Mid-Season High-Resolution Satellite Imagery for Forecasting Site-Specific Corn Yield

Citation: Peralta, N.R.; Assefa, Y.; Du, J.; Barden, C.J.; Ciampitti, I.A. Mid-Season High-Resolution Satellite Imagery for Forecasting Site-Specific Corn Yield. Remote Sens. 2016, 8, 848.This technical note presents the first Sentinel-2 data service platform for obtaining atmospherically-corrected images and generating the corresponding value-added products for any land surface on Earth (http://s2.boku.eodc.eu/). Using the European Space Agency’s (ESA) Sen2Cor algorithm, the platform processes ESA’s Level-1C top-of-atmosphere reflectance to atmospherically-corrected bottom-of-atmosphere (BoA) reflectance (Level-2A). The processing runs on-demand, with a global coverage, on the Earth Observation Data Centre (EODC), which is a public-private collaborative IT infrastructure in Vienna (Austria) for archiving, processing, and distributing Earth observation (EO) data (http://www.eodc.eu). Using the data service platform, users can submit processing requests and access the results via a user-friendly web page or using a dedicated application programming interface (API). Building on the processed Level-2A data, the platform also creates value-added products with a particular focus on agricultural vegetation monitoring, such as leaf area index (LAI) and broadband hemispherical-directional reflectance factor (HDRF). An analysis of the performance of the data service platform, along with processing capacity, is presented. Some preliminary consistency checks of the algorithm implementation are included to demonstrate the expected product quality. In particular, Sentinel-2 data were compared to atmospherically-corrected Landsat-8 data for six test sites achieving a R2 = 0.90 and Root Mean Square Error (RMSE) = 0.031. LAI was validated for one test site using ground estimations. Results show a very good agreement (R2 = 0.83) and a RMSE of 0.32 m2/m2 (12% of mean value)

Mid-Season High-Resolution Satellite Imagery for Forecasting Site-Specific Corn Yield

A timely and accurate crop yield forecast is crucial to make better decisions on crop management, marketing, and storage by assessing ahead and implementing based on expected crop performance. The objective of this study was to investigate the potential of high-resolution satellite imagery data collected at midgrowing season for identification of within-field variability and to forecast corn yield at different sites within a field. A test was conducted on yield monitor data and RapidEye satellite imagery obtained for 22 cornfields located in five different counties (Clay, Dickinson, Rice, Saline, and Washington) of Kansas (total of 457 ha). Three basic tests were conducted on the data: (1) spatial dependence on each of the yield and vegetation indices (VIs) using Moran’s I test; (2) model selection for the relationship between imagery data and actual yield using ordinary least square regression (OLS) and spatial econometric (SPL) models; and (3) model validation for yield forecasting purposes. Spatial autocorrelation analysis (Moran’s I test) for both yield and VIs (red edge NDVI = NDVIre, normalized difference vegetation index = NDVIr, SRre = red-edge simple ratio, near infrared = NIR and green-NDVI = NDVIG) was tested positive and statistically significant for most of the fields (p < 0.05), except for one. Inclusion of spatial adjustment to model improved the model fit on most fields as compared to OLS models, with the spatial adjustment coefficient significant for half of the fields studied. When selected models were used for prediction to validate dataset, a striking similarity (RMSE = 0.02) was obtained between predicted and observed yield within a field. Yield maps could assist implementing more effective site-specific management tools and could be utilized as a proxy of yield monitor data. In summary, high-resolution satellite imagery data can be reasonably used to forecast yield via utilization of models that include spatial adjustment to inform precision agricultural management decisions.Sociedad Argentina de Informática e Investigación Operativ

Mid-Season High-Resolution Satellite Imagery for Forecasting Site-Specific Corn Yield

A timely and accurate crop yield forecast is crucial to make better decisions on crop management, marketing, and storage by assessing ahead and implementing based on expected crop performance. The objective of this study was to investigate the potential of high-resolution satellite imagery data collected at midgrowing season for identification of within-field variability and to forecast corn yield at different sites within a field. A test was conducted on yield monitor data and RapidEye satellite imagery obtained for 22 cornfields located in five different counties (Clay, Dickinson, Rice, Saline, and Washington) of Kansas (total of 457 ha). Three basic tests were conducted on the data: (1) spatial dependence on each of the yield and vegetation indices (VIs) using Moran’s I test; (2) model selection for the relationship between imagery data and actual yield using ordinary least square regression (OLS) and spatial econometric (SPL) models; and (3) model validation for yield forecasting purposes. Spatial autocorrelation analysis (Moran’s I test) for both yield and VIs (red edge NDVI = NDVIre, normalized difference vegetation index = NDVIr, SRre = red-edge simple ratio, near infrared = NIR and green-NDVI = NDVIG) was tested positive and statistically significant for most of the fields (p < 0.05), except for one. Inclusion of spatial adjustment to model improved the model fit on most fields as compared to OLS models, with the spatial adjustment coefficient significant for half of the fields studied. When selected models were used for prediction to validate dataset, a striking similarity (RMSE = 0.02) was obtained between predicted and observed yield within a field. Yield maps could assist implementing more effective site-specific management tools and could be utilized as a proxy of yield monitor data. In summary, high-resolution satellite imagery data can be reasonably used to forecast yield via utilization of models that include spatial adjustment to inform precision agricultural management decisions.Sociedad Argentina de Informática e Investigación Operativ

Spatio-temporal evaluation of plant height in corn via unmanned aerial systems

Detailed spatial and temporal data on plant growth are critical to guide crop management. Conventional methods to determine field plant traits are intensive, time-consuming, expensive, and limited to small areas. The objective of this study was to examine the integration of data collected via unmanned aerial systems (UAS) at critical corn (Zea mays L.) developmental stages for plant height and its relation to plant biomass. The main steps followed in this research were (1) workflow development for an ultrahigh resolution crop surface model (CSM) with the goal of determining plant height (CSM-estimated plant height) using data gathered from the UAS missions; (2) validation of CSM-estimated plant height with ground-truthing plant height (measured plant height); and (3) final estimation of plant biomass via integration of CSM-estimated plant height with ground-truthing stem diameter data. Results indicated a correlation between CSM-estimated plant height and ground-truthing plant height data at two weeks prior to flowering and at flowering stage, but high predictability at the later growth stage. Log–log analysis on the temporal data confirmed that these relationships are stable, presenting equal slopes for both crop stages evaluated. Concluding, data collected from low-altitude and with a low-cost sensor could be useful in estimating plant height.Sociedad Argentina de Informática e Investigación Operativ

Antioxidant effect of Cannabis sativa L. resin associated with Larrea divaricata Cav. extract: synergism, additivity and antagonism

Cannabis sativa L. (Cannabaceae) is used as anticonvulsant in refractory epilepsy. Convulsions induce oxidative stress in the brain.Synergistic interactions between plants could improve the therapeutic and reduce adverse effects. Larrea divaricata Cav.’s(Zygophylliaceae) antioxidant activity is well-documented. The aim of this work was to evaluate the antioxidant activity of an ethanolicextract of C. sativa (CSR), study cannabidiol’s (CBD) participation and evaluate the possible synergistic effect of CSR with an aqueousextract of L. divaricata (LE). Antioxidant activity was determined by the 1,1-diphenyl-2-picryhydrazyl (DPPH) and the lipid peroxidationassays. For the DPPH assay, the inhibitory concentration 50 (IC50) were: CSR=19.82 ±1.47 µg/ml, LE=24.18±0.54µg/ml,CBD=12.49 ± 0.79µg/ml. The IC50 values for the combinations: CSR + LE 25 µg/ml=2.91±0.10µg/ml (p<0.001), CBD + LE 25µg/ml=4.19±0.11µg/ml (p<0.01). Combination indexes (CI) were: 15 µg/ml CSR / 25 µg/ml LE=0.38 (significant synergism), 10 µg/mlCBD / 25 µg/ml LE=0.39 (significant synergism). IC50 values for the lipid peroxidation assay: CSR=30.50±3.00µg/ml,LE=630.95±63.00µg/ml, CBD=10.20±1.00µg/ml. The IC50 values of the best combinations: CSR + 500 µg/ml LE=2.39±0.10µg/ml (p<0.0001), CBD + 500 µg/ml LE=2.19±0.18 µg/ml (p<0.0001); CI values: 10 µg/ml CSR /500 µg/ml LE=0.16 (strong synergism), 3 µg/mlCSR /500 µg/ml LE=0.36 (significant synergism). Conclusions: CBD is involved in CSR antioxidant activity. LE acted synergistically withCSR in both assays. CSR-LE association at optimal concentrations could be a good option to reach a significant synergism (CI<1). Atlow doses, the combination of CSR and LE could be used as co-adjuvant for the treatment of epilepsy to manage oxidative stress.Fil: Saint Martin, Elina Malén. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Química y Metabolismo del Fármaco. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Instituto de Química y Metabolismo del Fármaco; ArgentinaFil: Marrassini, Carla. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Química y Metabolismo del Fármaco. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Instituto de Química y Metabolismo del Fármaco; ArgentinaFil: Silva Sofrás, Fresia Melina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Química y Metabolismo del Fármaco. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Instituto de Química y Metabolismo del Fármaco; ArgentinaFil: Peralta, Ignacio Nahuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Química y Metabolismo del Fármaco. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Instituto de Química y Metabolismo del Fármaco; ArgentinaFil: Cogoi, Laura Carolina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Química y Metabolismo del Fármaco. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Instituto de Química y Metabolismo del Fármaco; ArgentinaFil: Van Baren, Catalina Maria. Universidad de Buenos Aires. Facultad de Farmacia y Bioquimica. Instituto de Química y Metabolismo del Fármaco; ArgentinaFil: Alonso, María R.. Universidad de Buenos Aires. Facultad de Farmacia y Bioquimica. Instituto de Química y Metabolismo del Fármaco; ArgentinaFil: Anesini, Claudia Alejandra. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Química y Metabolismo del Fármaco. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Instituto de Química y Metabolismo del Fármaco; Argentin

Early-Season Stand Count Determination in Corn via Integration of Imagery from Unmanned Aerial Systems (UAS) and Supervised Learning Techniques

Corn (Zea mays L.) is one of the most sensitive crops to planting pattern and early-season uniformity. The most common method to determine number of plants is by visual inspection on the ground but this field activity becomes time-consuming, labor-intensive, biased, and may lead to less profitable decisions by farmers. The objective of this study was to develop a reliable, timely, and unbiased method for counting corn plants based on ultra-high-resolution imagery acquired from unmanned aerial systems (UAS) to automatically scout fields and applied to real field conditions. A ground sampling distance of 2.4 mm was targeted to extract information at a plant-level basis. First, an excess greenness (ExG) index was used to individualized green pixels from the background, then rows and inter-row contours were identified and extracted. A scalable training procedure was implemented using geometric descriptors as inputs of the classifier. Second, a decision tree was implemented and tested using two training modes in each site to expose the workflow to different ground conditions at the time of the aerial data acquisition. Differences in performance were due to training modes and spatial resolutions in the two sites. For an object classification task, an overall accuracy of 0.96, based on the proportion of corrected assessment of corn and non-corn objects, was obtained for local (per-site) classification, and an accuracy of 0.93 was obtained for the combined training modes. For successful model implementation, plants should have between two to three leaves when images are collected (avoiding overlapping between plants). Best workflow performance was reached at 2.4 mm resolution corresponding to 10 m of altitude (lower altitude); higher altitudes were gradually penalized. The latter was coincident with the larger number of detected green objects in the images and the effectiveness of geometry as descriptor for corn plant detection.Sociedad Argentina de Informática e Investigación Operativ

Early-Season Stand Count Determination in Corn via Integration of Imagery from Unmanned Aerial Systems (UAS) and Supervised Learning Techniques

Corn (Zea mays L.) is one of the most sensitive crops to planting pattern and early-season uniformity. The most common method to determine number of plants is by visual inspection on the ground but this field activity becomes time-consuming, labor-intensive, biased, and may lead to less profitable decisions by farmers. The objective of this study was to develop a reliable, timely, and unbiased method for counting corn plants based on ultra-high-resolution imagery acquired from unmanned aerial systems (UAS) to automatically scout fields and applied to real field conditions. A ground sampling distance of 2.4 mm was targeted to extract information at a plant-level basis. First, an excess greenness (ExG) index was used to individualized green pixels from the background, then rows and inter-row contours were identified and extracted. A scalable training procedure was implemented using geometric descriptors as inputs of the classifier. Second, a decision tree was implemented and tested using two training modes in each site to expose the workflow to different ground conditions at the time of the aerial data acquisition. Differences in performance were due to training modes and spatial resolutions in the two sites. For an object classification task, an overall accuracy of 0.96, based on the proportion of corrected assessment of corn and non-corn objects, was obtained for local (per-site) classification, and an accuracy of 0.93 was obtained for the combined training modes. For successful model implementation, plants should have between two to three leaves when images are collected (avoiding overlapping between plants). Best workflow performance was reached at 2.4 mm resolution corresponding to 10 m of altitude (lower altitude); higher altitudes were gradually penalized. The latter was coincident with the larger number of detected green objects in the images and the effectiveness of geometry as descriptor for corn plant detection.Sociedad Argentina de Informática e Investigación Operativ

Early-Season Stand Count Determination in Corn via Integration of Imagery from Unmanned Aerial Systems (UAS) and Supervised Learning Techniques

Corn (Zea mays L.) is one of the most sensitive crops to planting pattern and early-season uniformity. The most common method to determine number of plants is by visual inspection on the ground but this field activity becomes time-consuming, labor-intensive, biased, and may lead to less profitable decisions by farmers. The objective of this study was to develop a reliable, timely, and unbiased method for counting corn plants based on ultra-high-resolution imagery acquired from unmanned aerial systems (UAS) to automatically scout fields and applied to real field conditions. A ground sampling distance of 2.4 mm was targeted to extract information at a plant-level basis. First, an excess greenness (ExG) index was used to individualized green pixels from the background, then rows and inter-row contours were identified and extracted. A scalable training procedure was implemented using geometric descriptors as inputs of the classifier. Second, a decision tree was implemented and tested using two training modes in each site to expose the workflow to different ground conditions at the time of the aerial data acquisition. Differences in performance were due to training modes and spatial resolutions in the two sites. For an object classification task, an overall accuracy of 0.96, based on the proportion of corrected assessment of corn and non-corn objects, was obtained for local (per-site) classification, and an accuracy of 0.93 was obtained for the combined training modes. For successful model implementation, plants should have between two to three leaves when images are collected (avoiding overlapping between plants). Best workflow performance was reached at 2.4 mm resolution corresponding to 10 m of altitude (lower altitude); higher altitudes were gradually penalized. The latter was coincident with the larger number of detected green objects in the images and the effectiveness of geometry as descriptor for corn plant detection.Sociedad Argentina de Informática e Investigación Operativ

Forecasting maize yield at field scale based on high-resolution satellite imagery

Estimating maize (Zea mays L.) yields at the field level is of great interest to farmers, service dealers, and policy-makers. The main objectives of this study were to: i) provide guidelines on data selection for building yield forecasting models using Sentinel-2 imagery; ii) compare different statistical techniques and vegetation indices (VIs) during model building; and iii) perform spatial and temporal validation to see if empirical models could be applied to other regions or when models' coefficients should be updated. Data analysis was divided into four steps: i) data acquisition and preparation; ii) selection of training data; iii) building of forecasting models; and iv) spatial and temporal validation. Analysis was performed using yield data collected from 19 maize fields located in Brazil (2016 and 2017) and in the United States (2016), and normalized vegetation indices (NDVI, green NDVI and red edge NDVI) derived from Sentinel-2. Main outcomes from this study were: i) data selection impacted yield forecast model and fields with narrow yield variability and/or with skewed data distribution should be avoided; ii) models considering spatial correlation of residuals outperformed Ordinary least squares (OLS) regression; iii) red edge NDVI was most frequently retained into the model compared with the other VIs; and iv) model prediction power was more sensitive to yield data frequency distribution than to the geographical distance or years. Thus, this study provided guidelines to build more accurate maize yield forecasting models, but also established limitations for up-scaling, from farm-level to county, district, and state-scales.Publicado originalmente en: Rai A. Schwalbert, Telmo J.C. Amado, Luciana Nieto, Sebastian Varela, Geomar M. Corassa, Tiago A.N. Horbe, Charles W. Rice, Nahuel R. Peralta, Ignacio A. Ciampitti. Forecasting maize yield at field scale based on high-resolution satellite imagery. Biosystem Engineering. 171: 179–192 DOI: https://doi.org/10.1016/j.biosystemseng.2018.04.020Sociedad Argentina de Informática e Investigación Operativ

Use of geophysical survey as a predictor of the edaphic properties variability in soils used for livestock production

The spatial variability in soils used for livestock production (i.e. Natraquoll and Natraqualf) at farm and paddock scale is usually very high. Understanding this spatial variation within a field is the first step for site-specific crop management. For this reason, we evaluated whether apparent electrical conductivity (ECa), a widely used proximal soil sensing technology, is a potential estimator of the edaphic variability in these types of soils. ECa and elevation data were collected in a paddock of 16 ha. Elevation was negatively associated with ECa. Geo-referenced soil samples were collected and analyzed for soil organic matter (OM) content, pH, the saturation extract electrical conductivity (ECext), available phosphorous (P), and anaerobically incubated Nitrogen (Nan). Relationships between soil properties and ECa were analyzed using regression analysis, principal components analysis (PCA), and stepwise regression. Principal components (PC) and the PC-stepwise were used to determine which soil properties have an important influence on ECa. In this experiment elevation was negatively associated with ECa. The data showed that pH, OM, and ECext exhibited a high correlation with ECa (R2=0.76; 0.70 and 0.65, respectively). Whereas P and Nan showed a lower correlation (R2=0.54 and 0.11 respectively). The model resulting from the PC-stepwise regression analysis explained slightly more than 69% of the total variation of the measured ECa, only retaining PC1. Therefore, ECext, pH and OM were considered key latent variables because they substantially influence the relationship between the PC1 and the ECa (loading factors>0.4). Results showed that ECa is associated with the spatial distribution of some important soil properties. Thus, ECa can be used as a support tool to implement site-specific management in soils for livestock use