research object and employed various nitrogen treatments to explore the correlation between hyperspectral vegetation indexes and leaf nitrogen concentration (LNC) and the canopy nitrogen density (CND) of cotton in different growth periods and interannual. We employed 30 published hyperspectral vegetation indexes obtained through spectral monitoring in 2019 and 2020 to screen for hyperspectral vegetation indexes highly correlated with the nitrogen in cotton indexes. Based on the same group of hyperspectral vegetation indexes, interannual and multi-temporal nitrogen estimation models of cotton were established using three modeling methods: simple multiple linear regression (MLR), partial least-squares regression (PLSR), and support vector regression (SVR). The results showed the following: (1) The correlations between LNC and CND and vegetation index in individual growth periods of cotton were lower than those for the entire growth period. The correlations between hyperspectral vegetation indexes and cotton LNC, CND, leaf area index (LAI), and aboveground biomass (AGB), were significantly different between years and varieties. The relatively stable indexes between vegetation and LNC were TCARI, PRI, CCRI, and SRI-2, and the absolute values of correlation were 0.251~0.387, 0.239~0.422, 0.245~0.387, and 0.357~0.533. In addition, the correlation between CIred-edge and REIlinear and group indicators (CND, AGB, and LAI) was more stable. (2) In the models established by MLR, PLSR, and SVR, the R2 value from the SVR method was higher in the estimation model based on the entire growth period data and LNC and CND. (3) Using the same group of selected hyperspectral vegetation indexes to estimate nitrogen in cotton in different growth stages, the accuracy of the estimation model of canopy nitrogen density (CND) was higher than that of the estimation model for leaf nitrogen concentration. The canopy nitrogen density most stable model was established by MLR at the flowering and boll stages and the full-boll stage with R2 = 0.532~0.665. This study explored the application potential of hyperspectral vegetation indexes to the nitrogen of drip-irrigated cotton, and the results provide a theoretical basis for hyperspectral monitoring for crop nutrients and canopy structure.
The early and accurate monitoring of crop yield is important for field management, storage needs, and cash flow budgeting. Traditional cotton yield measurement methods are time-consuming, ...labor-intensive, and subjective. Chlorophyll fluorescence signals originate from within the plant and have the advantages of being fast and non-destructive, and the relevant parameters can reflect the intrinsic physiological characteristics of the plant. Therefore, in this study, the top four functional leaves of cotton plants at the beginning of the flocculation stage were used to investigate the pattern of the response of chlorophyll fluorescence parameters (e.g., F0, Fm, Fv/F0, and Fv/Fm) to nitrogen, and the cumulative fluorescence parameters were constructed by combining them with the leaf area index to clarify the correlation between chlorophyll fluorescence parameters and cotton yield. Support vector machine regression (SVM), an artificial neural network (BP), and an XGBoost regression tree were used to establish a cotton yield prediction model. Chlorophyll fluorescence parameters showed the same performance as photosynthetic parameters, which decreased as leaf position decreased. It showed a trend of increasing and then decreasing with increasing N application level, reaching the maximum value at 240 kg·hm-2 of N application. The correlation between fluorescence parameters and yield in the first, second, and third leaves was significantly higher than that in the fourth leaf, and the correlation between fluorescence accumulation and yield in each leaf was significantly higher than that of the fluorescence parameters, with the best performance of Fv/Fm accumulation found in the second leaf. The correlation between Fv/Fm accumulation and yield in the top three leaves combined was significantly higher than that in the top four leaves. The correlation coefficient between Fv/Fm accumulation and yield was the highest, indicating the feasibility of applying chlorophyll fluorescence to estimate yield. Based on the machine learning algorithm used to construct a cotton yield prediction model, the estimation models of Fv/F0 accumulation and yield of the top two leaves combined as well as top three leaves combined were superior. The estimation model coefficient of determination of the top two leaves combined in the BP algorithm was the highest. In general, the Fv/F0 accumulation of the top two leaves combined could more reliably predict cotton yield, which could provide technical support for cotton growth monitoring and precision management.
Timely and accurate plant growth monitoring is crucial for precision crop management. Traditional remote sensing methods use a single agronomic parameter to evaluate crop growth status (GST), ...limiting accuracy.
To develop a comprehensive growth monitoring index (CGMI) based on multiple parameters.
A two-year field experiment in the Mosuwan Reclamation Region of Xinjiang, China was conducted to collect parameter characterization data for cotton growth, including leaf area index, canopy chlorophyll content, above-ground biomass, and boll numbers, and their contributions and interrelationships in relation to yield were analyzed. Entropy and game theory weighting methods were used to establish the CGMIEWM and CGMIGT, and a sequential forward selection algorithm (SFS) was used to screen the most effective remote-sensing monitoring feature variables for the different reproductive stages. Partial least squares regression (PLSR), random forest (RF), and support vector regression (SVR) were used to develop an optimal model to comprehensively monitor cotton growth and draw a spatial distribution map.
CGMIEWM and CGMIGT could effectively reflect GST. The correlation between CGMIGT and yield based on a game theory combination weighting method was significantly higher than that between yield and each agronomic parameter. The correlation between CGMIGT and yield (r = 0.75) was slightly higher at the initial boll stage than that of CGMIEWM (r = 0.73), whereas at the initial boll-opening stage, the correlation between CGMIGT and yield (r = 0.74) was significantly higher than that of CGMIEWM (r = 0.63). The weight coefficients used to construct the CGMIGT exhibited stable performance in different years. Feature variables were selected to monitor the comprehensive growth of cotton at different stages based on the SFS algorithm. PLSR, RF, and SVR were used to estimate CGMIGT. The RF algorithm had the best estimation performance in both the initial boll and initial boll-opening stages (R² = 0.63, root mean square error (RMSE) = 0.086, RE = 19.8 % vs. R² = 0.56, RMSE = 0.107, RE = 24.1 %). A comprehensive cotton growth distribution map in the Mosuwan Reclamation Region was drawn using the optimal model, and growth was comprehensively evaluated. Areas with good cotton growth were concentrated in the north, and there was a decreasing trend from north to south.
We provide a new comprehensive evaluation tool for cotton growth status large-scale, real-time monitoring.
Our results promote differentiated management, improve crop yield prediction accuracy, and aid in the formulation of cotton price strategies.
Potassium (K) is a highly mobile nutrient element that continuously adjusts its demand strategy among and within cotton leaves through redistribution. This indirectly leads to variations in the leaf ...potassium content (LKC, %) at different leaf positions. However, owing to the interaction between light and leaf age, leaf sensitivity at different positions to this change varies, including the reflection and absorption of the spectrum. How to selecting the optimal monitoring leaf position is an important factor in quickly and accurately evaluation of cotton LKC using spectral remote sensing technology. Therefore, this study proposes a comprehensive multileaf position estimation model based on the vertical distribution characteristics of LKC from top to bottom. This is aimed at achieving an accurate estimation of cotton LKC and optimizing the strategy for selecting the monitored leaf position. Between 2020 and 2021, we collected hyperspectral imaging data of the main stem leaves at different positions from top to bottom (Li, i=1, 2, 3, …, n), during the cotton budding, flowering, and boll setting stages. Vertical distribution characteristics, sensitivity differences, and spectral correlations of LKC at different leaf positions were investigated. Additionally, the optimal range of the dominant leaf position for monitoring was determined. Partial least squares regression (PLSR), random forest regression (RFR), support vector machine regression (SVR), and the entropy weight method (EWM) were used to establish LKC estimation models for single leaf and multileaf positions. The results showed a vertical heterogeneous distribution of cotton LKC, with LKC initially increasing and then gradually decreasing from top to bottom, and the average LKC of cotton reaches its maximum value at flowering stage. The upper leaf position demonstrated greater sensitivity to K and exhibited a stronger correlation with the spectrum. The selected dominant leaf positions for the three growth stages were L1–L5, L1–L4, and L1–L2, respectively. Based on the dominant leaf position monitoring range, the optimal single leaf position models for estimating LKC during the three growth stages were PLSR-L4, PLSR-L1, and SVR-L2, with The coefficient of determination of the validation set (R2val) of 0.786, 0.580, and 0.768, and the root-mean-square error of the validation set (RMSEval) of 0.168, 0.197, and 0.191, respectively. The multileaf position LKC estimation model was constructed by EWM with R2val of 0.887, 0.728, and 0.703, and RMSEval of 0.134, 0.172, and 0.209, respectively. In contrast, the newly developed multileaf position comprehensive estimation model yielded superior results, improving the stability of the model on the basis of high accuracy, especially during the budding and flowering stages. These findings hold significant importance for investigating cotton LKC spectral models and selecting suitable leaf positions for field monitoring.
The outbreak of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has caused an unprecedented global pandemic, and new cases are still on the rise due to the absence of effective ...medicines. However, developing new drugs within a short time is extremely difficult. Repurposing the existing drugs provides a fast and effective strategy to identify promising inhibitors. Here we focus on the screening of drugs database for discovering potential covalent inhibitors that target 3-chymotrypsin-like protease (3CLpro), an essential enzyme mediating viral replication and transcription. Firstly, we constructed a receptor-ligand pharmacophore model and verified it through decoy set. The importance of pharmacophore features was evaluated by combining molecular dynamics simulation with interaction analyses. Then, covalent docking was used to perform further screening. According to docking score and Prime/Molecular Mechanics Generalized Born Surface Area (MM-GBSA) score, total ten compounds obtained good scores and successfully established covalent bonds with the catalytic Cys145 residue. They also formed favorable interactions with key residues in active sites and closely integrated with 3CLpro with binding modes similar to known 3CLpro inhibitor. Finally, the top four hits DB08732, DB04653, DB01871 and DB07299 were further subjected to 100 ns molecular dynamics (MD) simulation and MM-GBSA binding free energy calculations. The results suggest that the four candidates show good binding affinities for 3CLpro, which warrants further evaluation for their in-vitro/in-vivo activities. Overall, our research methods provide a valuable reference for discovering promising inhibitors against SARS-CoV-2 and help to fight against the epidemic.
Communicated by Ramaswamy H. Sarma
