A transformer-based approach for early prediction of soybean yield using time-series images

Crop yield prediction which provides critical information for management decision-making is of significant importance in precision agriculture. Traditional manual inspection and calculation are often laborious and time-consuming. For yield prediction using high-resolution images, existing methods, e.g., convolutional neural network, are challenging to model long range multi-level dependencies across image regions. This paper proposes a transformer-based approach for yield prediction using early-stage images and seed information. First, each original image is segmented into plant and soil categories. Two vision transformer (ViT) modules are designed to extract features from each category. Then a transformer module is established to deal with the time-series features. Finally, the image features and seed features are combined to estimate the yield. A case study has been conducted using a dataset that was collected during the 2020 soybean-growing seasons in Canadian fields. Compared with other baseline models, the proposed method can reduce the prediction error by more than 40%. The impact of seed information on predictions is studied both between models and within a single model. The results show that the influence of seed information varies among different plots but it is particularly important for the prediction of low yields

Defining optimal soybean seeding rates and associated risk across North America

Soybean [Glycine max (L.) Merr.] seeding rate research across North America is typically conducted in small geo-political regions where environmental effects on the seeding rate × yield relationship are minimized. Data from 211 individual field studies (∼21,000 data points, 2007–2017) were combined from across North America ranging in yield from 1,000– 7,500 kg ha−1. Cluster analysis was used to stratify each individual field study into similar environmental (soil × climate) clusters and into high (HYL), medium (MYL), and low (LYL) yield levels. Agronomically optimal seeding rates (AOSR) were calculated and Monte Carlo risk analysis was implemented. Within the two northern most clusters the AOSR was higher in the LYL followed by the MYL and then HYL. Within the farthest south cluster, a relatively small (±15,000 seeds ha−1) change in seeding rate from the MYL was required to reach the AOSR of the LYL and HYL, respectively. The increase in seeding rate to reach the LYL AOSR was relatively greater (5x) than the decrease to reach the HYL AOSR within the northern most cluster. Regardless, seeding rates below the AOSR presented substantial risk and potential yield loss, while seeding rates above provided slight risk reduction and yield increases. Specific to LYLs and MYLs, establishing and maintaining an adequate plant stand until harvest maximized yield regardless of the seeding rate, while maximizing seed number was important with lower seeding rates. These findings will help growers manage their soybean seed investment by adjusting seeding rates based upon the productivity of the environment.Fil: Gaspar, Adam P.. Dow Agrosciences Argentina Sociedad de Responsabilidad Limitada.; ArgentinaFil: Mourtzinis, Spyridon. University of Wisconsin; Estados UnidosFil: Kyle, Don. Dow Agrosciences Argentina Sociedad de Responsabilidad Limitada.; ArgentinaFil: Galdi, Eric. Dow Agrosciences Argentina Sociedad de Responsabilidad Limitada.; ArgentinaFil: Lindsey, Laura E.. Ohio State University; Estados UnidosFil: Hamman, William P.. Ohio State University; Estados UnidosFil: Matcham, Emma G. University of Wisconsin; Estados UnidosFil: Kandel, Hans J.. North Dakota State University; Estados UnidosFil: Schmitz, Peder. North Dakota State University; Estados UnidosFil: Stanley, Jordan D.. North Dakota State University; Estados UnidosFil: Schmidt, John P.. Dow Agrosciences Argentina Sociedad de Responsabilidad Limitada.; ArgentinaFil: Mueller, Daren S.. University of Iowa; Estados UnidosFil: Nafziger, Emerson D.. University of Illinois; Estados UnidosFil: Ross, Jeremy. University of Arkansas for Medical Sciences; Estados UnidosFil: Carter, Paul R.. Dow Agrosciences Argentina Sociedad de Responsabilidad Limitada.; ArgentinaFil: Varenhorst, Adam J.. University of South Dakota; Estados UnidosFil: Wise, Kiersten A.. University of Kentucky; Estados UnidosFil: Ciampitti, Ignacio Antonio. Kansas State University; Estados UnidosFil: Carciochi, Walter Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata; Argentina. Kansas State University; Estados UnidosFil: Chilvers, Martin I.. Michigan State University; Estados UnidosFil: Hauswedell, Brady. University of South Dakota; Estados UnidosFil: Tenuta, Albert U.. University of Guelph; CanadáFil: Conley, Shawn P.. University of Wisconsin; Estados Unido

Effect of Seed Treatment and Foliar Crop Protection Products on Sudden Death Syndrome and Yield of Soybean

Sudden death syndrome (SDS), caused by Fusarium virguliforme, is an important soilborne disease of soybean. Risk of SDS increases when cool and wet conditions occur soon after planting. Recently, multiple seed treatment and foliar products have been registered and advertised for management of SDS but not all have been tested side by side in the same field experiment at multiple field locations. In 2015 and 2016, seed treatment fungicides fluopyram and thiabendazole; seed treatment biochemical pesticides citric acid and saponins extract of Chenopodium quinoa; foliar fungicides fluoxastrobin + flutriafol; and an herbicide, lactofen, were evaluated in Illinois, Indiana, Iowa, Michigan, South Dakota, Wisconsin, and Ontario for SDS management. Treatments were tested on SDS-resistant and -susceptible cultivars at each location. Overall, fluopyram provided the highest level of control of root rot and foliar symptoms of SDS among all the treatments. Foliar application of lactofen reduced foliar symptoms in some cases but produced the lowest yield. In 2015, fluopyram reduced the foliar disease index (FDX) by over 50% in both resistant and susceptible cultivars and provided 8.9% yield benefit in susceptible cultivars and 3.5% yield benefit in resistant cultivars compared with the base seed treatment (control). In 2016, fluopyram reduced FDX in both cultivars by over 40% compared with the base seed treatment. For yield in 2016, treatment effect was not significant in the susceptible cultivar while, in the resistant cultivar, fluopyram provided 3.5% greater yield than the base seed treatment. In this study, planting resistant cultivars and using fluopyram seed treatment were the most effective tools for SDS management. However, plant resistance provided an overall better yield-advantage than using fluopyram seed treatment alone. Effective seed treatments can be an economically viable consideration to complement resistant cultivars for managing SDS

Benefits and Profitability of Fluopyram-Amended Seed Treatments for Suppressing Sudden Death Syndrome and Protecting Soybean Yield: A Meta-Analysis

A meta-analytic approach was used to summarize data on the effects of fluopyram-amended seed treatment on sudden death syndrome (SDS) and yield of soybean (Glycine max L.) in over 200 field trials conducted in 12 U.S. states and Ontario, Canada from 2013 to 2015. In those trials, two treatments—the commercial base (CB), and CB plus fluopyram (CBF)—were tested, and all disease and yield data were combined to conduct a random-effects and mixed-effects meta-analysis (test of moderators) to estimate percent control and yield response relative to CB. Overall, a 35% reduction in foliar disease and 295 kg/ha (7.6%) increase in yield were estimated for CBF relative to CB. Sowing date and geographic region affected both estimates. The variation in yield response was explained partially by disease severity (19%), geographic region (8%), and sowing date (10%) but not by the resistance level of the cultivar. The probability of not offsetting the cost of fluopyram was estimated on a range of grain prices and treatment cost combinations. There was a high probability (\u3e80%) of yield gains when disease level was high in any cost–price combinations tested but very low when the foliar symptoms of the disease were absent

Corn Yield Loss Estimates Due to Diseases in the United States and Ontario, Canada from 2012 to 2015

Annual decreases in corn yield caused by diseases were estimated by surveying members of the Corn Disease Working Group in 22 corn-producing states in the United States and in Ontario, Canada, from 2012 through 2015. Estimated loss from each disease varied greatly by state and year. In general, foliar diseases such as northern corn leaf blight, gray leaf spot, and Goss’s wilt commonly caused the largest estimated yield loss in the northern United States and Ontario during nondrought years. Fusarium stalk rot and plant-parasitic nematodes caused the most estimated loss in the southern-most United States. The estimated mean economic loss due to yield loss by corn diseases in the United States and Ontario from 2012 to 2015 was $76.51 USD per acre. The cost of disease-mitigating strategies is another potential source of profit loss. Results from this survey will provide scientists, breeders, government, and educators with data to help inform and prioritize research, policy, and educational efforts in corn pathology and disease management

Recovery Plan for Tar Spot of Corn, Caused by Phyllachora maydis

Tar spot is a foliar disease of corn threatening production across the Americas. The disease was first documented in Mexico in 1904 and is now present in 15 additional countries throughout Central America, South America, and the Caribbean. Researchers and growers in Central America, South America, and the Caribbean consider tar spot to be a disease complex caused by multiple fungal pathogens. When environmental conditions are conducive for infection, these regions have experienced yield losses that can reach up to 100%. In 2015, tar spot was detected in the United States for the first time in Illinois and Indiana. Since that time tar spot has spread across the U.S. corn-growing region, and the disease has been found in Florida, Illinois, Indiana, Iowa, Michigan, Minnesota, Missouri, Ohio, Pennsylvania, and Wisconsin. In 2020, tar spot was also found in southwest Ontario, Canada. Losses in the United States due to tar spot totaled an estimated 241 million bushels from 2018 to 2020. With the potential to continue to spread across the U.S. corn-growing states, much greater losses could result when environmental conditions are conducive.This plan is published as Rocco da Silva, Camila, Jill Check, Joshua S. MacCready, Amos E. Alakonya, Robert Beiriger, Kaitlyn M. Bissonnette, Alyssa Collins et al. "Recovery Plan for Tar Spot of Corn, Caused by Phyllachora maydis." Plant Health Progress (2021). The American Phytopathological Society, 2021. doi:10.1094/PHP-04-21-0074-RP. This article is in the public domain and not copyrightable

Defining optimal soybean seeding rates and associated risk across North America

Soybean [Glycine max (L.) Merr.] seeding rate research across North America is typically conducted in small geo-political regions where environmental effects on the seeding rate × yield relationship are minimized. Data from 211 individual field studies (∼21,000 data points, 2007–2017) were combined from across North America ranging in yield from 1,000– 7,500 kg ha−1. Cluster analysis was used to stratify each individual field study into similar environmental (soil × climate) clusters and into high (HYL), medium (MYL), and low (LYL) yield levels. Agronomically optimal seeding rates (AOSR) were calculated and Monte Carlo risk analysis was implemented. Within the two northern most clusters the AOSR was higher in the LYL followed by the MYL and then HYL. Within the farthest south cluster, a relatively small (±15,000 seeds ha−1) change in seeding rate from the MYL was required to reach the AOSR of the LYL and HYL, respectively. The increase in seeding rate to reach the LYL AOSR was relatively greater (5x) than the decrease to reach the HYL AOSR within the northern most cluster. Regardless, seeding rates below the AOSR presented substantial risk and potential yield loss, while seeding rates above provided slight risk reduction and yield increases. Specific to LYLs and MYLs, establishing and maintaining an adequate plant stand until harvest maximized yield regardless of the seeding rate, while maximizing seed number was important with lower seeding rates. These findings will help growers manage their soybean seed investment by adjusting seeding rates based upon the productivity of the environment.Fil: Gaspar, Adam P.. Dow Agrosciences Argentina Sociedad de Responsabilidad Limitada.; ArgentinaFil: Mourtzinis, Spyridon. University of Wisconsin; Estados UnidosFil: Kyle, Don. Dow Agrosciences Argentina Sociedad de Responsabilidad Limitada.; ArgentinaFil: Galdi, Eric. Dow Agrosciences Argentina Sociedad de Responsabilidad Limitada.; ArgentinaFil: Lindsey, Laura E.. Ohio State University; Estados UnidosFil: Hamman, William P.. Ohio State University; Estados UnidosFil: Matcham, Emma G. University of Wisconsin; Estados UnidosFil: Kandel, Hans J.. North Dakota State University; Estados UnidosFil: Schmitz, Peder. North Dakota State University; Estados UnidosFil: Stanley, Jordan D.. North Dakota State University; Estados UnidosFil: Schmidt, John P.. Dow Agrosciences Argentina Sociedad de Responsabilidad Limitada.; ArgentinaFil: Mueller, Daren S.. University of Iowa; Estados UnidosFil: Nafziger, Emerson D.. University of Illinois; Estados UnidosFil: Ross, Jeremy. University of Arkansas for Medical Sciences; Estados UnidosFil: Carter, Paul R.. Dow Agrosciences Argentina Sociedad de Responsabilidad Limitada.; ArgentinaFil: Varenhorst, Adam J.. University of South Dakota; Estados UnidosFil: Wise, Kiersten A.. University of Kentucky; Estados UnidosFil: Ciampitti, Ignacio Antonio. Kansas State University; Estados UnidosFil: Carciochi, Walter Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata; Argentina. Kansas State University; Estados UnidosFil: Chilvers, Martin I.. Michigan State University; Estados UnidosFil: Hauswedell, Brady. University of South Dakota; Estados UnidosFil: Tenuta, Albert U.. University of Guelph; CanadáFil: Conley, Shawn P.. University of Wisconsin; Estados Unido