Soybean Canopy Research Articles

Influence of cover cropping and conservation tillage on weeds during the critical period for weed control in soybean

AbstractLimited research has been directed at evaluating the ability of single cover crop plantings to suppress weeds in crops beyond the initial field season. Thus, this experiment was conducted to investigate the ability of a second-year self-regenerated annual and second-year perennial cover crop planting to suppress weeds during the critical period for weed control (CPWC) in soybean crops. Whole-plot treatments included 1) conventional till, 2) no-till with cover crop residue, 3) living mulch + cover crop residue, and 4) living mulch + winter-killed residue. Subplot treatments involved weed management intensity: a) no weed management (weedy), b) weeds manually removed through the CPWC (third node soybean stage; V3), and c) weeds manually removed until soybean canopy closure (weed-free). Overall, total annual cover crop biomass during the second field season was comparable to biomass obtained from direct seeded stands during the initial field season. All cover crop treatments reduced total weed biomass through the CPWC compared to conventional till. Soybean yield was low across all treatments in this experiment. Still, yield was similar between cover crop and conventional till treatments at one site-year, however, yields were lower in all cover crop treatments at the other site-year.

Drought recognition based on feature extraction of multispectral images for the soybean canopy

Drought stress is a major environmental factor in soybean growth. Accurate and rapid detection of phenotypic traits during drought condition is important for water-saving irrigation and variety selection for the drought-resistant plant. The traditional manual measurement of phenotypes is time-consuming and error-prone, thus, an accurate and rapid drought recognition method was proposed based on feature extraction of multispectral images for the soybean canopy. First, the Suinong26 soybean variety was selected as the research object. The multispectral sensor (Parrot Drone SAS, Paris, France) was used to acquire the soybean canopies' multispectral image in four channels (NIR, RED, REG, and GRE). Second, after de-noising the original image using median filtering method, the target image of the soybean canopy in the NIR channel was extracted using a thresholding segmentation algorithm. Third, the segmented image was set as an image template to perform canopy segmentation on the rest of the channel images, the soybean canopy images were obtained in the RED, REG, and GRE channels. In addition, soybean canopies' 9 vegetation indexes (NDVI, GNDVI, NDGI, RVI, DVI, GCI, RECI, RDVI, NLI) and each channel's 7 texture features (mean, standard deviation, smoothness, third moment, information entropy, average gradient, fractal dimension) were calculated by using image fusion and processing technologies. Finally, based on the 37-dimensional features of the extracted multispectral images of the soybean canopy, a drought recognition model was established using a support vector machine (SVM) with a radial basis as the kernel function. The models' recognition accuracy reached 96.87%, and the running time was only 0.493441 s. The proposed model has improved the recognition accuracy by 8.33% on average compared with Genetic Algorithm-Backpropagation (GA-BP) neural network, radial basis neural network (RBF) and random forest (RF) method. The proposed method can provide a theoretical basis and technical support for precise regulation and scientific decision-making of breeding and cultivation management for drought-resistant soybean varieties.

Hyperspectral-to-image transform and CNN transfer learning enhancing soybean LCC estimation

Leaf chlorophyll content (LCC) is a distinct indicator of crop health status used to estimate nutritional stress, diseases, and pests. Thus, accurate LCC information can assist in the monitoring of crop growth. The combined use of hyperspectral and deep learning techniques (e.g., convolutional neural network [CNN] and transfer learning [TL]) can improve the performance of crop LCC estimation. We propose a hyperspectral-to-image transform (HIT) technique for converting canopy hyperspectral reflectance into 2D images. We designed a CNN architecture called LCCNet that fuses the deep and shallow features of CNNs to improve soybean LCC estimation. This study evaluated the combined use of hyperspectral remote sensing (RS), HIT, CNN, and TL techniques to estimate soybean LCC for multiple growth stages. The LCCNet was pre-trained based on a simulated dataset (n = 114,048) from the PROSAIL radiative transfer model (RTM) and used as prior knowledge for this work. The soybean canopy hyperspectral RS dataset (n = 910) was obtained using a FieldSpec 3 spectrometer. The knowledge gained while learning to estimate LCC from PROSAIL RTM was applied when estimating field soybean LCC (Dualex readings). TL was used to enhance the soybean estimation model, called the Soybean-LCCNet (RTM + HIT + CNN + TL) model. We tested the LCC (Dualex readings) estimation performance using (a) HIT + CNN, (b) LCCNet (RTM + HIT + CNN), (c) Soybean-LCCNet (RTM + HIT + CNN + TL), and (d) widely used LCC spectral features + partial least squares regression (PLSR). Four methods were ranked based on their LCC estimation performance: Soybean-LCCNet (R2 = 0.78, RMSE = 4.13 (Dualex readings)) > HIT + CNN (R2 = 0.75, RMSE = 4.41 (Dualex readings)) > PLSR-based method (R2 = 0.61, RMSE = 5.39 (Dualex readings)) > LCCNet (R2 = 0.53, RMSE = 7.11 (Dualex readings)). The main conclusions of this work are as follows: (1) HIT + CNN can provide a more robust LCC estimation performance than the widely used LCC SIs; (2) Fusing the deep and shallow features of CNNs can improve the performance of RS soybean LCC (Dualex readings) estimation; and (3) Soybean-LCCNet can reuse the CNN layer information of a pre-trained LCCNet based on a PROSAIL RTM dataset and improve the soybean LCC estimation performance.

Goniometer in the air: Enabling BRDF measurement of crop canopies using a cable-suspended plant phenotyping platform

The Bidirectional Reflectance Distribution Function (BRDF) quantifies the distribution of the spectral reflectance of a target surface at various viewing and illumination angles. In-field measurement of the BRDF of vegetation canopies improves the characterization of off-nadir measurements and informs radiative transfer models of canopy reflectance, where the Lambertian assumption does not hold. However, current field goniometers are unable to measure BRDF efficiently, especially for tall vegetation across the growing season because of the limitations of clearance, field accessibility, and flexibility of the sensor field of view. In this study, we explored the potential of using a large-scale cable-suspended field phenotyping system to quantify BRDF of the canopy reflectance at selected bands and Vegetation Indices (VIs) of maize and soybean canopies. The system performance benefited from the following conditions: no crop damage, full-season field accessibility of tall canopies, automatic measurement, and accurate positioning. Correlation analysis shows that a strong correlation exists among the reflectance, VIs, and the Green Pixel Fraction. The hemispheric distributions of the spectral reflectance at selected bands and VIs were quantified at multiple dates. Hot spots were observed in the backscatter direction at visible and near-infrared (NIR) bands at the largest sensor zenith angle around the Solar Principal Plane (SPP). In contrast, cold spots of the Normalized Difference Vegetation Index (NDVI) and its related VIs were observed in the backscatter direction around solar zenith angles. The row effect was found for the maize canopy at the NIR band and the Near-infrared reflectance of vegetation (NIRv). NDVI had the lowest anisotropy index values among investigated bands and VIs. This system could be further leveraged to generate rapid and detailed BRDF data at a high spatiotemporal resolution using multiple sensors.

Seasonal climate conditions impact the effectiveness of improving photosynthesis to increase soybean yield.

Photosynthetic stimulations have shown promising outcomes in improving crop photosynthesis, including soybean. However, it is still unclear to what extent these changes can impact photosynthetic assimilation and yield under long-term field climate conditions. In this paper, we present a systematic evaluation of the response of canopy photosynthesis and yield to two critical parameters in leaf photosynthesis: the maximum carboxylation rate of ribulose-1,5-bisphosphate carboxylase/oxygenase (Vcmax) and the maximum electron transport of the ribulose-1,5-bisphosphate regeneration rate (Jmax). Using the field-scale crop model Soybean-BioCro and ten years of observed climate data in Urbana, Illinois, U.S., we conducted sensitivity experiments to estimate the changes in canopy photosynthesis, leaf area index, and biomass due to the changes in Vcmax and Jmax. The results show that 1) Both the canopy photosynthetic assimilation (An) and pod biomass yields were more sensitive to the changes in Jmax, particularly at high atmospheric carbon-dioxide concentrations ([CO2]); 2) Higher [CO2] undermined the effectiveness of increasing the two parameters to improve An and yield; 3) Under the same [CO2], canopy light interception and canopy respiration were key factors that undermined improvements in An and yield; 4) A canopy with smaller leaf area index tended to have a higher yield improvement, and 5) Increases in assimilations and yields were highly dependent on growing-season climatic conditions. The solar radiation, temperature, and relative humidity were the main climate drivers that impacted the yield improvement, and they had opposite correlations with improved yield during the vegetative phase compared to the reproductive phase. In a world with elevated [CO2], genetic engineering crop photosynthesis should focus more on improving Jmax. Further, long-term climate conditions and seasonal variations must be considered to determine the improvements in soybean canopy photosynthesis and yield at the field scale. Quantifying the effectiveness of changing Vcmax and Jmax helps understand their individual and combined contributions to potential improvements in assimilation and yield. This work provides a framework for evaluating how altering the photosynthetic rate parameters impacts soybean yield and assimilation under different seasonal climate scenarios at the field scale.

"Canopy fingerprints" for characterizing three-dimensional point cloud data of soybean canopies.

Advances in imaging hardware allow high throughput capture of the detailed three-dimensional (3D) structure of plant canopies. The point cloud data is typically post-processed to extract coarse-scale geometric features (like volume, surface area, height, etc.) for downstream analysis. We extend feature extraction from 3D point cloud data to various additional features, which we denote as 'canopy fingerprints'. This is motivated by the successful application of the fingerprint concept for molecular fingerprints in chemistry applications and acoustic fingerprints in sound engineering applications. We developed an end-to-end pipeline to generate canopy fingerprints of a three-dimensional point cloud of soybean [Glycine max (L.) Merr.] canopies grown in hill plots captured by a terrestrial laser scanner (TLS). The pipeline includes noise removal, registration, and plot extraction, followed by the canopy fingerprint generation. The canopy fingerprints are generated by splitting the data into multiple sub-canopy scale components and extracting sub-canopy scale geometric features. The generated canopy fingerprints are interpretable and can assist in identifying patterns in a database of canopies, querying similar canopies, or identifying canopies with a certain shape. The framework can be extended to other modalities (for instance, hyperspectral point clouds) and tuned to find the most informative fingerprint representation for downstream tasks. These canopy fingerprints can aid in the utilization of canopy traits at previously unutilized scales, and therefore have applications in plant breeding and resilient crop production.

Dynamic simulation of leaf area index for the soybean canopy based on 3D reconstruction

Leaf area index (LAI) of the soybean canopy was an important indicator to reflect the growth and development of the soybean plant. However, the traditional LAI measurement in the field was expensive, time-consuming, and challenging to achieve high accuracy. Thus, in this study, a calculation method of LAI for the soybean canopy based on 3D reconstruction was proposed, and the dynamic simulation model of canopy LAI was established. First, northeast soybean varieties of Kangxianchong8 and Dongnong252 were taken as the research objects, and a multi-source image synchronous acquisition platform for soybean canopy based on Kinect 2.0 was constructed to obtain the canopy image data from the V3 to R7 growth periods. Second, the 3D structure of the soybean canopy was reconstructed by conditional filtering and statistical filtering. Third, a soybean LAI estimation method was established by canopy analysis. The determination coefficient R2 between the calculated value and the standard value of LAI was greater than 0.99. Finally, a dynamic simulation model of soybean LAI was established based on the Richards model and the genetic parameters of varieties. The results showed that the accuracy of the dynamic simulation model reached above 0.99, which realized the critical technology of rapid detection and dynamic simulation of LAI for the soybean canopy, and provided quantitative dynamic prediction and technical support for scientific regulation of ecology and morphology for the soybean canopy.

Estimation of Chlorophyll Content in Soybean Crop at Different Growth Stages Based on Optimal Spectral Index

Chlorophyll is an important component of crop photosynthesis as it is necessary for the material exchange between crops and the atmosphere. The amount of chlorophyll present reflects the growth and health status of crops. Spectral technology is a feasible method for obtaining crop chlorophyll content. The first-order differential spectral index contains sufficient spectral information related to the chlorophyll content and has a high chlorophyll prediction ability. Therefore, in this study, the hyperspectral index data and chlorophyll content of soybean canopy leaves at different growth stages were obtained. The first-order differential transformation of soybean canopy hyperspectral reflectance data was performed, and five indices, highly correlated with soybean chlorophyll content at each growth stage, were selected as the optimal spectral index input. Four groups of model input variables were divided according to the following four growth stages: four-node (V4), full-bloom (R2), full-fruit (R4), and seed-filling stage (R6). Three machine learning methods, support vector machine (SVM), random forest (RF), and back propagation neural network (BPNN) were used to establish an inversion model of chlorophyll content at different soybean growth stages. The model was then verified. The results showed that the correlation coefficient between the optimal spectral index and chlorophyll content of soybean was above 0.5, the R2 period correlation coefficient was above 0.7, and the R4 period correlation coefficient was above 0.8. The optimal estimation model of soybean and chlorophyll content is established through the combination of the first-order differential spectral index and RF during the R4 period. The optimal estimation model validation set determination coefficient (R2) was 0.854, the root mean square error (RMSE) was 2.627, and the mean relative error (MRE) was 4.669, demonstrating high model accuracy. The results of this study can provide a theoretical basis for monitoring the growth and health of soybean crops at different growth stages.

Importance of solar radiation and the need for improved respect to Sun by Agrometeorologists

Solar radiation refers to energy produced by the Sun, some of which reaches the Earth. This is the primary energy source for most processes in the atmosphere, hydrosphere, and biosphere. Solar radiation is of crucial importance inproviding the energy source for photosynthesis. During 1973 to 1977, when I did my Ph.D. in Agroclimatology in the Iowa State University, USA, I conducted the field experiments during 1975 and 1976 on soil-plant-water relations, growth and nutrient uptake patterns of field grown soybeans under water stress at the Western Iowa Experimental Farm, Castana. During these experiments, the solar radiation was measured with a pyranometer sensor mounted on an anodized aluminimum base. Daily solar radiation values were obtained from an LI-500 integrator attached to the pyranometer sensor. The attenuation of photosynthetically active radiation (PAR) and net radiation (NRAD) in the soybean canopy was determined during the growing season by using vertical profiles of radiation and leaf-area-index values. Once I received my Ph.D., I worked at the Headquarters of the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) in Hyderabad from 1977 to 1983 and at the ICRISAT Sahelian Center, Niamey, Niger from 1984 to 1996. In several field experiments in ICRISAT, the hourly values of net radiation were obtained through net radiometers mounted above the crop canopies. The growth and interception of PAR in a maize/pigeonpea intercrop and sole maize and sole pigeonpea crops grown in large plots were compared. The productivity of maize, sorghum, pigeonpea and a maize/pigeonpea intercrop in the operational research watersheds was measured in relation to the interception of PAR. In another study, it was shown that different shading ability of the crop canopies was one of the factors which determined the crop/weed competition balance and thus contributed to differential crop productivity observed in different cropping systems. Afterwards, when I worked at the World Meteorological Organization from 1996 to 2016 as the Chief of the Agricultural Meteorology Department, as the Director of the Climate Prediction and Adaptation Branch and as the Acting Secretary of the Intergovernmental Panel on Climate Change, a lot of work was done on Solar Radiation. Given all the above work on Solar Radiation, I emphasized the need for improved respect to Sun Bhagawan by Agrometeorologists at the end of this article.

Calculation method of wilting index based on fractal dimension of multispectral images for the soybean canopy

Drought stress is an important environmental factor that restricts the quality and high yield of the soybean. Rapid and nondestructive monitoring of canopy traits under drought stress is very critical to the cultivation and quality improvement of for the soybean plants. To solve the problems of traditional artificial chemical tests in determining soybean wilt index, such as cumbersome, time-consuming, and laborious, a method for calculating wilting index was proposed based on the fractal dimension of multispectral images for the soybean canopy. First, the soybean variety of Suinong 26 was chosen as the experimental subject, the multispectral images of five channels (RGB, GRE, NIR, RED and REG) of the soybean canopy were acquired. Second, the soybean canopy image was extracted by K-means clustering algorithm from the original multispectral image, with an effective segmentation rate (ESR) of 0.9729. Third, on the base of the multispectral image enhanced by the gamma transform for the soybean canopy, a method for calculating wilting index (LBC,LDBC, LDB) was constructed for the soybean canopy by using fractal dimension algorithms including box-counting (BC) dimension, differential box-counting (DBC) dimension and double blanket (DB) dimension. Finally, the proposed method was proved by using the average leaf inclination (LI) and the projected leaf area (PLA). The experimental results showed that the determination coefficients R2 were all above 0.85. The LBC got the highest correlation with LI and PLA in the RGB channel with coefficient determination R2 above 0.8729. The LDB got the highest correlation with LI and PLA in the GRE, NIR, and REG channels with determination coefficient R2 above 0.8824. The LDBC got the highest correlation with LI and PLA in the RED channel with determination coefficient R2 above 0.8857. This achievement presented that the proposed wilting index could characterize the change degree for the ecology and morphology when soybeans were affected by drought stress, which can provide the technical support to scientifically monitor the phenotypic traits in the process of variety breeding, field cultivation for the soybean plant.

Quantification of the Cumulative Shading Capacity in a Maize-Soybean Intercropping System Using an Unmanned Aerial Vehicle.

In intercropping systems, higher crops block direct radiation, resulting in inevitable shading on the lower crops. Cumulative shading capacity (CSC), defined as the amount of direct radiation shaded by higher crops during a growth period, affects the light interception and radiation use efficiency of crops. Previous studies investigated the light interception and distribution of intercropping. However, how to directly quantify the CSC and its inter-row heterogeneity is still unclear. Considering the canopy height differences (Hms, obtained using an unmanned aerial vehicle) and solar position, we developed a shading capacity model (SCM) to quantify the shading on soybean in maize-soybean intercropping systems. Our results indicated that the southernmost row of soybean had the highest shading proportion, with variations observed among treatments composed of strip configurations and plant densities (ranging from 52.44% to 57.44%). The maximum overall CSC in our treatments reached 123.77 MJ m-2. There was a quantitative relationship between CSC and the soybean canopy height increment (y = 3.61×10-2×ln(x)+6.80×10-1, P < 0.001). Assuming that the growth status of maize and soybean was consistent under different planting directions and latitudes, we evaluated the effects of factors (i.e., canopy height difference, latitude, and planting direction) on shading to provide insights for optimizing intercropping planting patterns. The simulation showed that increasing canopy height differences and latitude led to increased shading, and the planting direction with the least shading was about 90° to 120° at the experimental site. The newly proposed SCM offers a quantitative approach for better understanding shading in intercropping systems.

Monitoring of Soybean Maturity Using UAV Remote Sensing and Deep Learning

Soybean breeders must develop early-maturing, standard, and late-maturing varieties for planting at different latitudes to ensure that soybean plants fully utilize solar radiation. Therefore, timely monitoring of soybean breeding line maturity is crucial for soybean harvesting management and yield measurement. Currently, the widely used deep learning models focus more on extracting deep image features, whereas shallow image feature information is ignored. In this study, we designed a new convolutional neural network (CNN) architecture, called DS-SoybeanNet, to improve the performance of unmanned aerial vehicle (UAV)-based soybean maturity information monitoring. DS-SoybeanNet can extract and utilize both shallow and deep image features. We used a high-definition digital camera on board a UAV to collect high-definition soybean canopy digital images. A total of 2662 soybean canopy digital images were obtained from two soybean breeding fields (fields F1 and F2). We compared the soybean maturity classification accuracies of (i) conventional machine learning methods (support vector machine (SVM) and random forest (RF)), (ii) current deep learning methods (InceptionResNetV2, MobileNetV2, and ResNet50), and (iii) our proposed DS-SoybeanNet method. Our results show the following: (1) The conventional machine learning methods (SVM and RF) had faster calculation times than the deep learning methods (InceptionResNetV2, MobileNetV2, and ResNet50) and our proposed DS-SoybeanNet method. For example, the computation speed of RF was 0.03 s per 1000 images. However, the conventional machine learning methods had lower overall accuracies (field F2: 63.37–65.38%) than the proposed DS-SoybeanNet (Field F2: 86.26%). (2) The performances of the current deep learning and conventional machine learning methods notably decreased when tested on a new dataset. For example, the overall accuracies of MobileNetV2 for fields F1 and F2 were 97.52% and 52.75%, respectively. (3) The proposed DS-SoybeanNet model can provide high-performance soybean maturity classification results. It showed a computation speed of 11.770 s per 1000 images and overall accuracies for fields F1 and F2 of 99.19% and 86.26%, respectively.

Growth and development of multiple waterhemp (Amaranthus tuberculatus) cohorts in corn and soybeans

Waterhemp is a troublesome weed species in cropping systems throughout Wisconsin and much of the US Midwest. Coupled with extended emergence window, rapid growth, high genetic diversity, and herbicide resistance to multiple sites of action, waterhemp represents a major challenge for chemical-based weed control. The objective of this experiment was to evaluate the impact of soybean and corn canopy on growth and development of multiple waterhemp cohorts in 2019 and 2020. Treatments consisted of narrow- (38 cm row spacing) and wide-row (76 cm row spacing) soybeans, corn (76 cm row spacing), and fallow (no crop), with 6 waterhemp transplant timings (cohorts) in 2019 and 2020, respectively. Waterhemp seedlings grown in the greenhouse were transplanted at the 2-3 true leaves growth stage to the field at 10-day increments throughout the typical waterhemp emergence season for Wisconsin (June-July). Waterhemp plants were measured for height and harvested for biomass when they reached the flowering stage. The number of days from the first transplant timing to result in 50% and 90% biomass and height reduction (ED50 and ED90) of subsequent waterhemp cohorts were estimated and used as indicators of weed suppression ability by the respective crop. Narrow-row soybeans required fewer days to suppress 50% growth of new waterhemp cohorts followed by corn, wide row soybeans, and fallow, respectively (4, 6, 9, and 14 d for 50% biomass reduction, and 10, 14, 18, and 42 d for 50% height reduction, respectively). Similarly, narrow-row soybeans required fewer days to suppress 90% biomass of waterhemp, followed by corn, wide-row soybeans, and fallow (11, 15, 18, and 78 d, respectively). Conversely, narrow and wide-row soybeans required fewer days to suppress 90% height of new waterhemp cohorts followed by corn and fallow (20, 26, 43, and 85 d, respectively). Rapid soybean canopy closure (e.g., planting soybeans earlier and in narrow row-spacing) combined with other integrated weed management practices have the potential to reduce reliance on POST herbicide application and mitigate herbicide resistance evolution. In corn, mid- to late-season monitoring and management of later emerging waterhemp cohorts is recommended due to longer time required to reach 90% waterhemp growth suppression.

Effects of feeding injury from Popillia japonica (Coleoptera: Scarabaeidae) on soybean spectral reflectance and yield.

Remote sensing has been shown to be a promising technology for the detection and monitoring of plant stresses including insect feeding. Popillia japonica Newman, is an invasive insect species in the United States, and a pest of concern to soybean, Glycine max (L.) Merr., in the upper Midwest. To investigate the effects of P. japonica feeding injury (i.e., defoliation) on soybean canopy spectral reflectance and yield, field trials with plots of caged soybean plants were established during the summers of 2020 and 2021. In each year, field-collected P. japonica adults were released into some of the caged plots, creating a gradient of infestation levels and resulting injury. Estimates of injury caused by P. japonica, ground-based hyperspectral readings, total yield, and yield components were obtained from the caged plots. Injury was greatest in the upper canopy of soybean in plots infested with P. japonica. Overall mean canopy injury (i.e., across lower, middle, and upper canopy) ranged from 0.23 to 6.26%, which is representative of injury levels observed in soybean fields in the Midwest United States. Feeding injury from P. japonica tended to reduce measures of soybean canopy reflectance in near infra-red wavelengths (~700 to 1000 nm). These results indicate that remote sensing has potential for detection of injury from P. japonica and could facilitate scouting for this pest. Effects of P. japonica injury on total yield were not observed, but a reduction in seed size was detected in one of the two years. The threat to soybean yield posed by P. japonica alone appears minimal, but this pest adds to the guild of other defoliating insects in soybean whose combined effects could threaten yield. The results of this research will guide refinement of management recommendations for this pest in soybean and hold relevance for other cropping systems.

Impact of Fungicide Application Timing Based on Soybean Rust Prediction Model on Application Technology and Disease Control

The application of remote sensing techniques and prediction models for soybean rust (SBR) monitoring may result in different fungicide application timings, control efficacy, and spraying performance. This study aimed to evaluate the applicability of a prediction model as a threshold for disease control decision-making and to identify the effect of different application timings on SBR control as well as on the spraying technology. There were two experimental trials that were conducted in a 2 × 4 factorial scheme: 2 cultivars (susceptible and partially resistant to SBR); and four application timings (conventional chemical control at a calendarized system basis; based on the prediction model; at the appearance of the first visible symptoms; and control without fungicide application). Spray deposit and coverage at each application timing were evaluated in the lower and upper region of the soybean canopy through quantitative analysis of a tracer and water-sensitive papers. The prediction model was calculated based on leaf reflectance data that were collected by remote sensing. Application timings impacted the application technology as well as control efficacy. Calendarized system applications were conducted earlier, promoting different spray performances. Spraying at moments when the leaf area index was higher obtained poorer distribution. None of the treatments were capable of achieving high spray penetration into the canopy. The partially resistant cultivar was effective in holding disease progress during the crop season, whereas all treatments with chemical control resulted in less disease impact. The use of the prediction model was effective and promising to be integrated into disease management programs.

Field Studies on the Effect of Rye Cover Crop on Soybean Root Disease and Productivity

Cover crops improve soil and water quality in annual cropping systems, but knowledge of their impact on soybean ( Glycine max L.) seedling and root diseases is limited. The effects of winter rye cover crops ( Secale cereale L.) on soybean population, biomass, root morphology, seedling and root diseases, pathogen incidence, canopy reflectance, and yield were assessed over 2 years in Iowa and Missouri, U.S.A. Plots without a rye cover crop were compared with plots with early-kill rye and late-kill rye cover crops, which were terminated 34 to 49 days or 5 to 17 days before soybean planting, respectively. Soybean shoot dry weight, root rot severity, and incidence of Fusarium spp. and Pythium spp. on roots were not influenced by the treatments. Soybean grain yield and plant population were reduced in the presence of rye in 2 site years, increased in 1 site year, and unchanged in the remaining site years. Soybean canopy reflectance was measured at 810 nm, and measurements were first made at 70 to 80 days after planting (DAP). At least five measurements were obtained at 7- to 15-day intervals, ending at 120 to 125 DAP. Measurements at approximately 120 to 125 DAP differed by treatments but were not consistently associated with the presence or absence of a rye cover crop. Our field studies suggest that Iowa and Missouri soybean farmers can use winter rye as a cover crop in soybean fields with low seedling disease pressure without increasing the risk of seedling and root diseases or suppressing yield. [Formula: see text] Copyright © 2022 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Time-series multispectral imaging in soybean for improving biomass and genomic prediction accuracy.

Multispectral (MS) imaging enables the measurement of characteristics important for increasing the prediction accuracy of genotypic and phenotypic values for yield-related traits. In this study, we evaluated the potential application of temporal MS imaging for the prediction of aboveground biomass (AGB) in soybean [Glycine max (L.) Merr.]. Field experiments with 198 accessions of soybean were conducted with four different irrigation levels. Five vegetation indices (VIs) were calculated using MS images from soybean canopies from early vegetative to early reproductive stage. To predict the genotypic values of AGB, VIs at the different growth stages were used as secondary traits in a multitrait genomic prediction. The prediction accuracy of the genotypic values of AGB from MS and genomic data largely outperformed that of the genomic data alone before the flowering stage (90% of accessions did not flower), suggesting that it would be possible to determine cross-combinations based on the predicted genotypic values of AGB. We compared the prediction accuracy of a model using the five VIs and a model using only one VI to predict the phenotypic values of AGB and found that the difference in prediction accuracy decreased over time at all irrigation levels except for the most severe drought. The difference in the most severe drought was not as small as that in the other treatments. Only the prediction accuracy of a model using the five VIs in the most severe droughts gradually increased over time. Therefore, the optimal timing for MS imaging may depend on the irrigation levels.

Spring-seeded winter rye living mulches enhance crop biodiversity and promote reduced tillage organic soybeans

As recognition increases of the benefits of reducing soil disturbance to preserve soil health, there is mounting interest in developing innovative methods of using cover crops as living mulches to control weeds in organic grain systems. Spring-planted winter cereal rye (Secale cereale L.) interseeded with soybeans (Glycine max. [L.] Merr.) is a promising, yet untested, living mulch system because rye exhibits vigorous growth in the early spring during the critical weed free period, but then dies back as the soybean canopy matures. The objectives of this study were to compare a rye living mulch system with a tilled “organic business-as-usual” control, and to understand the risks and benefits associated with delaying soybean planting date to manage the weed seed bank prior to establishment of rye and soybeans. Three treatments including (1) a June-planted rye and soybean living mulch system, (2) a June planted tilled control, and (3) a May planted tilled control, were compared in terms of weed prevalence and soybean grain yield in a randomized complete block experimental design with four replications implemented across 3 site years from 2019 to 2020. Interseeding rye as a living mulch resulted in consistently higher weed pressure as compared to tilled controls. Increased weed pressure in May- over June-planted controls in 2 of 3 site years indicate planting date influences weed dynamics. Rye biomass was positively correlated with soybean yield (R2 = 0.76, r = 0.87, p &amp;lt; 0.05) and negatively correlated with weed biomass (R2 = 0.63, r = −0.79, p &amp;lt; 0.05). Under optimal conditions where rye biomass was maximized, interseeding rye adequately suppressed weeds without reducing soybean yields as compared to tilled controls. However, under drier conditions with lower rye production, increased weed pressure and reduced yields emphasize the risks associated with living mulch systems.

The impact of electrocution treatments on weed control and weed seed viability in soybean

AbstractField experiments were conducted in 2020 and 2021 to determine the effectiveness of electrocution on several weeds commonly encountered in Missouri soybean production using an implement known as The Weed Zapper™. In the first study, the effectiveness of electrocution on waterhemp, cocklebur, giant and common ragweed, horseweed, giant and yellow foxtail, and barnyardgrass was determined. Electrocution was applied when plants reached average heights and/or growth stages of 30 cm, 60 cm, flowering, pollination, and seed set. Electrocution was applied once or twice, at two different tractor speeds. Electrocution was more effective at the later plant growth stages. Pearson correlation coefficients indicated that control of weed species was most related to plant height and amount of plant moisture at the time of electrocution. When plants contained seed at the time of electrocution, viability was reduced from 54% to 80% among the species evaluated. A second study determined the effect of electrocution on late-season waterhemp plants, and also soybean injury and yield. Electrocution timings took place throughout reproductive soybean growth stages. The control of waterhemp escapes within the soybean trial ranged from 51% to 97%. Yield of soybean electrocuted at the R4 and R6 growth stages was similar to that of the nontreated control, but soybean yield was reduced by 11% to 26% following electrocution at all other timings. However, the visual injury and yield loss observed in these experiments likely represents a worst-case scenario because growers who maintain a clear height differential between waterhemp and the soybean canopy would not need to maintain contact with the soybean canopy. Overall, results from these experiments indicate that electrocution as part of an integrated weed-management program could eliminate late-season herbicide-resistant weed escapes in soybean, and reduce the number and viability of weed seed that return to the soil seedbank.

A Fourier Transform-Based Calculation Method of Wilting Index for Soybean Canopy Using Multispectral Image

The growth process of soybean plants needs a lot of water. The rapid detection of canopy wilting of soybean under drought stress is of great significance for soybean variety breeding, cultivation regulation and fine management. Aiming at the problems of cumbersome and time-consuming when the traditional chemical technology was used to determine soybean wilting index, a calculation method of wilting index for soybean canopy was proposed in this study based on multispectral images’ Fourier transform. Suinong 26, a northeast soybean variety, was taken as the object. First, four kinds of soybean multispectral images of green, red, red-edge and near-infrared channels were acquired by a Sequoia multispectral camera. Second, based on the multispectral reflection image preprocessed by median filter and mean filter, the target area of a multispectral image of the soybean canopy was extracted by the iterative threshold method and affine transformation algorithm, and the effective segmentation rate was 97.02%. In addition, Fourier transform was used to analyze the spectrum characteristics of the soybean canopy’s multispectral image. When the spectrum radius of each channel was 50, the energy reached more than 98% and was concentrated in the low-frequency region of the spectrum center. Finally, according to the difference between the low-frequency DC component and the proportion of total energy in the spectral radius of the multispectral images under normal and drought treatment, a calculation model of the soybean wilting index was constructed based on the energy spectrum of Fourier transform. The results showed that the difference of the wilting index between normal and drought treatment for the four channels (green, near-infrared, red and red-edge) was 2.38, 3.11, 3.56 and 4.11, respectively. The effectiveness of the wilting index was verified and analyzed by using the average leaf inclination angle. The determination coefficient R2 of the four channels between the calculated wilting index and the average leaf inclination angle was more than 0.85. This calculation method can provide a quantitative basis and technical support for the scientific regulation of ecological and morphological phenotypic traits of soybean plants under drought stress.