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

Schwalbert, Raíssa; Amado, Telmo Jorge Carneiro; Nieto, Luciana; Varela, Sebastián; Corassa, Geomar Mateus; Horbe, Tiago A.N.; Rice, Charles W.; Peralta, Nahuel Raúl; Ciampitti, Ignacio A.

doi:10.1016/j.biosystemseng.2018.04.020

Cited by 46 publications

(45 citation statements)

References 52 publications

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“…The VIs are related to several properties of plants and these VIs are frequently used for other outcomes, such as disease detection, plant stress, nutrition, yield forecast, and phenology. [61][62][63][64][65][66] From all the VIs tested on this study, OSAVI presented the greatest accuracy when comparing training and validation data sets. The OSAVI was built to optimize the soil-adjusted vegetation index (SAVI) with the aim of reducing the sensitivity of the NDVI to soil background and atmospheric effects.…”

Section: Discussionmentioning

confidence: 85%

“…The third outcome of this study is related to the ability of the indices EVI, EVI2, OSAVI, SAVI, NDVI, NIR‐RED, and NIR/RED to distinguish weeds from other targets. The VIs are related to several properties of plants and these VIs are frequently used for other outcomes, such as disease detection, plant stress, nutrition, yield forecast, and phenology …”

Section: Discussionmentioning

confidence: 99%

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Pre‐planting weed detection based on ground field spectral data

Pott

Amado

Schwalbert

et al. 2019

Pest Management Science

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BACKGROUND Site‐specific weed management (SSWM) demands higher resolution data for mapping weeds in fields, but the success of this tool relies on the efficiency of optical sensors to discriminate weeds relative to other targets (soils and residues) before cash crop establishment. The objectives of this study were to (i) evaluate the accuracy of spectral bands to differentiate weeds (target) and other non‐targets, (ii) access vegetation indices (VIs) to assist in the discrimination process, and (iii) evaluate the accuracy of the thresholds to distinguish weeds relative to non‐targets for each VI using training and validation data sets. RESULTS The main outcomes of this study for effectively distinguishing weeds from other non‐targets are (i) training and validation data exhibited similar spectral curves, (ii) red and near‐infrared spectral bands presented greater accuracy relative to the other bands, and (iii) the tested VIs increased the discrimination accuracy related to single bands, with an overall accuracy above 95% and a kappa above 0.93. CONCLUSION This study provided a novel approach to distinguish weeds from other non‐targets utilizing a ground‐level sensor before cash crop planting based on field spectral data. However, the limitations of this study are related to the spatial resolution to distinguish weeds that might be closer to the one this study presented, and also related to the soil and crop residues conditions at the time of collecting the readings. Overall the results presented contribute to an improved understanding of spectral signatures from different targets (weeds, soils, and residues) before planting time supporting SSWM. © 2019 Society of Chemical Industry

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Section: Discussionmentioning

confidence: 85%

Section: Discussionmentioning

confidence: 99%

Pre‐planting weed detection based on ground field spectral data

Pott

Amado

Schwalbert

et al. 2019

Pest Management Science

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“…Currently, the availability of crop yield monitoring systems mounted to harvesters can provide yield maps but obviously only at the end of the season. Therefore, the rapid development in RS and the need for crop yield monitoring and prediction attracts the attention of many researchers to investigate within-field variability through satellite and aerial RS data [15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

“…The development of empirical equations between vegetation indices (VIs) and crop yield is a simple and operational way to assess within-field variability [24,25], whereas the developed equations have spatial and temporal constraints to apply in another field or another season [19,26,27]. A large number of VIs have been developed to describe crop growth and subsequently yield.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Within-Field Variability of Corn Yield using Sentinel-2 and Machine Learning Techniques

Kayad

Sozzi

Gatto³

et al. 2019

Remote Sensing

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Monitoring and prediction of within-field crop variability can support farmers to make the right decisions in different situations. The current advances in remote sensing and the availability of high resolution, high frequency, and free Sentinel-2 images improve the implementation of Precision Agriculture (PA) for a wider range of farmers. This study investigated the possibility of using vegetation indices (VIs) derived from Sentinel-2 images and machine learning techniques to assess corn (Zea mays) grain yield spatial variability within the field scale. A 22-ha study field in North Italy was monitored between 2016 and 2018; corn yield was measured and recorded by a grain yield monitor mounted on the harvester machine recording more than 20,000 georeferenced yield observation points from the study field for each season. VIs from a total of 34 Sentinel-2 images at different crop ages were analyzed for correlation with the measured yield observations. Multiple regression and two different machine learning approaches were also tested to model corn grain yield. The three main results were the following: (i) the Green Normalized Difference Vegetation Index (GNDVI) provided the highest R2 value of 0.48 for monitoring within-field variability of corn grain yield; (ii) the most suitable period for corn yield monitoring was a crop age between 105 and 135 days from the planting date (R4–R6); (iii) Random Forests was the most accurate machine learning approach for predicting within-field variability of corn yield, with an R2 value of almost 0.6 over an independent validation set of half of the total observations. Based on the results, within-field variability of corn yield for previous seasons could be investigated from archived Sentinel-2 data with GNDVI at crop stage (R4–R6).

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“…Before yield map analysis, grain yield data was filtered to remove the extreme outliers [i.e., values outside of the mean ± 3 standard deviation (Schwalbert et al, 2018)] due to common inherent errors when the combine changed speed and direction (Simbahan et al, 2004). The final data set was normally distributed and comprised 97% of the original data (mean 13.2 Mg ha −1 , standard deviation 2.15 Mg ha −1 ).…”

Section: Data Processing and Analysismentioning

confidence: 99%

Combining Environmental Monitoring and Remote Sensing Technologies to Evaluate Cropping System Nitrogen Dynamics at the Field-Scale

Preza‐Fontes

Bhattarai

Christianson

et al. 2019

Front. Sustain. Food Syst.

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Nitrogen (N) losses from cropping systems in the U.S. Midwest represent a major environmental and economic concern, negatively impacting water and air quality. While considerable research has investigated processes and controls of N losses in this region, significant knowledge gaps still exist, particularly related to the temporal and spatial variability of crop N uptake and environmental losses at the field-scale. The objectives of this study were (i) to describe the unique application of environmental monitoring and remote sensing technologies to quantify and evaluate relationships between artificial subsurface drainage nitrate (NO 3-N) losses, soil nitrous oxide (N 2 O) emissions, soil N concentrations, corn (Zea mays L.) yield, and remote sensing vegetation indices, and (ii) to discuss the benefits and limitations of using recent developments in technology to monitor cropping system N dynamics at field-scale. Preliminary results showed important insights regarding temporal (when N losses primarily occurred) and spatial (measurement footprint) considerations when trying to link N 2 O and NO 3-N leaching losses within a single study to assess relationship between crop productivity and environmental N losses. Remote sensing vegetation indices were significantly correlated with N 2 O emissions, indicating that new technologies (e.g., unmanned aerial vehicle platform) could represent an integrative tool for linking sustainability outcomes with improved agronomic efficiencies, with lower vegetation index values associated with poor crop performance and higher N 2 O emissions. However, the potential for unmanned aerial vehicle to evaluate water quality appears much more limited because NO 3-N losses happened prior to early-season crop growth and image collection. Building on this work, we encourage future research to test the usefulness of remote sensing technologies for monitoring environmental quality, with the goal of providing timely and accurate information to enhance the efficiency and sustainability of food production.

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

Cited by 46 publications

References 52 publications

Pre‐planting weed detection based on ground field spectral data

Pre‐planting weed detection based on ground field spectral data

Monitoring Within-Field Variability of Corn Yield using Sentinel-2 and Machine Learning Techniques

Combining Environmental Monitoring and Remote Sensing Technologies to Evaluate Cropping System Nitrogen Dynamics at the Field-Scale

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