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

Neonicotinoid seed treatments of soybean provide negligible benefits to US farmers

Neonicotinoids are the most widely used insecticides worldwide and are typically deployed as seed treatments (hereafter NST) in many grain and oilseed crops, including soybeans. However, there is a surprising dearth of information regarding NST effectiveness in increasing soybean seed yield, and most published data suggest weak, or inconsistent yield benefit. The US is the key soybean-producing nation worldwide and this work includes soybean yield data from 194 randomized and replicated field studies conducted specifically to evaluate the effect of NSTs on soybean seed yield at sites within 14 states from 2006 through 2017. Here we show that across the principal soybean-growing region of the country, there are negligible and management-specific yield benefits attributed to NSTs. Across the entire region, the maximum observed yield benefits due to fungicide (FST = fungicide seed treatment) + neonicotinoid use (FST + NST) reached 0.13 Mg/ha. Across the entire region, combinations of management practices affected the effectiveness of FST + N ST to increase yield but benefits were minimal ranging between 0.01 to 0.22 Mg/ha. Despite widespread use, this practice appears to have little benefit for most of soybean producers; across the entire region, a partial economic analysis further showed inconsistent evidence of a break-even cost of FST or FST + N ST. These results demonstrate that the current widespread prophylactic use of NST in the key soybean-producing areas of the US should be re-evaluated by producers and regulators alike

Neonicotinoid seed treatments of soybean provide negligible benefits to US farmers

Neonicotinoids are the most widely used insecticides worldwide and are typically deployed as seed treatments (hereafter NST) in many grain and oilseed crops, including soybeans. However, there is a surprising dearth of information regarding NST effectiveness in increasing soybean seed yield, and most published data suggest weak, or inconsistent yield benefit. The US is the key soybean-producing nation worldwide and this work includes soybean yield data from 194 randomized and replicated field studies conducted specifically to evaluate the effect of NSTs on soybean seed yield at sites within 14 states from 2006 through 2017. Here we show that across the principal soybean-growing region of the country, there are negligible and management-specific yield benefits attributed to NSTs. Across the entire region, the maximum observed yield benefits due to fungicide (FST = fungicide seed treatment) + neonicotinoid use (FST + NST) reached 0.13 Mg/ha. Across the entire region, combinations of management practices affected the effectiveness of FST + N ST to increase yield but benefits were minimal ranging between 0.01 to 0.22 Mg/ha. Despite widespread use, this practice appears to have little benefit for most of soybean producers; across the entire region, a partial economic analysis further showed inconsistent evidence of a break-even cost of FST or FST + N ST. These results demonstrate that the current widespread prophylactic use of NST in the key soybean-producing areas of the US should be re-evaluated by producers and regulators alike

Impact of Foliar Fungicides on Frogeye Leaf Spot Severity, Radiation Use Efficiency and Yield of Soybean in Iowa

Frogeye leaf spot, caused by Cercospora sojina K. Hara, is a major soybean (Glycine max L. Merr.) disease that has become more prevalent in the upper Midwest and can be managed with foliar fungicides. Incorporating disease severity into a parameter directly related to yield may better relay the impact of disease on yield and yield components than severity alone. Experiments during the 2018 and 2019 growing seasons in fields located in north central and southwestern Iowa were completed to (i) determine how foliar fungicides affected frogeye leaf spot, remotely sensed plant health indicators, and soybean yield, and (ii) compare the relationship and impact of foliar fungicides and frogeye leaf spot on radiation-use efficiency (RUE) estimated using unmanned aerial vehicle reflectance data. Fungicides affected frogeye severity and yield in one of the three locations; in Lewis 2018, the flutriafol + fluoxastrobin treatment reduced frogeye leaf spot severity by over 50% and increased yield by 19% compared to non-treated controls. Applications of foliar fungicides increased canopy coverage compared to non-treated controls (p = 0.012), but NDVI, SPAD values, and RUE values did not differ between fungicide treatments at all three locations. Estimated soybean RUE values (1.05 to 1.66 g Mj−1) were within the range of known values. Overall, this study indicates that RUE can be a valuable resource to estimate the impact of the disease on yield, however, additional research will be needed to use RUE within certain pathosystems

Estimating Soybean Radiation Use Efficiency Using a UAV in Iowa

Radiation use efficiency (RUE) is difficult to estimate and unreasonable to perform on a small plot scale using traditional techniques. However, the increased availability of Unmanned Aerial Vehicles (UAVs) provides the ability to collect spatial and temporal data at high resolution and frequency, which has made a potential workaround. An experiment was completed in Iowa to (i) demonstrate RUE estimation of soybean [Glycine max (L.) Merr.] from reflectance data derived from consumer-grade UAV imagery and (ii) investigate the impact of foliar fungicides on RUE in Iowa. Some fungicides are promoted to have plant health benefits beyond disease protection, and changes in RUE may capture their effect. Frogeye leaf spot severity did not exceed 2%. RUE values ranged from 0.98 to 1.07 and 0.96 to 1.12 across the entire season and the period post-fungicide application, respectively, and fell within the range of previously published soybean RUE values. Plots treated with fluxapyroxad + pyraclostrobin had more canopy cover (p = 0.078) compared to the non-treated control 133 days after planting (DAP), but yields did not differ. A “greening effect” was detected at the end of the sample collection. RUE estimation using UAV imagery can be considered a viable option for the evaluation of management techniques on a small plot scale. Since it is directly related to yield, RUE could be an appropriate parameter to elucidate the impact of plant diseases and other stresses on yield.This article is published as Phillips XA, Kandel YR, Licht MA, Mueller DS. Estimating Soybean Radiation Use Efficiency Using a UAV in Iowa. Agronomy. 2020; 10(12):2002. https://doi.org/10.3390/agronomy10122002. Posted with permission. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)

Estimating Soybean Radiation Use Efficiency Using a UAV in Iowa

Radiation use efficiency (RUE) is difficult to estimate and unreasonable to perform on a small plot scale using traditional techniques. However, the increased availability of Unmanned Aerial Vehicles (UAVs) provides the ability to collect spatial and temporal data at high resolution and frequency, which has made a potential workaround. An experiment was completed in Iowa to (i) demonstrate RUE estimation of soybean [Glycine max (L.) Merr.] from reflectance data derived from consumer-grade UAV imagery and (ii) investigate the impact of foliar fungicides on RUE in Iowa. Some fungicides are promoted to have plant health benefits beyond disease protection, and changes in RUE may capture their effect. Frogeye leaf spot severity did not exceed 2%. RUE values ranged from 0.98 to 1.07 and 0.96 to 1.12 across the entire season and the period post-fungicide application, respectively, and fell within the range of previously published soybean RUE values. Plots treated with fluxapyroxad + pyraclostrobin had more canopy cover (p = 0.078) compared to the non-treated control 133 days after planting (DAP), but yields did not differ. A “greening effect” was detected at the end of the sample collection. RUE estimation using UAV imagery can be considered a viable option for the evaluation of management techniques on a small plot scale. Since it is directly related to yield, RUE could be an appropriate parameter to elucidate the impact of plant diseases and other stresses on yield

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. [Graphic: see text] Copyright © 2022 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license