Chlorophyll Content Of Maize Research Articles

Hyperspectral indices data fusion-based machine learning enhanced by MRMR algorithm for estimating maize chlorophyll content.

Accurate estimation of chlorophyll is essential for monitoring maize health and growth, for which hyperspectral imaging provides rich data. In this context, this paper presents an innovative method to estimate maize chlorophyll by combining hyperspectral indices and advanced machine learning models. The methodology of this study focuses on the development of machine learning models using proprietary hyperspectral indices to estimate corn chlorophyll content. Six advanced machine learning models were used, including robust linear stepwise regression, support vector machines (SVM), fine Gaussian SVM, Matern 5/2 Gaussian stepwise regression, and three-layer neural network. The MRMR algorithm was integrated into the process to improve feature selection by identifying the most informative spectral bands, thereby reducing data redundancy and improving model performance. The results showed significant differences in the performance of the six machine learning models applied to chlorophyll estimation. Among the models, the Matern 5/2 Gaussian process regression model showed the highest prediction accuracy. The model achieved R2 = 0.71 for the training set, RMSE = 338.46 µg/g and MAE = 264.30 µg/g. In the case of the validation set, the Matern 5/2 Gaussian process regression model further improved its performance, reaching R2 =0.79, RMSE=296.37 µg/g, MAE=237.12 µg/g. These metrics show that Matern's 5/2 Gaussian process regression model combined with the MRMR algorithm to select optimal traits is highly effective in predicting corn chlorophyll content. This research has important implications for precision agriculture, particularly for real-time monitoring and management of crop health. Accurate estimation of chlorophyll allows farmers to take timely and targeted action.

Genome-wide association study reveals genetic basis and candidate genes for chlorophyll content of leaves in maize (Zea mays L.).

The chlorophyll content (CC) directly affects photosynthesis, growth, and yield. However, the genetic basis of CC is still unclear in maize (Zea mays L.). Here, we conducted a genome-wide association study using mixed linear model for CC of the fifth leaves at seedling stage (CCFSS) and the ear leaves at filling stage (CCEFS) for 334 maize inbred lines. The heritability estimates for CCFSS and CCEFS, obtained via variance components analysis using the lme4 package in R, were 70.84% and 78.99%, respectively, indicating that the CC of leaves is primarily controlled by genetic factors. A total of 15 CC-related SNPs and 177 candidate genes were identified with a p-value < 4.49 × 10-5, which explained 4.98-7.59% of the phenotypic variation. Lines with more favorable gene variants showed higher CC. Meanwhile, Gene Ontology (GO) analysis implied that these candidate genes were probably related to chlorophyll biosynthesis. In addition, gene-based association analyses revealed that six variants in GRMZM2G037152, GRMZM5G816561, GRMZM2G324462, and GRMZM2G064657 genes were significantly (p-value < 0.01) correlated with CC, of which GRMZM2G064657 (encodes a phosphate transporter protein) and GRMZM5G816561 (encodes a cytochrome P450 protein) were specifically highly expressed in leaves tissues. Interestingly, these candidate genes were previously reported to involve in the regulation of the contents of chlorophyll in plants or Chlamydomonas. These results may contribute to the understanding of genetic basis and molecular mechanisms of maize CC and the selection of maize varieties with improved CC.

Detection of chlorophyll content based on optical properties of maize leaves

The chlorophyll content reflects plants’ photosynthetic capacity, growth stage, and nitrogen status. Maize is one of the three widely planted gain crops in the world. In order to offer useful information for the development of chlorophyll content detectors of maize leaves, a single integrating sphere system was used to measure the transmittance and reflectance spectra of maize leaves over the wavelength range of 500–950 nm. The linear relationships of transmittance and reflectance with chlorophyll content were investigated. The feature wavelengths (FWs) sensitive to chlorophyll content were extracted from the full transmittance and reflectance spectra using the successive projections algorithm (SPA). The partial least squares regression (PLSR) models for predicting the chlorophyll content were established using the full spectra and extracted FWs. The results showed that there were obvious linear relationships between transmittance and reflectance with chlorophyll content of maize leaves and the best linear relationships were found at 709 nm and 714 nm, respectively, with the linear correlation coefficients of 0.801 and 0.696, and the root-mean-squares error (RMSEP) of 0.321 mg·g−1 and 0.405 mg·g−1, respectively. Eight and 6 FWs were extracted from the transmittance and reflectance spectra, respectively. The PLSR model established using the selected FWs from transmittance spectra had better prediction performance with RMSEP of 0.208 mg·g−1 than using full transmittance spectra. The built PLSR models using the full reflectance spectra and extracted FWs had poor robustness. This research offers some theoretical basis for developing a maize leaf chlorophyll content detector based on transmittance or reflectance.

Effects of fungal carbon dots application on growth characteristics and cadmium uptake in maize

The advancement of nanotechnology has led to the increased use of nanomaterials for the purpose of restoring contaminated soils. However, so far no research has been reported on the interactions of carbon dots with heavy metals (loid)s in phytoremediation. The purpose of this study was to investigate the effect of a new carbon dots derived from fungal exopolysaccharide (EPSs) on the growth and cadmium uptake in maize plants. This research was carried out using a completely randomized design with three replications in a greenhouse condition. Treatments included control, carbon dots (150 mg kg−1), cadmium (50 mg kg−1) and cadmium + carbon dots (50 mg kg−1+150 mg kg−1). The carbon dots synthesized by hydrothermal method from EPSs. The results showed that shoot dry weight and chlorophyll content of maize increased 9.7% and 23.2% in the presence of carbon dots, respectively. Carbon dots improved the chlorophyll content of maize by 24.3% in the cadmium treatment. Cadmium concentration increased (106%) in maize shoot but it decreased in root maize (68%). Carbon dots caused an increase of 5.7 and 6.7 times in the transfer factor and phytoremediation rate of cadmium, respectively. The presence of carbon dots triggered an increase of 77.9% and 39.9% of dissolved organic carbon in non-contaminated and cadmium-contaminated soils, respectively. Soil microbial biomass carbon increased 54.9% and 24.1% carbon dots and cadmium + carbon dots treatments, respectively. The study demonstrates the potential of fungal carbon dots for phytoremediation of heavy metal (loid)s contaminated soils. It also highlights the potential of nanotechnology in environmental remediation efforts.

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.

Genome-Wide Association Study Identified Novel SNPs Associated with Chlorophyll Content in Maize.

Chlorophyll is an essential component that captures light energy to drive photosynthesis. Chlorophyll content can affect photosynthetic activity and thus yield. Therefore, mining candidate genes of chlorophyll content will help increase maize production. Here, we performed a genome-wide association study (GWAS) on chlorophyll content and its dynamic changes in 378 maize inbred lines with extensive natural variation. Our phenotypic assessment showed that chlorophyll content and its dynamic changes were natural variations with a moderate genetic level of 0.66/0.67. A total of 19 single-nucleotide polymorphisms (SNPs) were found associated with 76 candidate genes, of which one SNP, 2376873-7-G, co-localized in chlorophyll content and area under the chlorophyll content curve (AUCCC). Zm00001d026568 and Zm00001d026569 were highly associated with SNP 2376873-7-G and encoded pentatricopeptide repeat-containing protein and chloroplastic palmitoyl-acyl carrier protein thioesterase, respectively. As expected, higher expression levels of these two genes are associated with higher chlorophyll contents. These results provide a certain experimental basis for discovering the candidate genes of chlorophyll content and finally provide new insights for cultivating high-yield and excellent maize suitable for planting environment.

Aktivitas Nitrat Reduktase (ANR) Tanaman Jagung pada Pola Tumpangsari yang Diberi Serasah Jagung-Kedelai serta Biochar di Lahan Suboptimal Sidondo Sulawesi Tengah

The aim of the study was to examine the relationship between nitrate reductase activity and N uptake, tissue N content, N uptake, and chlorophyll content of corn plants in intercropping patterns which are given corn-soybean litter and biochar. The use of plant litter and biochar derived from crop residues as a source of organic matter in different intercropping patterns affected nitrate reductase activity, tissue N content, N uptake, and chlorophyll content of maize. This experiment was carried out on sub-optimal Sidondo land, Central Sulawesi with the main limiting factors being the availability of water and alkaline soil pH (7.21) which were arranged in a divided plot design with five litter treatments as subplots and five cropping patterns as the main plot with three replicates. Parameters observed were N levels, plant tissue N uptake, chlorophyll, and nitrate reductase activity. Collected data were analyzed using Anova and Duncan's multiple random tests. The results showed that corn litter, soybean mixed corn + soybean litter and their biochar in different intercropping patterns affected nitrate reductase activity, nitrogen uptake, chlorophyll levels very significantly (P˂0.01), increased nitrate reductase activity by 9710.86 mol NO2- -1g-1hour-1 at the age of 55 DAP, while N uptake (2237 mg.kg-1), tissue N content (4.33%), and total chlorophyll content (10.61 mg.l-1) were the highest in the litter treatment, corn and soybeans as well as biochar in corn and soybean intercropping (1:2), and (2:4). Thus, the provision of corn and soybean litter and biochar in intercropping systems can increase nitrate reductase activity, N uptake, tissue N levels, and corn chlorophyll levels in the corn-soybean intercropping system.&#x0D; &#x0D; Keywords: chlorophyll, N content of corn plant tissue, nitrate reductase activity, N uptake

Maize Growth, Fodder Yield and Nutrient Uptake in relation to Phosphorus and Sulphur Nutrition

Phosphorus (P) and Sulphur (S) are the most important plant nutrients which play significant roles in enhancing the early growth and development and increasing the fodder yield of maize. This important field study was planned to evaluate the P-S interaction effects on maize growth, fodder yield and nutrient (P and S) uptake. The soil used for study was clay loam, medium-alkaline (pH: 7.9), non-saline (EC: 0.85 dS m-1), strongly calcareous (CaCO3: 24 %), adequate in organic matter (1.6 %), low in ABDTPA-P (3.5 mg kg-1), while medium in SO4-S (21.5 mg kg-1). Eight P and S treatments were involved in this study, i.e. single application of each 90 kg P2O5 (T1) and 20 kg S ha-1 (T2); integration of 90 kg P2O5 with S @ 20, 40 and 60 kg ha-1 (T3, T4 and T5, respectively), and integration of 120 kg P2O5 with S @ 20, 40 and 60 kg ha-1 (T6, T7 and T8, respectively). The experiment followed an RCBD arrangement that was replicated thrice. Recommended seed rate of maize (95 kg ha-1) was used for sowing. The results revealed that shoot biomass production was maximum when maize plants received higher doses of P and S in an integrated manner (120-60 kg P2O5-S ha-1). At 30 DAS, shoot fresh biomass was found maximum, and statistically alike, when 90 or 120 kg P2O5 was integrated with S @ 60 kg ha-1. Shoot fresh biomass was maximum, at 40 DAS when 120-60 kg P2O5-S ha-1 was applied to maize. Shoot dry biomass after 30 days of sowing was highest when 90 kg P2O5 was applied with S @ 60 kg ha-1 or when, 120 kg P2O5 was used with S @ 40 and 60 kg ha-1. Shoot length was maximum, both at 30 and 40 DAS, in case of using 120-60 kg P2O5-S ha-1. Chlorophyll content of maize was maximum, both at 30 and 40 DAS, when 90 kg P2O5 was applied with S @ 60 kg ha-1. Maximum fodder yield of maize was noted when 120 kg P2O5 was applied with 60 kg S ha-1. Highest P-uptake, both at 30 and 40 DAS was noted when 120 kg P2O5 was applied with S @ 60 kg ha-1. At the first stage of growth, S-uptake was highest when 60 kg S was integrated with 90 kg P2O5 ha-1. The study advocated integrated application of 120-60 kg P2O5-S ha-1 for obtaining economically maximum fodder yield of maize.

Impact of Magnetite Nanoparticles Coated with Aspartic Acid on the Growth, Antioxidant Enzymes Activity and Chlorophyll Content of Maize.

In recent decades, magnetite nanoparticles received greater attention in nanobiotechnology due to wide applications. This study presents the influence of the oxidative stress caused by magnetite nanoparticles coated with aspartic acid (A-MNP) of 9.17 nm mean diameter size, on maize (Zea mays) seedlings, in terms of growth, enzymatic activity and chlorophyll content as evaluated in exposed plant tissues. Diluted suspensions of colloidal magnetite nanoparticles stabilized in water were added to the culture medium of maize seeds, such as to equate nanoparticle concentrations varying from 0.55 mg/L to 11 mg/L. The obtained results showed that the growth of maize was stimulated by increasing the level of A-MNPs. Plant samples treated with different concentrations of A-MNP proved increased activities of catalase and peroxidase, and chlorophyll content, as well. The exposure of plants to magnetite nanoparticles may induce oxidative stress, which activates the plant defense/antioxidant mechanisms.

Diagnosis of maize chlorophyll content based on hybrid preprocessing and wavelengths optimization

Leaf chlorophyll content (LCC) is one of important indicators for photosynthesis evaluation and crop nutrition diagnosis. For establishing an accuracy LCC diagnosis model, this study focused on reduce influences of multi-source interferences which resulted from external noises and internal multi-compositions. A systematic strategy with pipelines methods of hybrid preprocessing and wavelengths optimization was proposed to overcome such complicated interferences. Maize datasets of canopy reflectance and LCC measurements were collected under six fertilization treatments and three replications in three growth stages (V6, V9, and V12) in 2019 summer maize. Hybrid preprocessing was proposed by cascading preprocessing methods of discrete wavelet transformation (DWT) smoothing, multiplicative scatter correction (MSC), second derivative (D2). Optimization of sensitive wavelength was conducted to reduce the influences of multi-components overlapping. Methods of competitive adaptive reweighted sampling (CARS), iterative retaining informative variable (IRIV), and variable iterative space shrinkage space (VISSA) combined with partial least square (PLS) were used to explore and validate spectral responses traits of multi-components overlapping. The results of VISSA-PLS showed superior potentials for diagnose maize LCC with accuracy of RP2 of 0.76 and RMSEP of 2.45. The modeling results indicated that the systematic solution of DWT-MSC-D2-VISSA-PLS could improve the performance of LCC diagnosis. It provided application potentials for accurate diagnosis of maize growth status and lay foundations for topdressing recommendations.

Alleviation of Cd stress in maize by compost mixed biochar

Cadmium (Cd) is a heavy metal that neutrally occurs in soil. It is carcinogenic in humans and caused a significant decline in the plant’s growth when up has taken beyond the threshold limit. The fertilizers, manure, sewage sludge, and aerial deposition are the main source of cadmium contamination in soil. Furthermore, poor soil organic matter is also one of the allied factors which facilitate the development of Cd toxicity in soil. The decomposition resistance nature of biochar makes it an effective amendment for cadmium remediation. Through crop production, Cd enters the food system. Individual studies on biochar and compost are found in the literature but the combined effect of biochar and compost are rarely documented especially in maize crops. The current pot study was conducted in Pesticide Quality Control Laboratory, Multan, Pakistan. However, the current study was novel and conducted by using compost mixed biochar (CB) against Cd toxicity in maize. Four application rates of CB i.e., 0, 0.50, 0.75 and 1.00% (1.00CB) were applied under 3 levels of Cd i.e., 0 (0Cd), 2.5 (2.5Cd) and 5 mg Cd kg−1 soil (5.0Cd). Overall, results indicated that 1.00%CB remained significantly best at higher 5.0Cd for improvement in soil organic matter, plant height, root length, number of leaves, leaves fresh and dry weight, plant fresh and dry weight, chlorophyll a, b, total and carotenoids. A significant decrease in soil pHs, leaves anthocyanin and lycopene also validated the efficacious functioning of 1.00%CB over control in 2.5 and 5.0Cd. In conclusion, the use of 1.00%CB is a better approach to decrease Cd harmful effects to improve gas exchange attributes, growth and chlorophyll contents in maize. Long-term research is required on co-composted biochar toward mitigation of cadmium toxicity under different geographical locations.

Predicting the Chlorophyll Content of Maize over Phenotyping as a Proxy for Crop Health in Smallholder Farming Systems

Smallholder farmers depend on healthy and productive crop yields to sustain their socio-economic status and ensure livelihood security. Advances in South African precision agriculture in the form of unmanned aerial vehicles (UAVs) provide spatially explicit near-real-time information that can be used to assess crop dynamics and inform smallholder farmers. The use of UAVs with remote-sensing techniques allows for the acquisition of high spatial resolution data at various spatio-temporal planes, which is particularly useful at the scale of fields and farms. Specifically, crop chlorophyll content is assessed as it is one of the best known and reliable indicators of crop health, due to its biophysical pigment and biochemical processes that indicate plant productivity. In this regard, the study evaluated the utility of multispectral UAV imagery using the random forest machine learning algorithm to estimate the chlorophyll content of maize through the various growth stages. The results showed that the near-infrared and red-edge wavelength bands and vegetation indices derived from these wavelengths were essential for estimating chlorophyll content during the phenotyping of maize. Furthermore, the random forest model optimally estimated the chlorophyll content of maize over the various phenological stages. Particularly, maize chlorophyll was best predicted during the early reproductive, late vegetative, and early vegetative growth stages to RMSE accuracies of 40.4 µmol/m−2, 39 µmol/m−2, and 61.6 µmol/m−2, respectively. The least accurate chlorophyll content results were predicted during the mid-reproductive and late reproductive growth stages to RMSE accuracies of 66.6 µmol/m−2 and 69.6 µmol/m−2, respectively, as a consequence of a hailstorm. A resultant chlorophyll variation map of the maize growth stages captured the spatial heterogeneity of chlorophyll within the maize field. Therefore, the study’s findings demonstrate that the use of remotely sensed UAV imagery with a robust machine algorithm is a critical tool to support the decision-making and management in smallholder farms.

Changes in Leaf Phenolics and Chlorophyll Content of Maize Inoculated with Glomus intraradices Upon Spodoptera frugiperda Infestation

Maize, the third important cereal crop, is severely affected by Spodoptera frugiperda (fall armyworm). Arbuscular mycorrhizal fungi (AMF) colonized crops are shown to tolerate insect infestation because of systemic priming of defence against insect attack. The present study aimed to examine the effect of various doses of AMF (Glomus intraradices) inoculation on triggering tolerance against S. frugiperda infestation in maize. The total phenolics and the chlorophyll content of insect uninfested leaves obtained from AMF applied and insect attacked treatments were higher over AMF uninoculated plants upon insect attack. However, a greater concentration of phenolics and chlorophyll was observed in plants treated with maximum spore inoculum of 5 per seed. Similarly, plant biomass was also greater in the same treatment even after S. frugiperda infestation. These observations indicate the importance of AMF in sustaining maize growth and health during S. frugiperda attack.

Study on Deep Learning Model for Online Estimation of Chlorophyll Content Based on Near Ground Multispectral Feature Bands

Chlorophyll content of plant leaves is an essential indicator of crop growth status. This study is focused on the nondestructive estimation of the chlorophyll content of maize using near ground multispectral data. We propose a one-dimensional convolutional neural network-gated recurrent unit (1-D-CNN-GRU). That is, it combines a 1D-CNN with strong feature expression capacity and strong memory capacity with the gated recurrent unit (GRU) neural network to estimate of the chlorophyll content of maize directly from multispectral images. Furthermore, the iteratively retaining informative variables-successive projections algorithm (IRIV-SPA) is first used to select the feature wavebands from the 11 available wavebands of two datasets in the experiment. The experimental results show that the selected feature wavebands have more accuracy than the raw wavebands when using the same model; based on these feature wavebands, the 1D-CNN-GRU model has smaller errors than the other conventional models such as support vector regression (SVR) and random forest (RF), with an MRE of 0.069, RMSE of 3.473 on Datasets I, and an MRE of 0.108, RMSE of 7.568 on Datasets II. Meanwhile, the real-time performance is also validated in the experiment. These investigations can provide a valuable guidance for online monitoring chlorophyll content of crops such as maize etc., based on near ground multispectral band data processing for developing smart agricultural system.

Exploring the utility of Sentinel-2 for estimating maize chlorophyll content and leaf area index across different growth stages

ABSTRACT This study investigated the utility of Sentinel-2 spectral data for estimating leaf area index (LAI), leaf and canopy chlorophyll content of maize at different growth stages. Vegetation indices based on the visible-near infrared and red-edge regions of the spectrum were computed from Sentinel-2 imagery acquired within one or two days of field data collection. Results showed that green chlorophyll index (CIgreen) and red-edge chlorophyll index (CIred-edge), using the red-edge variant centred at 705 nm, consistently showed higher relationship to maize LAI with r 2 of 0.65 and 0.63 during the early stages of growth, respectively, and an r 2 of 0.79 and 0.81 during tassel stage, respectively. Regarding canopy chlorophyll content the results indicated the spectral advantage of the Sentinel-2 sensor with the presence of two red-edge bands for continuous monitoring of maize chlorophyll content. Overall, the results indicated that maize biophysical variables can be monitored at satellite level using Sentinel-2 data.

Application effect of high efficiency and stability urea added with biochemical inhibitors and humic acid in loess

We carried out pot experiment to investigate nitrogen transformation characteristics, yield increasing effect, and apparent utilization rate of nitrogen fertilizer in loess soils by combining chemi-cal inhibitor and biostimulant humic acid to investigate the application effect and provide a theoretical basis for new type highly efficient and stable urea in loess soil. In this study, blank (CK) and urea (N) were set as controls, and humic acid alone (F), N-butyl thiophosphate-triamine (NBPT), 3,4-dimethyl-pyrazolate phosphate (DMPP), 2-chloro-6-trimethyl-pyridine (CP) and humic acid were respectively combined with three biochemical inhibitors to urea. The results showed that compared with N treatment, F, NBPT+F, DMPP+F, CP+F treatments significantly increased maize yield, chlorophyll content, leaf area index and nitrogen uptake, and had obvious effects on soil available nitrogen contents. The addition of humic acid increased chlorophyll content of maize leaves in all cases compared to the application of biochemical inhibitors alone. Compared with CP treatment, CP+F treatment could significantly increase nitrogen uptake, chlorophyll content, and nitrogen adsorption efficiency of maize. Addition of humic acid with NBPT increased nitrification inhibition rate by 10.7% compared with NBPT alone, but decreased yield, leaf area index, nitrogen uptake, nitrogen use efficiency. Compared with DMPP treatment, DMPP+F treatment significantly reduced maize yield, leaf area index, nitrogen uptake, nitrogen use efficiency and nitrification inhibition rate. Considering maize yield, plant N uptake, N fertilizer uptake rate and soil ammonium N and nitrate N contents, the addition of humic acid and CP is recommended for urea application in loess areas to enhance urea performance, yield, and fertilizer utilization.

Evaluating in-situ maize chlorophyll content using an external optical sensing system coupled with conventional statistics and deep neural networks

Plant conditions can be monitored by direct-leaf measurements. For this purpose, several researchers have developed applications based on smartphone cameras, but the variable quality of smartphone cameras, even those of the same brand, produces non-standardized results. The present study describes a new and reliable technique that measures the chlorophyll content of maize leaves for plant monitoring on any smartphone. To check the accuracy of the data generated by the developed handheld optical sensing system, the obtained data were compared against those of an established chlorophyll-monitoring meter (a SPAD-502 monitor). The required SPAD, RGB, and global positioning system data were collected from maize fields (∼2 ha) at the research farm of the Indonesian Coffee and Cocoa Research Institute. The collected data were analyzed using conventional statistics/regression analysis and a deep neural network (DNN). The inverse-distance weighted outputs were interpolated to generate zoning maps. In a conventional statistics/regression analysis, the chlorophyll levels were significantly correlated with the SPAD values (R2 = 0.82–0.84, root mean squared error [RMSE] = 2.95–3.05). However, after applying the DNN with 12 extracted input features, four hidden layers, and 637 parameters, the chlorophyll content estimation was significantly improved (R2 and RMSE = 0.89 and 2.6, respectively), and the zoning map generated by the developed system was nearly aligned with the SPAD zoning map. The findings confirm that this technique is applicable to all types of smartphones regardless of their camera properties and provides the light-aided intensity necessary for direct-leaves measurement. The locations of the collected leaves were simultaneously monitored in real-time to generate the mapping results.

Chlorophyll content estimation based on cascade spectral optimizations of interval and wavelength characteristics

Spectroscopy is an efficient way to estimate the chlorophyll content of crop canopy in the visible and near-infrared region to indicate the photosynthesis capacity and growth status. Although hyperspectral data of crop canopy provide a lot of information, a critical issue is that the subsequent redundant and interference wavelengths could reduce the accuracy and robustness of the estimated results in the field. Therefore, a cascade method of interval-wavelength screening was proposed to reduce the data dimension and improve the crop estimation accuracy and robustness for the chlorophyll content diagnosis of maize crops. In the experiments, spectral reflectance data of maize canopy within 325–1075 nm were obtained, and chlorophyll contents were measured under the conditions of six nitrogen application rates and three growth periods following the early flaring, flaring, and tasseling stages. First, the backward interval partial least squares (BiPLS) method was used to optimize the spectrum interval. Then, competitive adaptive reweighted sampling (CARS) and genetic algorithm (GA) were used for secondary wavelength screening, and the partial least squares regression (Bi-CARS-PLSR and Bi-GA-PLSR) model was established with the chlorophyll content. Compared with the full-spectrum modeling (Full-PLSR) results and the GA and CARS screening wavelength modeling (Full-CARS-PLSR and Full-GA-PLSR) results in the full-spectrum range, the number of wavelengths in the Full-PLSR model was 751. The accuracy of the Full-PLSR model with the coefficient of determination was 0.88 for the calibration set (Rc2) and 0.66 for the verification set (Rv2). The root mean square error (RMSE) was 2.05 mg/L. The wavelength numbers of Full-CARS-PLSR and Full-GA-PLSR were 125 and 99, and their Rc2 and Rv2 were 0.88 and 0.67, respectively. The RMSE values of Full-CARS-PLSR and Full-GA-PLSR were 2.04 and 1.89 mg/L, respectively. By using the proposed cascade method, the wavelength numbers of the Bi-CARS-PLSR model and Bi-GA-PLSR model after interval optimization were 60 and 49, respectively. Their accuracy values for the calibration set (Rc2) were both 0.87, while those for the verification set (Rv2) were 0.7 and 0.78, respectively. The RMSE values of both models were 1.97 and 1.86 mg/L, respectively. The results showed that the proposed cascaded interval-wavelength screening method could eliminate redundant and collinearity variables and improve model performances. At the same time, the Bi-GA-PLSR modeling result was used for field prediction to establish a chlorophyll distribution map, which had a high consistency with the real chlorophyll distribution, and showed the potential to detect chlorophyll content of maize in the field.

Using Spectral Reflectance to Estimate the Leaf Chlorophyll Content of Maize Inoculated With Arbuscular Mycorrhizal Fungi Under Water Stress.

Leaf chlorophyll content is an important indicator of the growth and photosynthesis of maize under water stress. The promotion of maize physiological growth by (AMF) has been studied. However, studies of the effects of AMF on the leaf chlorophyll content of maize under water stress as observed through spectral information are rare. In this study, a pot experiment was carried out to spectrally estimate the leaf chlorophyll content of maize subjected to different durations (20, 35, and 55 days); degrees of water stress (75%, 55% and 35% water supply) and two inoculation treatments (inoculation with Funneliformis mosseae and no inoculation). Three machine learning algorithms, including the back propagation (BP) method, least square support vector machine (LSSVM) and random forest (RF) method, were used to estimate the leaf chlorophyll content of maize. The results showed that AMF increased the leaf chlorophyll content, net photosynthetic rate (A), stomatal conductance (gs), transpiration rate (E), and water use efficiency (WUE) of maize but decreased the intercellular carbon dioxide concentration (Ci) of maize and atmospheric vapor pressure deficit (VPD) regardless of the water stress duration and degree. The first-order differential spectral data can better reflect the correlation between leaf chlorophyll content and spectrum of inoculated maize when compared with original spectral data. The BP model performed bestin modeling the maize leaf chlorophyll content, yielding the largest R2-values and smallest root mean square error (RMSE) values, regardless of stress duration. These results provide a reliable basis for the effective monitoring of the leaf chlorophyll content of maize under water stress.

Effect of Soil Applied Silicon on Gas Exchange Parameters, Growth and Ion Content of Maize under Alkaline Stress

A pot experiment was conducted to understand the effect of soil applied silicon on maize crop growth under alkaline stress. The treatment consists of silicon levels (kg ha-1) – 0, 100 and 150 and alkaline stress levels (mM) – 0, 25, 50, 75. The pots were arranged in completely randomized block design in factorial arrangement with three replications. The result revealed that the shoot and root length and chlorophyll content of maize were reduced with increasing alkaline stress. The percent reduction due to alkaline stress on shoot and root length was to tune of 3.6 to 15.8 and 8.9 to 35.8, respectively and chlorophyll content 11.2 to 35.7. The sodium ion content increased, but potassium content and K Na-1 ratio decreased with alkaline stress. The gas exchange parameters viz., photosynthetic rate, stomatal conductance and transpiration rate were significantly decreased with alkalinity levels. The adverse effect of alkalinity on maize was turnround with intervention of soil applied silicon. The percent increase due to silicon intervention ranged from 4.3 to 12.6 in photosynthetic rate, 4.5 to 14.5 in stomatal conductance, 6.3 to 21.4 in transpiration rate, 7.0 to 20.9 and 8.4 to 29.3 in shoot and root potassium, 19.4 to 33.5 and 20.6 to 55.9 in shoot and root K Na-1. The percent decrease in sodium concentration in shoot and root ranged from 8.6 to 14.2 and 4.3 to 23.7. Between 100 and 150 kgs of silicon applied, the desired result was achieved with 150 kg Si ha-1. It is recommended to apply 150 kg Si ha-1 to maneuver the ill effects of alkalinity on maize.