The current mainstream approach of using manual measurements and visual inspections for crop lodging detection is inefficient, time-consuming, and subjective. An innovative method for wheat lodging detection that can overcome or alleviate these shortcomings would be welcomed. This study proposed a systematic approach for wheat lodging detection in research plots (372 experimental plots), which consisted of using unmanned aerial systems (UAS) for aerial imagery acquisition, manual field evaluation, and machine learning algorithms to detect the occurrence or not of lodging. UAS imagery was collected on three different dates (23 and 30 July 2019, and 8 August 2019) after lodging occurred. Traditional machine learning and deep learning were evaluated and compared in this study in terms of classification accuracy and standard deviation. For traditional machine learning, five types of features (i.e. gray level co-occurrence matrix, local binary pattern, Gabor, intensity, and Hu-moment) were extracted and fed into three traditional machine learning algorithms (i.e., random forest (RF), neural network, and support vector machine) for detecting lodged plots. For the datasets on each imagery collection date, the accuracies of the three algorithms were not significantly different from each other. For any of the three algorithms, accuracies on the first and last date datasets had the lowest and highest values, respectively. Incorporating standard deviation as a measurement of performance robustness, RF was determined as the most satisfactory. Regarding deep learning, three different convolutional neural networks (simple convolutional neural network, VGG-16, and GoogLeNet) were tested. For any of the single date datasets, GoogLeNet consistently had superior performance over the other two methods. Further comparisons between RF and GoogLeNet demonstrated that the detection accuracies of the two methods were not significantly different from each other (p > 0.05); hence, the choice of any of the two would not affect the final detection accuracies. However, considering the fact that the average accuracy of GoogLeNet (93%) was larger than RF (91%), it was recommended to use GoogLeNet for wheat lodging detection. This research demonstrated that UAS RGB imagery, coupled with the GoogLeNet machine learning algorithm, can be a novel, reliable, objective, simple, low-cost, and effective (accuracy > 90%) tool for wheat lodging detection.

Bacterial leaf streak (BLS), caused by Xanthomonas translucens pv. undulosa, has increased in both prevalence and severity in the major hard red spring wheat (HRSW)-producing state North Dakota. The disease is readily observed after flag leaf emergence and can quickly lead to defoliation and severe yield loss. The objectives of this research were to document the prevalence and incidence of BLS in North Dakota and provide estimations of yield and economic losses. Trained field scouts determined the incidence and prevalence of BLS in ND on HRSW plants between Feekes growth stage (FGS) 8 and FGS 11.2 from 2015 to 2021, and data were used to determine BLS-affected hectares. Yield data in combination with BLS ratings were obtained from HRSW performance trials to estimate the impact of BLS on yield. The combination of variety identity, hectarage data, BLS-affected hectarage estimates, and yield loss estimates was used to estimate economic losses from BLS in 2019 and 2020. Our data suggest that BLS-affected hectares ranged from 747 to 141,680 between 2015 and 2021. Yield loss was observed at multiple HRSW performance trial locations, with estimated yield losses as high as 60% on susceptible varieties. The amount of BLS-affected hectares was the highest in 2019 and 2020, and direct economic losses for North Dakota HRSW producers were estimated to be as high as $4.7 and $8.0 million, respectively. These data highlight the importance of BLS in HRSW and the need to procure resources for breeding efforts and grower education on management of BLS.

The main techniques for minimizing Fusarium head blight (FHB, or scab) and deoxynivalenol in wheat and barley are well established and generally available: planting of moderately FHB-resistant cultivars, risk monitoring, and timely use of the most effective fungicides. Yet the adoption of these techniques remains uneven across the FHB-prone portions of the U.S. cereal production area. A national survey was undertaken by the U.S. Wheat and Barley Scab Initiative in 17 states where six market classes of wheat and barley are grown. In 2014, 5,107 usable responses were obtained. The highest percentages reporting losses attributable to FHB in the previous 5 years were in North Dakota, Maryland, Kentucky, and states bordering the Great Lakes but across all states, ≥75% of respondents reported no FHB-related losses in the previous 5 years. Adoption of cultivar resistance was uneven by state and market class and was low except among hard red spring wheat growers. In 13 states, a majority of respondents had not applied an FHB-targeted fungicide in the previous 5 years. Although the primary FHB information source varied by state, crop consultants were considered to be an important source or their primary source of information on risk or management of FHB by the largest percentage of respondents. Use of an FHB risk forecasting website was about twice as high in North Dakota as the 17-state average of 6%. The most frequently cited barriers to adopting FHB management practices were weather or logistics preventing timely fungicide application, difficulty in determining flowering timing for fungicide applications, and the impracticality of FHB-reducing rotations. The results highlight the challenges of managing an episodically damaging crop disease and point to specific areas for improvement.

This study was conducted to determine the effect of pre-harvest glyphosate application on spring wheat quality characteristics, ranging from kernel quality to baking quality. Two wheat cultivars were grown in three locations, and glyphosate was applied at the recommended rate at the soft dough stage (early application) and the ripe stage (recommended application time). When glyphosate was applied at the soft dough stage, kernel weight, wheat protein and wet gluten decreased significantly (p ≤ 0.05), however, gluten index significantly increased (p ≤ 0.05). Dough quality, farinograph stability, and quality number were significantly (p ≤ 0.05) higher when glyphosate was applied at the soft dough stage, and absorption and the mixing tolerance index were lower. As for baking quality, loaf volume and mix time were significantly (p ≤ 0.05) higher in the treated samples in comparison to the untreated control, and other baking quality characteristics did not show any significant difference. Overall, the results indicate that spring wheat quality characteristics are impacted to the greatest degree when glyphosate is applied earlier than recommended at the soft dough stage, as opposed to the recommended application at the ripe stage of physiological development.

A significant proportion of wheat (Triticum aestivum L.) yield increases achieved at the farm level in the past can be attributed to improved crop management practices. The important role of crop management practices in increasing the yield potential in wheat sustainably is illustrated with recent research findings from North Dakota. In the dryer regions, no-till and crop rotations significantly improve yield and protect the productivity of the soil. Intensifying management in the wetter regions through increased plant populations and by nitrogen timing did not increase yield in 2005 while fungicide applied at flowering for scab control did. In one study genotype interacted with management, with the most disease-tolerant genotypes producing the highest yield when treated with fungicides. Identifying and exploiting positive genotype by management interactions offers a potential avenue for increasing yield potential in wheat in the future. Identifying or developing crop management practices that hold potential for synergistically improving the yield of genotypes is a significant challenge. Imposing management practices that hold promise for improving yield sustainably on breeding material during the selection process has scope for identifying genotypes with greater yield potential at the farm level when these management practices are employed.