DeepWheat: Estimating Phenotypic Traits from Crop Images with Deep Learning

In this paper, we investigate estimating emergence and biomass traits from color images and elevation maps of wheat field plots. We employ a state-of-the-art deconvolutional network for segmentation and convolutional architectures, with residual and Inception-like layers, to estimate traits via high dimensional nonlinear regression. Evaluation was performed on two different species of wheat, grown in field plots for an experimental plant breeding study. Our framework achieves satisfactory performance with mean and standard deviation of absolute difference of 1.05 and 1.40 counts for emergence and 1.45 and 2.05 for biomass estimation. Our results for counting wheat plants from field images are better than the accuracy reported for the similar, but arguably less difficult, task of counting leaves from indoor images of rosette plants. Our results for biomass estimation, even with a very small dataset, improve upon all previously proposed approaches in the literature.Comment: WACV 2018 (Code repository: https://github.com/p2irc/deepwheat_WACV-2018

Interaction of Trifludimoxazin + Saflufenacil and Pyroxasulfone for Control of False Cleavers (Galium spurium) and Wild Oat (Avena fatua)

Non-Peer ReviewedThe development of herbicide resistant (HR) weeds has led to a need for examining alternative herbicide mechanisms of action for weed control. The objectives of this study were to examine the herbicide interaction of trifludimoxazin + saflufenacil and pyroxasulfone on residual weed control in wheat, and to determine the type of herbicide interaction present: additive, synergistic, or no effect. This study was conducted at four different site locations during the 2020 growing season. Wild oat and false cleavers were cross-seeded in 2 m strips across the experimental area in a split-block design. Treatments comprising of two factors (herbicide group and rate) were applied perpendicular to the weed strips in a randomized complete block design (RCBD) with four replicates. The treatments for this study consisted of four different rates (1, 1.5, 2.0, and 2.5X) of BAS85100H (2:1 pre-mix of saflufenacil and trifludimoxazin (18, 27, 36, 45 g ai h-1) and pyroxasulfone applied alone and as a tank-mix (60, 90, 120, 150 g ai ha-1). Additional treatments included an untreated check and commercial checks of two rates of Heat Complete® (saflufenacil (18, 36 g ai h-1) and pyroxasulfone (60, 120 g ai h-1)). Crop phytotoxicity and herbicide efficacy ratings were taken 7-14, 21-28, and 36-40 days after emergence (DAE). Both herbicide group and rate were shown to be significant at p = 0.05 for each weed species. Flint’s adaptations to Colby’s equation was used to determine the relationship present between the herbicide groups. Group 15 or 14+15 displayed the highest level of wild oat control, with 70% suppression being the highest efficacy observed. Using Flint’s analysis, it could be determined that at higher rates a synergistic relationship may be present between Group 14 and 15 herbicides. The Group 14+15 treatments displayed the highest level of false cleavers control and performed significantly better than that of Group 14 and 15 alone. Flint’s analysis showed that there is an additive relationship present between the Group 14 and 15 herbicides. A comparison of the actual versus expected weed control showed that the weed control obtained for the combination treatments was almost identical to that of the expected for an additive relationship. To further examine the herbicide relationships present, the herbicide application rates in future field studies could be extended and growth chamber experiments could be conducted to gain a more precise dose response. Link to Video Presentation: https://youtu.be/NoYSrM-iRF

Nutrient Management Practices for the Optimization of Organic Milling Oat (Avena sativa)

Non-Peer ReviewedMilling oats are a valuable organic crop in Western Canada. Saskatchewan alone accounts for almost half of Canada’s total organic oat acreage. This two-year cropping sequence study investigates the impact of previous-year stubble (PS), animal manure (AM) type, and manure application timing on subsequent organic oat yields and quality. Two trials were established from 2019 - 2020 in a lattice design at the University of Saskatchewan’s Kernen Research Farm and Goodale Research Farm, outside of Saskatoon, SK. PS crops consisted of fababean, fababean green manure plough down (PD), fallow, and wheat. Composted cattle manure (CM), fresh laying hen manure (HM), and no manure (control) were applied either prior to PS crop (Yr0) or prior to oat crop (Yr1). PS crop type was observed to have a significant effect on oat yields. Compared to yields following wheat stubble (2243-3941 kg ha-1), fallow increased yields by 26% and 50% at Kernen and Goodale, respectively. Oat yields following fababean PD stubble were comparable to fallow at both locations. A PS by AM interaction was present only at the Kernen site. Fababean PD “ HM resulted in yields comparable to fallow applied with either AM. Application timing of manures did not influence oat yields. The preliminary results of this study suggests that PS crop type strongly influences oat yields, more so than manure and its application timing. Furthermore, growing a fababean crop for green manure can be an effective alternative to fallow for the improvement of subsequent organic oat crops

Assessing Phytotoxicity in Lentils (Lens culinaris) Using Hyperspectral UAV Imagery

Non-Peer ReviewedWeed control is of great importance in the successful management of lentil due to its poor competitive ability and short stature. With a lack of effective herbicides and an increase in herbicide resistant weeds; weed control is becoming even more challenging to lentil producers. Field ratings to assess herbicide safety and phytotoxicity in crops can be a tedious and bias associated process. The objective of this research is to determine if phenotyping crop phytotoxicity is possible using UAV imagery. A two-factor randomized complete block design was conducted at two locations in Saskatchewan, Canada in 2019. The factors lentil variety (CDC Greenstar, CDC Maxim, CDC Impala and CDC Improve) and herbicide rates- including the recommended dose and up to ten times the recommended dose of both saflufenacil and metribuzin herbicides. Unmanned aerial vehicle hyperspectral imagery was captured 6, 16 and 23 days after the application of metribuzin in accordance with visual ratings for phytotoxicity. Increasing herbicide dose decreased both field measures of above-ground biomass and plant stand counts. The greatest spectral variation in reflectance was present for metribuzin versus the saflufenacil herbicide. The spectra were noted to differ especially in the green peak, red-edge, and near infrared regions. Further work is being done to analyze imagery data from 2020 to determine if appropriate vegetative indices can be produced to classify different levels of herbicide tolerance. The end goal of this work is to contribute to improving herbicide screening technology with the ability to assess crop phytotoxicity autonomously via computer algorithms. Link to Video Presentation: https://youtu.be/8H7sm-DUki

Multi-Horizon Predictive Soil Mapping of Historical Soil Properties Using Remote Sensing Imagery

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).Natural Sciences and Engineering Research Council of Canada, grant number PDF-557515-2021Peer ReviewedThere is increasing demand for more detailed soil maps to support fine-scale land use planning, soil carbon management, and precision agriculture in Saskatchewan. Predictive soil mapping that incorporates a combination of environmental covariates provides a cost-effective tool for generating finer resolution soil maps. This study focused on mapping soil properties for multiple soil horizons in Saskatchewan using historical legacy soil data in combination with remote sensing band indices, bare soil composite imagery, climate data, and terrain attributes. Mapped soil properties included soil organic carbon content (SOC), total nitrogen, cation exchange capacity (CEC), electrical conductivity (EC), inorganic carbon (IOC), sand and clay content, and total profile soil organic carbon stocks. For each of these soil properties, a recursive feature elimination was undertaken to reduce the number of features in the overall model. This process involved iteratively removing features such that random forest out-of-bag error was minimized. Final random forest models were built for each property and evaluated using an independent test dataset. Overall, predictive models were successful for SOC (R2 = 0.71), total nitrogen (R2 = 0.65), CEC (R2 = 0.46), sand content (R2 = 0.44) and clay content (R2 = 0.55). The methods used in this study enable mapping of a greater geographic region of Saskatchewan compared to those previously established that relied solely on bare soil composite imagery

Improving Saskatchewan-based pea yields through blending of semi-leafless and leafed peasImproving Saskatchewan-based pea yields through blending of semi-leafless and leafed peas

Non-Peer ReviewedWestern Canadian pea production for 2018 decreased by 13% from 2017, which was due to a decline in harvested area. Our previous group research found that the blend of semi-leafless (afaf TLTL) and normal leafed (AFAF TLTL) peas could create a 10% yield increase. To enhance the potential of pea blending. This project evaluated the compatibility of mixing Near-Isogenic Line (NIL) (where the inbred line is only different from the respective recurrent parent in one genomic location) pairs within blends. As well as, we evaluated different blending ratios of semi-leafless with their respective NIL leafed to receive more significant yield increases and avoid confounding variety effects. This study utilized four pea varieties: CDC Amarillo, CDC Centennial, CDC Dakota, and CDC Striker. The research conducted at the University of Saskatchewan's Kernen Research Farm in Saskatoon, Saskatchewan, Canada, over two years in the 2018 and 2019 growing seasons. To evaluating the blending ratio benefits and NIL blending compatibility, data collection focused on yield as well as other field performances such as plant biomass, disease, standing ability, pea leaf development, and phenotyping Digital elevation model (DEM) variation. The ratio combined experiment designed as 50/50, 66/37, 83/17 semi-leafless/NIL leafed blending ratios with two sole leaf type monocultures. In first-year results, we found that Amarillo and Striker varieties in the 83/17 blending ratio facilitated low disease severity. Blends approached similar lodging resistance for semi-leafless types and significantly decreased the leafed peas lodging tendency. The 50/50 Dakota blend increased canopy density and had slower canopy greenness decline when compared with the rest of the Dakota treatments. However, no significant yield improvements by blending detected in the first-year. To determine the compatibility of NIL blends. The variety combined experiment compared NIL blends versus Non-NIL blends (two varieties blend having different genetic backgrounds) and found no significant difference between them in all field data. In the first-year report, despite the results did not detect a significant yield improvement, but we found enhanced pea field characteristics, which should promote a higher yield potential. The comparison between NIL blends and Non-NIL blends statistically showed that NIL blends would adapt in the pea blend, which encourages the possibility of this technology release commercially

A semi‐automatic workflow for plot boundary extraction of irregularly sized and spaced field plots from UAV imagery

Abstract Advances in high‐throughput platforms such as UAVs (unoccupied aerial vehicles) facilitate rapid image‐based phenotypic data acquisition. However, existing plot‐level data extraction methods are unreliable if field plots differ in size and spacing, as often occurs in early‐generation plant breeding trials. To overcome the limitations of conventional plot extraction techniques, a combinational approach with both field‐map information and image classification techniques can be used to optimize plot extraction. The objective of this study was to develop a plot boundary extraction workflow for irregularly sized and spaced field plots from UAV imagery using plot spacing data and vegetation index‐based classifiers. An herbicide screening experiment consisting of three replications of 780 lentil (Lens culinaris Medik.) populations was foliar sprayed with saflufenacil. Aerial image acquisition was conducted during the peak vegetation stage using a RedEdge multispectral camera. A semi‐automatic workflow was compiled in eCognition software to extract lentil plot boundaries. Normalized difference vegetation index (NDVI) was calculated to locate the plots with vegetation and those with low NDVI or no vegetation, and pixel resizing based on plot size and orientation was used to draw the plot boundary. The extraction results showed a precise estimation of plot boundary for all the plots with a wide range of herbicide damage, including the plots with complete loss of vegetation. By using a simple convolutional filter (line filter), image thresholding, and pixel resizing, this approach avoided the use of complex algorithm‐based methodologies. Results suggest that this workflow can be extended to a wide range of phenotyping studies

A Semi-Automatic Workflow to Extract Irregularly Aligned Plots and Sub-Plots: A Case Study on Lentil Breeding Populations

Plant breeding experiments typically contain a large number of plots, and obtaining phenotypic data is an integral part of most studies. Image-based plot-level measurements may not always produce adequate precision and will require sub-plot measurements. To perform image analysis on individual sub-plots, they must be segmented from plots, other sub-plots, and surrounding soil or vegetation. This study aims to introduce a semi-automatic workflow to segment irregularly aligned plots and sub-plots in breeding populations. Imagery from a replicated lentil diversity panel phenotyping experiment with 324 populations was used for this study. Image-based techniques using a convolution filter on an excess green index (ExG) were used to enhance and highlight plot rows and, thus, locate the plot center. Multi-threshold and watershed segmentation were then combined to separate plants, ground, and sub-plot within plots. Algorithms of local maxima and pixel resizing with surface tension parameters were used to detect the centers of sub-plots. A total of 3489 reference data points was collected on 30 random plots for accuracy assessment. It was found that all plots and sub-plots were successfully extracted with an overall plot extraction accuracy of 92%. Our methodology addressed some common issues related to plot segmentation, such as plot alignment and overlapping canopies in the field experiments. The ability to segment and extract phenometric information at the sub-plot level provides opportunities to improve the precision of image-based phenotypic measurements at field-scale