Droplet Size Impact on Efficacy of a Dicamba-plus-Glyphosate Mixture

Chemical weed control remains a widely used component of integrated weed management strategies because of its cost-effectiveness and rapid removal of crop pests. Additionally, dicamba-plus-glyphosate mixtures are a commonly recommended herbicide combination to combat herbicide resistance, specifically in recently commercially released dicamba-tolerant soybean and cotton. However, increased spray drift concerns and antagonistic interactions require that the application process be optimized to maximize biological efficacy while minimizing environmental contamination potential. Field research was conducted in 2016, 2017, and 2018 across three locations (Mississippi, Nebraska, and North Dakota) for a total of six site-years. The objectives were to characterize the efficacy of a range of droplet sizes [150 μm (Fine) to 900 μm (Ultra Coarse)] using a dicamba-plus-glyphosate mixture and to create novel weed management recommendations utilizing pulse-width modulation (PWM) sprayer technology. Results across pooled site-years indicated that a droplet size of 395 μm (Coarse) maximized weed mortality from a dicamba-plus-glyphosate mixture at 94 L ha–1. However, droplet size could be increased to 620 μm (Extremely Coarse) to maintain 90% of the maximum weed mortality while further mitigating particle drift potential. Although generalized droplet size recommendations could be created across site-years, optimum droplet sizes within each site-year varied considerably and may be dependent on weed species, geographic location, weather conditions, and herbicide resistance(s) present in the field. The precise, site-specific application of a dicamba-plus-glyphosate mixture using the results of this research will allow applicators to more effectively utilize PWM sprayers, reduce particle drift potential, maintain biological efficacy, and reduce the selection pressure for the evolution of herbicide-resistant weeds

The history of language learning and teaching in Britain

This article provides an introduction, based on the most recent research available, to the history of language learning and teaching (HoLLT) in Britain. After an overview of the state of research, I consider which languages have been learnt, why and how that has changed; the role of teachers and tests in determining what was taught; changes in how languages have been taught (and why); and the emergence of the modern infrastructure of language teaching policy and practice. I conclude with case study of the contribution of Walter Rippmann, a key figure, in the period 1895 to ca. 1920, a time of professionalisation of language teaching and of efforts towards innovation and change, which set the agenda for many of the major developments of the twentieth century, including a call for scientifically based language teaching and a greater emphasis on the spoken language

UAV-Assisted Thermal Infrared and Multispectral Imaging of Weed Canopies for Glyphosate Resistance Detection

The foundation of contemporary weed management practices in many parts of the world is glyphosate. However, dependency on the effectiveness of herbicide practices has led to overuse through continuous growth of crops resistant to a single mode of action. In order to provide a cost-effective weed management strategy that does not promote glyphosate-resistant weed biotypes, differences between resistant and susceptible biotypes have to be identified accurately in the field conditions. Unmanned Aerial Vehicle (UAV)-assisted thermal and multispectral remote sensing has potential for detecting biophysical characteristics of weed biotypes during the growing season, which includes distinguishing glyphosate-susceptible and glyphosate-resistant weed populations based on canopy temperature and deep learning driven weed identification algorithms. The objective of this study was to identify herbicide resistance after glyphosate application in true field conditions by analyzing the UAV-acquired thermal and multispectral response of kochia, waterhemp, redroot pigweed, and common ragweed. The data were processed in ArcGIS for raster classification as well as spectral comparison of glyphosate-resistant and glyphosate-susceptible weeds. The classification accuracy between the sensors and classification methods of maximum likelihood, random trees, and Support Vector Machine (SVM) were compared. The random trees classifier performed the best at 4 days after application (DAA) for kochia with 62.9% accuracy. The maximum likelihood classifier provided the highest performing result out of all classification methods with an accuracy of 75.2%. A commendable classification was made at 8 DAA where the random trees classifier attained an accuracy of 87.2%. However, thermal reflectance measurements as a predictor for glyphosate resistance within weed populations in field condition was unreliable due to its susceptibility to environmental conditions. Normalized Difference Vegetation Index (NDVI) and a composite reflectance of 842 nm, 705 nm, and 740 nm wavelength managed to provide better classification results than thermal in most cases

UAV-Assisted Thermal Infrared and Multispectral Imaging of Weed Canopies for Glyphosate Resistance Detection

The foundation of contemporary weed management practices in many parts of the world is glyphosate. However, dependency on the effectiveness of herbicide practices has led to overuse through continuous growth of crops resistant to a single mode of action. In order to provide a cost-effective weed management strategy that does not promote glyphosate-resistant weed biotypes, differences between resistant and susceptible biotypes have to be identified accurately in the field conditions. Unmanned Aerial Vehicle (UAV)-assisted thermal and multispectral remote sensing has potential for detecting biophysical characteristics of weed biotypes during the growing season, which includes distinguishing glyphosate-susceptible and glyphosate-resistant weed populations based on canopy temperature and deep learning driven weed identification algorithms. The objective of this study was to identify herbicide resistance after glyphosate application in true field conditions by analyzing the UAV-acquired thermal and multispectral response of kochia, waterhemp, redroot pigweed, and common ragweed. The data were processed in ArcGIS for raster classification as well as spectral comparison of glyphosate-resistant and glyphosate-susceptible weeds. The classification accuracy between the sensors and classification methods of maximum likelihood, random trees, and Support Vector Machine (SVM) were compared. The random trees classifier performed the best at 4 days after application (DAA) for kochia with 62.9% accuracy. The maximum likelihood classifier provided the highest performing result out of all classification methods with an accuracy of 75.2%. A commendable classification was made at 8 DAA where the random trees classifier attained an accuracy of 87.2%. However, thermal reflectance measurements as a predictor for glyphosate resistance within weed populations in field condition was unreliable due to its susceptibility to environmental conditions. Normalized Difference Vegetation Index (NDVI) and a composite reflectance of 842 nm, 705 nm, and 740 nm wavelength managed to provide better classification results than thermal in most cases

A study on deep learning algorithm performance on weed and crop species identification under different image background

Weed identification is fundamental toward developing a deep learning-based weed control system. Deep learning algorithms assist to build a weed detection model by using weed and crop images. The dynamic environmental conditions such as ambient lighting, moving cameras, or varying image backgrounds could affect the performance of deep learning algorithms. There are limited studies on how the different image backgrounds would impact the deep learning algorithms for weed identification. The objective of this research was to test deep learning weed identification model performance in images with potting mix (non-uniform) and black pebbled (uniform) backgrounds interchangeably. The weed and crop images were acquired by four canon digital cameras in the greenhouse with both uniform and non-uniform background conditions. A Convolutional Neural Network (CNN), Visual Group Geometry (VGG16), and Residual Network (ResNet50) deep learning architectures were used to build weed classification models. The model built from uniform background images was tested on images with a non-uniform background, as well as model built from non-uniform background images was tested on images with uniform background. Results showed that the VGG16 and ResNet50 models built from non-uniform background images were evaluated on the uniform background, achieving models' performance with an average f1-score of 82.75% and 75%, respectively. Conversely, the VGG16 and ResNet50 models built from uniform background images were evaluated on the non-uniform background images, achieving models' performance with an average f1-score of 77.5% and 68.4% respectively. Both the VGG16 and ResNet50 models' performances were improved with average f1-score values between 92% and 99% when both uniform and non-uniform background images were used to build the model. It appears that the model performances are reduced when they are tested with images that have different object background than the ones used for building the model

Proximal Hyperspectral Image Dataset of Various Crops and Weeds for Classification via Machine Learning and Deep Learning Techniques

About the DataThe data consists of proximal hyperspectral images of canola, soybean, sugarbeet, kochia, ragweed, redroot pigweed and waterhemp. The data was collected in the near infrared range of 400–1000 nm using Specim FX10 hyperspectral sensor, under controlled halogen light source. The platform and data acquisition software used for data collection was SPECIM's LabScanner system and Lumo Scanner respectively. The raw hyperspectral images were reference calibrated using the white and dark reference image. The hyperspectral images are saved as Numpy Array (.npy) files in their respective directories. Support Jupyter Notebooks provide additional tools for augmentation, region of interest selection, and spectral preprocessing.Benefit of DataData can enhance the number of data points for machine learning and deep learning models, aiding in classification or identification tasks.It can serve as a valuable instrument for studies in spectroscopy.It can assist in the development and testing of three-dimensional data models.Dataset InformationEach plant consists of 20 images, each image having four plants. Except in the case of redroot pigweed which has one plant/image and consists of 40 images.Number of images:canola = 20soybean = 20sugarbeet = 20kochia = 20ragweed = 20redraft_pigweed = 40water hemp = 20</p

Stability of Corn (Zea mays)-Velvetleaf (Abutilon theophrasti) Interference Relationships

The crop-weed interference relationship is a critical component of bioeconomic weed management models. Multi-year field experiments were conducted at five locations to determine the stability of corn-velvetleaf interference relationships across years and locations. Two coefficients (I and A) of a hyperbolic equation were estimated for each data set using nonlinear regression procedures. The I and A coefficients represent percent corn yield loss as velvetleaf density approaches zero, and maximum percent corn yield loss, respectively. The coefficient I was stable across years at two locations, but varied across years at one location. The coefficient A did not vary across years within locations. Both coefficients, however, varied among locations. Results do not support the use of common coefficient estimates for all locations within a region

Droplet Size Impact on Efficacy of a Dicamba-plus-Glyphosate Mixture

Chemical weed control remains a widely used component of integrated weed management strategies because of its cost-effectiveness and rapid removal of crop pests. Additionally, dicamba-plus-glyphosate mixtures are a commonly recommended herbicide combination to combat herbicide resistance, specifically in recently commercially released dicamba-tolerant soybean and cotton. However, increased spray drift concerns and antagonistic interactions require that the application process be optimized to maximize biological efficacy while minimizing environmental contamination potential. Field research was conducted in 2016, 2017, and 2018 across three locations (Mississippi, Nebraska, and North Dakota) for a total of six site-years. The objectives were to characterize the efficacy of a range of droplet sizes [150 μm (Fine) to 900 μm (Ultra Coarse)] using a dicamba-plus-glyphosate mixture and to create novel weed management recommendations utilizing pulse-width modulation (PWM) sprayer technology. Results across pooled site-years indicated that a droplet size of 395 μm (Coarse) maximized weed mortality from a dicamba-plus-glyphosate mixture at 94 L ha–1. However, droplet size could be increased to 620 μm (Extremely Coarse) to maintain 90% of the maximum weed mortality while further mitigating particle drift potential. Although generalized droplet size recommendations could be created across site-years, optimum droplet sizes within each site-year varied considerably and may be dependent on weed species, geographic location, weather conditions, and herbicide resistance(s) present in the field. The precise, site-specific application of a dicamba-plus-glyphosate mixture using the results of this research will allow applicators to more effectively utilize PWM sprayers, reduce particle drift potential, maintain biological efficacy, and reduce the selection pressure for the evolution of herbicide-resistant weeds