Soil Plant Analysis Development Research Articles

Complementary Nutrients in Decoupled Aquaponics Enhance Basil Performance

Aquaponics combines raising fish and growing plants by recycling water and nutrients to reduce water consumption and reliance on chemical fertilizers. Coupled aquaponics systems recirculate water between fish and plant crops, whereas decoupled systems send mineralized fish effluent and wastewater unidirectionally to an independent hydroponic loop. Decoupling enables changes to the water, such as pH adjustments and complementary nutrient additions, to promote plant performance. In this study, basil, Ocimum basilicum (L.), was transplanted into 4 L containers filled with decoupled aquaponic (DAP), nutrient-complemented decoupled aquaponic (DAP+), or chemical-based conventional hydroponic (CON) nutrient solutions and grown for 21 days at pH 5.8. Plants grown in DAP+ and CON had greater biomass, height, and Soil Plant Analysis Development (SPAD) chlorophyll index and lower root:shoot biomass ratios than those in DAP. Shoot fresh and dry biomass was 11% greater for CON than DAP+, while height, SPAD chlorophyll index, and root:shoot ratio did not differ. We concluded that added nutrients in DAP+ enhanced performance compared to DAP, and the biologically derived nutrition in DAP+ enhanced performance to be similar, but not equal, to CON. We cannot recommend specific adjustments to the targeted blend of complementary nutrients tested, but findings suggest that complementary nutrients are effective in decoupled aquaponics.

Development of a high-accuracy autonomous sensing system for a field scouting robot

We developed a sensing system that can automatically acquire spectral reflection data using a hyperspectral camera with the aim of developing a field scouting robot. In order to deal with light-source fluctuations that occur during the daytime, we devised a sensing system equipped with artificial light that could be used at night. The fractional vegetation cover (FVC) and the soil plant analysis development (SPAD) value ​​were estimated by calculating the normalized difference vegetation index (NDVI) from the spectral reflection data, and the accuracy was verified by comparing the results with the results obtained during the daytime. When segmenting the vegetation using NDVI and calculating the FVC, the accuracy was 1.4% higher at night than in the day. There was a correlation between the FVC and the measured SPAD value with a coefficient of determination of 0.51 at night. For the regression of the NDVI and the SPAD value ​​of the manually extracted vegetation, the coefficient of determination was 0.85 at night and 0.77 in the day. As a result of sensing specific leaves every hour during the day, the difference in the SPAD value estimated from the NDVI was up to 7.9 SPAD units due to light fluctuation. In the regression of the NDVI and the SPAD value ​​of the vegetation automatically segmented by NDVI, the coefficient of determination did not change from 0.85 during the night, but decreased to 0.71 during the day because of the poor accuracy of the segmentation. The combination of NDVI wavelengths for identifying vegetation and the combination for estimating the SPAD value were almost the same during the night, but not during the day. Since the light environment is constant during nighttime sensing, vegetation information such as the FVC and the SPAD value can be estimated accurately. The method developed in this study, which does not require correction using a standard whiteboard, is suitable for autonomous sensing systems in field scouting robots because this method can minimize the need for human intervention during crop sensing.

Mapping QTLs for morpho-physiological traits related to grain yield under late sown conditions in wheat (Triticum aestivum L.)

The elevating temperature makes heat stress one of the major issues for wheat production globally. To elucidate genetic basis and map heat tolerance QTLs for agroecology of the State of Bihar under NEPZ, a set of 166 doubled haploid lines (DHLs) derived from the cross between PBW343/IC252874 was used. The population was evaluated under normal sown (NS) and late sown (LS) conditions for two cropping seasons. The canopy temperature (CT) showed positive correlations with grain yield, whereas Soil Plant Analysis Development (SPAD) chlorophyll meter value was not significantly correlated and associated with grain yield (GY) in both the normal and late sown conditions. Composite interval mapping identified a total of 12 quantitative trait loci (QTLs) viz., 2 (NS), 10 (LS) mapped on linkage groups 1A, 1D, 2B, 2D, 3B, 4D, 5B, and 6D, during both the crop seasons 2017–18 and 2018–19. Combining the results of these QTLs revealed a major stable QTL for GY on chromosome 3B which explained 11.84% to 21.24% of phenotypic variance under both sowing conditions. QTL for CT and SPAD was detected on chromosome 1A, while QTL for GY on chromosome 3B and 5B. The identified QTLs could be targeted for genetic improvement and marker-assisted selection for heat tolerance in wheat. The tools like SPAD and CT could be exploited to screen a large number of breeding lines.

Prediction of SPAD values for paddy leaves using smartphone-based direct contact imaging method

The Soil Plant Analysis Development (SPAD) value obtained from the SPAD meter is directly related to leaf chlorophyll content. The chlorophyll content is related to nitrogen content means the amount of fertilizer of a crop. Therefore, determining SPAD value is directly involved with crop health. Minolta SPAD meter can directly measure this value, and this is a well-established method in the research field for measuring chlorophyll content. Still, this instrument is too costly, which is beyond a farmers’ reach in the perspective of Bangladesh. The purpose of this study is to predict the SPAD value for paddy leaves using the smartphone-based direct contact imaging method to estimate the chlorophyll content of a paddy leaf. Numerous features were extracted from each image to predict the SPAD values. The features were then used as parameters in the multiple linear regression model. The models' performance was evaluated using images captured from a paddy field using a Minolta SPAD-502 Chlorophyll Meter. The multiple linear regression model's R2 and root mean square error (RMSE) values were 0.71 and 3.6512, respectively. Therefore, this result confirms that the direct digital contact imaging method has the potential to quantify the SPAD value of paddy leaves accurately. However, these results could be more accurate if the image acquisition was made from the seedling to mature stage of the paddy. In the future, an android app will be developed using this value which can directly measure the chlorophyll content of paddy leaves.

Timing and Rates of Two Products Using Hydrogen Peroxide (H2O2) to Control Algae in Ebb and Flow Hydroponic Systems

Algae is not desirable in hydroponics and creates problems such as reduced yield and decreased dissolved oxygen, and affects the physiology of plants and, thus, needs to be controlled. An experiment was conducted in Ebb and Flow hydroponic systems to investigate the application timing and rates of two hydrogen peroxide products (Zerotol and PERpose Plus). Treatments included 35 mL weekly, 35 mL biweekly, 70 mL weekly, 70 mL biweekly, and a control with no application of hydrogen peroxide using a 40-gallon reservoir of water. Pepper ‘Early Jalapeno’ and ‘Lunchbox Red’ and tomato ‘Geronimo’ and ‘Little Sicily’ were used. The study was conducted in a split-plot design with two replications over time. Plant growth parameters, including plant height, flower number, net CO2 assimilation, fresh weight, and dry weight were recorded. Algae data, including dry weight, algae cell counts, and chl a were also measured. Results indicated that with increasing rate and timing of either product decreased algae counts, dry weight, and chl a values. However, weekly and biweekly application of 70 mL of both products were not different for algae quantification. In pepper, plant height, shoot fresh and dry weight, and root fresh and dry weight were found to be significantly greater with Zerotol 35 mL biweekly, Zerotol 70 mL weekly, PERpose Plus 35 mL biweekly, and PERpose Plus 70 mL weekly compared with the control. ‘Lunchbox Red’ was significantly greater than ‘Early Jalapeno’ in all growth parameters, except soil plant analysis development (SPAD). ‘Lunchbox Red’ had the greatest flower number, with weekly application of 70 mL PERpose Plus. In tomato, greatest flower number and SPAD were observed in ‘Geronimo’ with a weekly application of 70 mL PERpose Plus and 70 mL Zerotol, respectively. Greater shoot and root fresh and dry weight for both tomato cultivars were recorded with 35 mL biweekly or 70 mL weekly application with either product. The results from both plants as well as algae analysis suggest that weekly application of 70 mL of either Zerotol or PERpose Plus produced the best results in terms of controlling algae and improving the growth of pepper and tomato plants.

On-Site Detection of Nitrogen Content in Maize using Optical Sensor

Nutrients are essential for the growth of plants and they also increase the yield. Among the major nutrients, nitrogen is the most important nutrient for crop growth. The current study aims to estimate chlorophyll content vis-a-vis nitrogen content using different techniques and attempts to establish comparative benefits among available techniques. Maize crop being highly known for fertilizer responsiveness was chosen to carry out experiments with different levels of nitrogenous fertilizers. A preliminary field survey was taken in the farmer's field in Coimbatore and Salem districts. Two field experiments and one pot culture experiment at different doses of nitrogen input were conducted in 2019, 2020, and 2021 in the Department of Nano science and Technology at Tamil Nadu Agricultural University. In this study conventional nitrogen was applied with various dosage viz.,0 kgha-1(T1), 50 kgha-1 (T2), 100 kgha-1 (T3), 150 kgha-1 (T4), 200 kgha-1 (T5), 250 kgha-1 (T6), 300 kgha-1 (T7). In the pot culture experiment, IFFCO Nano nitrogen was sprayed on the maize leaves with seven different doses which were control (no spray) (T1), conventional nitrogen (T2), 0.5X RDF(Recommended Dose of Fertilizer) (T3), 1.0X RDF (T4), 1.5X RDF (T5), 2.0X RDF (T6), 2.5X RDF (T7). The chlorophyll content in all the trials were estimated using Dimethyl sulfoxide(DMSO), a portable in-house developed nutrient meter, and Spectroradio meter method. Chlorophyll content was measured at two different growth stages of maize viz., 60 & 90 days after sowing. The total nitrogen was analysed using Kjeldahl method in the maize leaf. Among the treatments, with different doses of nitrogen levels, 300 kgha-1 RDN & 2.5X RDF (T7) recorded higher SPAD (Soil Plant Analysis Development) values, chlorophyll “a” and chlorophyll “b” content and total nitrogen content at 60 DAS (Days After Sowing) in all tested methods. Chlorophyll and total nitrogen content were significantly decreased in later days. In a stressed environment with no nitrogen (T1) applied condition the chlorophyll and nitrogen content showed significantly lower levels

Wheat yield and protein estimation with handheld‐ and UAV‐based reflectance measurements

AbstractPrecision agriculture provides efficient means of obtaining real‐time data to guide nitrogen (N) management based on predicted crop profitability. This study was conducted to assess the efficacy of using in‐season measurements (plant height, biomass weight, biomass N, soil plant analysis development [SPAD], GreenSeeker [GS] normalized difference vegetative index [NDVI], and unmanned aerial vehicle [UAV] NDVI) at Feekes 5 (tillering) and Feekes 10 (anthesis) to estimate wheat (Triticum aestivum L.) yield and protein. The secondary aim was to determine whether the accuracy of yield and protein prediction varies by wheat class and cultivar. Six cultivars—hard red spring (HRS) wheat ‘Jefferson’ and ‘SY Basalt’, hard white spring (HWS) wheat ‘Dayn’ and ‘UI Platinum’, and soft white spring (SWS) wheat ‘Seahawk’ and ‘UI Stone’—were planted at two locations in Idaho in 2018–2020. Plots were arranged in a randomized complete block design with four replications with each cultivar evaluated at seven N rates (0, 50, 100, 150, 200, 250, and 300 kg N ha–1). The determination of the Pearson correlation coefficients revealed that all parameters were linearly correlated with yield except for SPAD at Feekes 5 and biomass weight at Feekes 10. Although estimation of in‐season grain protein remains a challenge, NDVI was strongly correlated with yield especially at Feekes 5. The accuracy of yield prediction was similar for all wheat classes. Comparable accuracy of yield estimation was achieved with GS NDVI and UAV NDVI. Both hand‐held and aerial‐based spectral measurements could be used to prescribe N rates to be applied during tiller formation when wheat yield can be optimized.

Predicting protein content of silage maize using remotely sensed multispectral imagery and proximal leaf sensing

Abstract Timely estimation of silage maize protein provides an effective decision to adapt optimized strategies for nitrogen fertilizer management and also harvesting time for farmers. So, this research aimed to investigate whether using vegetative indices (VIs) derived from UAV remotely sensed multispectral (with 520–900 nm wavelengths) imagery and also Soil Plant Analysis Development (SPAD) greenness index can be used to detect the leaf protein concentration (LPC) of silage maize, as a function of various nitrogen rates (0, 50, 100, and 150% of recommended dosage). Results of principal component analysis (PCA) suggested that LPC was highly correlated with leaf greenness index in all developmental stages. In addition, LPC was highly correlated with most of the VIs investigated. A PCA clustering showed the meaningful pattern of N rates. Higher LPC values, VIs, and greenness index were more concentrated in the higher nitrogen (N100% and N150%) sectors. Nitrogen Reflectance Index (NRI) was identified as the most important VIs to monitor and predict LPC in the silage maize field, showing a strong polynomial relationship with LPC in both eight-leaf collar (V8) (R2 = 0.81, p ≤ 0.01) and tasseling (VT) (R2 = 0.98, p ≤ 0.001) stages. In addition, among VIs, the Normalized Difference Vegetation Index (NDVI) demonstrated a significant linear regression relationship with LPC (R2 = 0.80, p ≤ 0.01) in the VT. Findings suggested the high potential of VIs extracted by UAV-taken multispectral imagery and also SPAD proximal sensing to help farmers rapidly diagnose LPC in silage maize, in line with the objectives of precision farming.

Selenium uptake by rocket plants (Eruca sativa) grown in a calcareous soil as affected by Se species, Se rate and a seaweed extract-based biostimulant application

To investigate the potentiality of Eruca sativa (rocket) to be enriched in selenium (Se) and, thus, to promote human health through consumption, a pot experiment was designed. Two rates, 5 and 10 mg/kg soil, of either selenite or selenate sodium salts were applied to appropriate pots, each filled with 1 kg of calcareous soil. Rocket seedlings were transplanted and grown in these pots, and to half of the pots the biostimulant Actiwave was added. Twelve weeks later, the plants were harvested and Se concentrations determined in shoots and roots. Plant growth characteristics were measured and plants biometrics were assessed by soil plant analyses development (SPAD), normalised difference vegetation index (NDVI) and normalised difference red edge (NDRE). Sulfur (S) and phosphorus (P) concentrations in plant samples were also determined to discuss possible interactions among the three elements. The highest Se concentration of 1070.5 mg/kg dry weight (DW) was observed for the high selenate rate without biostimulant, placing rocket in the group of Se hyperaccumulator plants. Toxic effects were recorded for the plants that received the high selenate rate, whereas no toxicity symptoms were observed for either selenite rate. According to Se concentrations in controls, biostimulant application significantly suppressed Se uptake and significantly increased S and P uptake. The same negative biostimulant effect on Se concentration in plants was clear in selenate treatments. When the results were expressed as total uptake (mg/pot), positive correlations among Se, S and P were found for selenate treatments, whereas for selenite treatments, the opposite was observed. Impressively, 1.6 mg Se/pot on a DW basis was accumulated in rocket shoots in the low selenate-rate without biostimulant treatment, corresponding to approximately 30% of the added 5 mg of selenate.

Effects of High-Temperature Stress on Photosynthetic Characteristics and Antioxidant Enzyme System of Paeonia ostii

Paeonia ostii is an economically important oil crop, which has been widely cultivated in the middle and lower reaches of the Yangtze River in China in recent years. Although P. ostii is highly adaptable to the environment, the prolonged high summer temperature in this region severely inhibits its growth, which adversely affects seed yield and quality. In this study, P. ostii plants were subjected to 20°C/15°C (day/night) and 40°C/35°C (day/night) temperatures for 15 days. The changes in physiological and biochemical indicators of P. ostii under high-temperature stress were initially investigated. The results showed that with the deepening of leaf etiolation, chlorophyll a and chlorophyll b concentration, carotenoid concentration, Soil Plant Analysis Development (SPAD) values and leaf relative water content decreased significantly, while both relative electrical conductivity (REC) and free proline concentration showed an upward trend. Meanwhile, the continuous accumulation of reactive oxygen species (ROS) in P. ostii plants, led to an increased activity of antioxidant enzymes including superoxide dismutase (SOD), peroxidase (POD), catalase (CAT) and ascorbate peroxidase (APX). Moreover, with the extension of the high-temperature treatment, the anatomical structures of P. ostii were destroyed, resulting in a decreased photochemical efficiency of the photosystem II (PSII) reaction center and photosynthesis was inhibited. Taken together, these results provide reference values for understanding the physiological response of P. ostii to high-temperature stress and establish a foundation for further research on the relevant underlying molecular mechanisms.

Multispectral Drone Imagery and SRGAN for Rapid Phenotypic Mapping of Individual Chinese Cabbage Plants.

The phenotypic parameters of crop plants can be evaluated accurately and quickly using an unmanned aerial vehicle (UAV) equipped with imaging equipment. In this study, hundreds of images of Chinese cabbage (Brassica rapa L. ssp. pekinensis) germplasm resources were collected with a low-cost UAV system and used to estimate cabbage width, length, and relative chlorophyll content (soil plant analysis development [SPAD] value). The super-resolution generative adversarial network (SRGAN) was used to improve the resolution of the original image, and the semantic segmentation network Unity Networking (UNet) was used to process images for the segmentation of each individual Chinese cabbage. Finally, the actual length and width were calculated on the basis of the pixel value of the individual cabbage and the ground sampling distance. The SPAD value of Chinese cabbage was also analyzed on the basis of an RGB image of a single cabbage after background removal. After comparison of various models, the model in which visible images were enhanced with SRGAN showed the best performance. With the validation set and the UNet model, the segmentation accuracy was 94.43%. For Chinese cabbage dimensions, the model was better at estimating length than width. The R2 of the visible-band model with images enhanced using SRGAN was greater than 0.84. For SPAD prediction, the R2 of the model with images enhanced with SRGAN was greater than 0.78. The root mean square errors of the 3 semantic segmentation network models were all less than 2.18. The results showed that the width, length, and SPAD value of Chinese cabbage predicted using UAV imaging were comparable to those obtained from manual measurements in the field. Overall, this research demonstrates not only that UAVs are useful for acquiring quantitative phenotypic data on Chinese cabbage but also that a regression model can provide reliable SPAD predictions. This approach offers a reliable and convenient phenotyping tool for the investigation of Chinese cabbage breeding traits.

Agronomic parameters as indicators of inbred maize nitrogen status

Inbred maize (Zea mays L.) lines are grown for seed in Puerto Rico. In efforts to increase crop yields, some producers may apply nitrogen (N) fertilizer in excess of crop nutrient requirements. The use of cover crops (CC) in rotation can serve as an alternative to continuous cultivation of maize. This experiment was conducted in field plots of Güamaní (fine-loamy over sandy or sandy-skeletal, mixed, superactive, isohyperthermic Torrifluventic Haplustepts) soil in Guayama, Puerto Rico. The cropping systems evaluated were a maize-cowpea [Vigna unguiculata (L.) Walp.] cover crop rotation versus a typical practice of maize fallow (FA), each with five N fertilizer levels. The maize planting sequence was an initial spring 2014 planting, followed by a winter 2014-2015 planting and a final winter 2015-2016 planting. The cowpea cover crop was planted in the summers of 2014, 2015 and 2016. The inbred maize lines were female lines used for commercial hybrid maize production. Mean maize plant densities ranged from 59,391 to 69,182 plants per hectare for the cropping seasons. Indicators of crop N status were the Soil Plant Analysis Development (SPAD-502) Chlorophyll Meter®measuring leaf greenness; the Normalized Difference Vegetation Index (NDVI, measured with GreenSeeker®) and plant height measurements. The application of N fertilizer significantly influenced (p<0.05) leaf greenness (in five out of 13 occasions) and plant height (in seven of 12 occasions) but not NDVI. The use of cover crop significantly influenced leaf greenness (in four out of nine occasions) and plant height (in three out of eight occasions). The agronomic parameters tested did not predict yield or crop N status, thus are not recommended in the decision-making process of N fertilizer management of inbred maize.

COMBINING ABILITY OF WATERLOGGING TOLERANCE IN WHEAT (TRITICUM AESTIVUM L.)

The mean performance and combining abilities of cross combinations derived from a complete diallel mating and their parents were evaluated under waterlogging conditions. Analysis of variance for combining abilities indicated significant GCA (general combining ability) and SCA (specific combining ability) for single spike yield, SPAD (soil plant analysis development) and leaf area; GCA for NDVI (normalised differences vegetation index) and SCA for Fe and Mn contents in roots and membrane thermal stability. The parents Stendal, Beşköprü and Pamukova 97 were the best combiners in terms of waterlogging tolerance, while Beşköprü × Pamukova 97, Pamukova 97 × Beşköprü, Stendal × Pamukova 97, Stendal × Beşköprü and Beşköprü × Hanlı were identified as the best cross combinations, with high positive specific combining ability effects for most waterlogging related characters.

Study on the Optimization of Hyperspectral Characteristic Bands Combined with Monitoring and Visualization of Pepper Leaf SPAD Value.

Chlorophyll content is an important indicator of plant photosynthesis, which directly affects the growth and yield of crops. Using hyperspectral imaging technology to quickly and non-destructively estimate the soil plant analysis development (SPAD) value of pepper leaf and its distribution inversion is of great significance for agricultural monitoring and precise fertilization during pepper growth. In this study, 150 samples of pepper leaves with different leaf positions were selected, and the hyperspectral image data and SPAD value were collected for the sampled leaves. The correlation coefficient, stability competitive adaptive reweighted sampling (sCARS), and iteratively retaining informative variables (IRIV) methods were used to screen characteristic bands. These were combined with partial least-squares regression (PLSR), extreme gradient boosting (XGBoost), random forest regression (RFR), and gradient boosting decision tree (GBDT) to build regression models. The developed model was then used to build the inversion map of pepper leaf chlorophyll distribution. The research results show that: (1) The IRIV-XGBoost model demonstrates the most comprehensive performance in the modeling and inversion stages, and its , , and are 0.81, 2.76, and 2.30, respectively; (2) The IRIV-XGBoost model was used to calculate the SPAD value of each pixel of pepper leaves, and to subsequently invert the chlorophyll distribution map of pepper leaves at different leaf positions, which can provide support for the intuitive monitoring of crop growth and lay the foundation for the development of hyperspectral field dynamic monitoring sensors.

High intensity and red enriched LED lights increased the growth of lettuce and endive

Changes in plant responses have been associated with different fractions of the visible spectrum and light intensity. Advances in light-emitting diodes (LED) have enabled the study of the effect of narrow wavelengths on plant growth and antioxidant compound synthesis. LED technology also facilitates the incorporation of light sources in a controlled setting where light spectra and intensity can be regulated. The objective of this study was to compare the effect of two commercial light spectra (S1: standard white light with 32.8% blue, 42.5% green, 21.7% red, and 2.4% far-red; S2: AP67 spectrum, designed for horticultural growth, with 16.9% blue, 20.5% green, 49.7% red and 12.3% far red) at two light intensities [low intensity (78 μmol m–2s–1 of photons for S1 and 62 μmol m–2s–1 for S2, and high intensity (HI) (102 and 100 μmol m–2s–1 for S1 and S2, respectively)] on growth and antioxidant compound contents in two leafy vegetables: endive (Cichorium endivia L.) and lettuce (Lactuca sativa L.). Fresh weight (FW), dry weight (DW), and DW% of plants were taken as growth indicators. In addition, leaf number, soil plant analysis development index, leaf area (LA), and specific leaf area were also evaluated. Antioxidant synthesis was measured as total phenol content, total flavonoid content, and antioxidant activity. The results showed that S2 and HI increased the FW, DW, and LA in both species. On the other hand, antioxidant compound contents were significantly increased by HI but did not vary with the spectrum. Highlights - The spectra of LED affected leaf number in lettuce and endive. - S2 spectrum improved growth parameters of both leafy vegetables. - Light intensity improved growth parameters of both leafy vegetables. - Antioxidant compound contents were significantly increased by high intensity LED light.

Rice Height Monitoring between Different Estimation Models Using UAV Photogrammetry and Multispectral Technology

Unmanned aerial vehicle (UAV) photogrammetry was used to monitor crop height in a flooded paddy field. Three multi-rotor UAVs were utilized to conduct flight missions in order to capture RGB (RedGreenBlue) and multispectral images, and these images were analyzed using several different models to provide the best results. Two image sets taken by two UAVs, mounted with RGB cameras of the same resolution and Global Navigation Satellite System (GNSS) receivers of different accuracies, were applied to perform photogrammetry. Two methods were then proposed for creating crop height models (CHMs), one of which was denoted as the M1 method and was based on the Digital Surface Point Cloud (DSPC) and the Digital Terrain Point Cloud (DSPT). The other was denoted as the M2 method and was based on the DSPC and a bathymetric sensor. An image set taken by another UAV mounted with a multispectral camera was used for multispectral-based photogrammetry. A Normal Differential Vegetation Index (NDVI) and a Vegetation Fraction (VF) were then extracted. A new method based on multiple linear regression (MLR) combining the NDVI, the VF, and a Soil Plant Analysis Development (SPAD) value for estimating the measured height (MH) of rice was then proposed and denoted as the M3 method. The results show that the M1 method, the UAV with a GNSS receiver with a higher accuracy, obtained more reliable estimations, while the M2 method, the UAV with a GNSS receiver of moderate accuracy, was actually slightly better. The effect on the performance of CHMs created by the M1 and M2 methods is more negligible in different plots with different treatments; however, remarkably, the more uniform the distribution of vegetation over the water surface, the better the performance. The M3 method, which was created using only a SPAD value and a canopy NDVI value, showed the highest coefficient of determination (R2) for overall MH estimation, 0.838, compared with other combinations.

UAV- and Machine Learning-Based Retrieval of Wheat SPAD Values at the Overwintering Stage for Variety Screening

In recent years, the delay in sowing has become a major obstacle to high wheat yield in Jiangsu Province, one of the major wheat producing areas in China; hence, it is necessary to screen wheat varieties are resilient for late sowing. This study aimed to provide an effective, fast, and non-destructive monitoring method of soil plant analysis development (SPAD) values, which can represent leaf chlorophyll contents, for late-sown winter wheat variety screening. This study acquired multispectral images using an unmanned aerial vehicle (UAV) at the overwintering stage of winter wheat growth, and further processed these images to extract reflectance of five single spectral bands and calculated 26 spectral vegetation indices. Based on these 31 variables, this study combined three variable selection methods (i.e., recursive feature elimination (RFE), random forest (RF), and Pearson correlation coefficient (r)) with four machine learning algorithms (i.e., random forest regression (RFR), linear kernel-based support vector regression (SVR), radial basis function (RBF) kernel-based SVR, and sigmoid kernel-based SVR), resulted in seven SVR models (i.e., RFE-SVR_linear, RF-SVR_linear, RF-SVR_RBF, RF-SVR_sigmoid, r-SVR_linear, r-SVR_RBF, and r-SVR_sigmoid) and three RFR models (i.e., RFE-RFR, RF-RFR, and r-RFR). The performances of the 10 machine learning models were evaluated and compared with each other according to the achieved coefficient of determination (R2), residual prediction deviation (RPD), root mean square error (RMSE), and relative RMSE (RRMSE) in SPAD estimation. Of the 10 models, the best one was the RF-SVR_sigmoid model, which was the combination of the RF variable selection method and the sigmoid kernel-based SVR algorithm. It achieved high accuracy in estimating SPAD values of the wheat canopy (R2 = 0.754, RPD = 2.017, RMSE = 1.716 and RRMSE = 4.504%). The newly developed UAV- and machine learning-based model provided a promising and real time method to monitor chlorophyll contents at the overwintering stage, which can benefit late-sown winter wheat variety screening.

Mild Salinity Stimulates Biochemical Activities and Metabolites Associated with Anticancer Activities in Black Horehound (Ballota nigra L.)

Black horehound (Ballota nigra L.) is one of the most important medicinal plants, as a rich source of health-promoting essential oils and metabolites. Salinity stress affects plant development and alters antioxidant activity and plant metabolite composition. The present research aimed to study the effect of salinity on physiological and biochemical changes and metabolites of B. nigra under greenhouse and in vitro culture conditions. The plants were treated with different concentrations of NaCl (25, 50, 75, 100 mM), and morphological characteristics of the plant were measured. The growth-related traits and soil plant analysis development (SPAD) were decreased both in vivo and in vitro. Additionally, increased salt concentration negatively affected the cell membrane integrity. The total phenolic content and flavonoids of plants growing in the greenhouse increased by 21% at 50 mM of NaCl, but the amounts decreased significantly at higher stress levels (100 mM of NaCl). Antioxidant activity was also measured. Among the NaCl treatments, the most increased DPPH scavenging activities (IC50) under greenhouse and in vitro conditions were detected at mild salinity stress, but the activity significantly decreased in higher salinity treatments (i.e., 75 and 100 mM). In general, with increasing the salinity level to 75 mM, the activities of CAT and APX were significantly upregulated in both greenhouse and in vitro culture conditions. A correlation between total phenolics and flavonoids contents as well as antioxidant activity was obtained. Salinity level caused a shift in the metabolite expression. Mild salinity stress elevated the metabolites involved in anticancer and anti-inflammatory activities, such as β-ionone and caryophyllene oxide. However, the higher salt stress resulted in a significant reduction in their expression. Differential expression of metabolites to various levels of salt stress can be further exploited for the in vitro biosynthesis of metabolites.

Effect of different seedling growing methods on the SPAD, NDVI values and some morphological parameters of four sweet corn (Zea mays L.) hybrids

Abstract The main goal of our investigation was to determine the relationship between different growing methods of sweet corn seedlings and some physiological and morphological parameters of four hybrids in order to get information about the ability of their stress tolerance in a two-year experiment (2019, 2020). Seedlings were grown with and without pre-conditioning. Pre-conditioning is based on growing young plants exposing them to cold stress. Seedling emergence percentage, plant height, total leaf number, the total mass of fresh aboveground biomass, and ear length were determined as well as Soil Plant Analysis Development (SPAD) and Normalized Difference Vegetation Index (NDVI) values. In 2019, the pre-conditioned seedlings were more tolerant to cold stress for most of the tested parameters. Overall, the SPAD and NDVI values of the pre-conditioned seedlings were considered better in both years, however, it was not verified for all the studied hybrids. Among the hybrids, ‘Strongstar’ had the highest benefit from pre-conditioning compared to the standard growing method in terms of resulting in 17.5% higher plants, 13.1% longer ears, and 10.4% higher SPAD values in 2019. In 2020, when the cold stress was not so dominant, ‘Gyöngyhajnal’ gained the most from pre-conditioning with 9.7% higher plants, 32% more fresh aboveground biomass, 6.8% longer ears, 3.6% higher SPAD, and 9.3% higher NDVI values. More emphasis should be placed on the choice of stress-tolerant hybrids as well as on the seedling growing method and the date of transplanting to improve adaptation to the more frequent weather extremes.

Genetic Improvement of Post-Heading Root Morphology and Physiology Facilitating Yield Increase of japonica Inbred Rice

Since genetic improvement greatly promoted an increased yield japonica inbred rice in east China after the 1990s, better root characteristics were certainly expected. In 2018 and 2019, nine japonica inbred rice released in the 1990s, 2000s, and 2010s were investigated to evaluate the changes in root morpho-physiology and identify root traits that contributed to the positive yield trends during the genetic process. The 2010’s rice had 8.0 and 4.3% higher grain yield than the 1990’s and the 2000’s rice, respectively (p < 0.05). Genetic yield gain was mainly attributed to the increased spikelets per panicle. Compared with the 1990’s and the 2000’s rice, the 2010’s rice had higher shoot biomass at heading and maturity (p < 0.05), as well as root biomass (p < 0.05), especially for root biomass of 15–30 cm soil depth. Leaf area index (LAI), soil-plant analysis development (SPAD) values, and leaf photosynthetic rate at middle grain-filling period (MGP) and late grain-filling period (LGP) were all increased. The 2010’s rice had consistently higher root length and volume, root oxidation activity, and root bleeding rate at MGP and LGP than the 1990’s and the 2000’s rice (p < 0.05). Positive correlations were detected between root length and volume, root oxidation activity, and root bleeding rate at MGP, LGP, and SPAD values, leaf photosynthetic rate at MGP and LGP, and higher shoot biomass accumulation after heading and grain yield (p < 0.05 or p < 0.01). The present study implied that genetic improvement optimized post-heading root morphology and physiology, which maintained shoot stay-green and facilitated biomass accumulation and yield increase in japonica inbred rice during the genetic process since the 1990s.