Validation of agronomic UAV and field measurements for tomato varieties

Enciso, Juan; Avila, Carlos A.; Jung, Jinha; Elsayed-Farag, S.; Chang, Anjin; Yeom, Junho; Landivar, Juan; Maeda, Murilo M.; Chávez, José César Lenin Navarro

doi:10.1016/j.compag.2019.02.011

Cited by 51 publications

(29 citation statements)

References 24 publications

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“…Remote sensing via unmanned aerial vehicles (UAV) is currently being investigated as a means to close the gap because of its capability to acquire the high temporal and spatial resolution data required for high throughput phenotyping over relatively limited areas. UAVs can collect huge quantities of data "on demand", providing opportunities for estimation and prediction of a wide range of agronomic traits [12][13][14][15][16][17][18][19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Multi-Temporal Predictive Modelling of Sorghum Biomass Using UAV-Based Hyperspectral and LiDAR Data

et al. 2020

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High-throughput phenotyping using high spatial, spectral, and temporal resolution remote sensing (RS) data has become a critical part of the plant breeding chain focused on reducing the time and cost of the selection process for the “best” genotypes with respect to the trait(s) of interest. In this paper, the potential of accurate and reliable sorghum biomass prediction using visible and near infrared (VNIR) and short-wave infrared (SWIR) hyperspectral data as well as light detection and ranging (LiDAR) data acquired by sensors mounted on UAV platforms is investigated. Predictive models are developed using classical regression-based machine learning methods for nine experiments conducted during the 2017 and 2018 growing seasons at the Agronomy Center for Research and Education (ACRE) at Purdue University, Indiana, USA. The impact of the regression method, data source, timing of RS and field-based biomass reference data acquisition, and the number of samples on the prediction results are investigated. R2 values for end-of-season biomass ranged from 0.64 to 0.89 for different experiments when features from all the data sources were included. Geometry-based features derived from the LiDAR point cloud to characterize plant structure and chemistry-based features extracted from hyperspectral data provided the most accurate predictions. Evaluation of the impact of the time of data acquisition during the growing season on the prediction results indicated that although the most accurate and reliable predictions of final biomass were achieved using remotely sensed data from mid-season to end-of-season, predictions in mid-season provided adequate results to differentiate between promising varieties for selection. The analysis of variance (ANOVA) of the accuracies of the predictive models showed that both the data source and regression method are important factors for a reliable prediction; however, the data source was more important with 69% significance, versus 28% significance for the regression method.

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Section: Introductionmentioning

confidence: 99%

Multi-Temporal Predictive Modelling of Sorghum Biomass Using UAV-Based Hyperspectral and LiDAR Data

et al. 2020

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“…Moeckel et al (2018) estimated crop height and biomass of eggplant, tomato and cabbage plants from UAV-based Red-Green-Blue (RGB) imagery and Structure-from-Motion and found a good correlation with manual field observations. Enciso et al (2019) used RGB and multi-spectral UAV imagery collected during the growing season of eight difference tomato varieties to estimate plant height, canopy cover and NDVI, and found that height could be accurately estimated and that canopy cover was highly correlated with field-based LAI measurements. However, this assessment was performed at the plot and not individual plant level.…”

Section: Introductionmentioning

confidence: 99%

Unmanned Aerial Vehicle-Based Phenotyping Using Morphometric and Spectral Analysis Can Quantify Responses of Wild Tomato Plants to Salinity Stress

et al. 2019

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With salt stress presenting a major threat to global food production, attention has turned to the identification and breeding of crop cultivars with improved salt tolerance. For instance, some accessions of wild species with higher salt tolerance than commercial varieties are being investigated for their potential to expand food production into marginal areas or to use brackish waters for irrigation. However, assessment of individual plant responses to salt stress in field trials is time-consuming, limiting, for example, longitudinal assessment of large numbers of plants. Developments in Unmanned Aerial Vehicle (UAV) sensing technologies provide a means for extensive, repeated and consistent phenotyping and have significant advantages over standard approaches. In this study, 199 accessions of the wild tomato species, Solanum pimpinellifolium , were evaluated through a field assessment of 600 control and 600 salt-treated plants. UAV imagery was used to: (1) delineate tomato plants from a time-series of eight RGB and two multi-spectral datasets, using an automated object-based image analysis approach; (2) assess four traits, i.e., plant area, growth rates, condition and Plant Projective Cover (PPC) over the growing season; and (3) use the mapped traits to identify the best-performing accessions in terms of yield and salt tolerance. For the first five campaigns, >99% of all tomato plants were automatically detected. The omission rate increased to 2–5% for the last three campaigns because of the presence of dead and senescent plants. Salt-treated plants exhibited a significantly smaller plant area (average control and salt-treated plant areas of 0.55 and 0.29 m 2 , respectively), maximum growth rate (daily maximum growth rate of control and salt-treated plant of 0.034 and 0.013 m 2 , respectively) and PPC (5–16% difference) relative to control plants. Using mapped plant condition, area, growth rate and PPC, we show that it was possible to identify eight out of the top 10 highest yielding accessions and that only five accessions produced high yield under both treatments. Apart from showcasing multi-temporal UAV-based phenotyping capabilities for the assessment of plant performance, this research has implications for agronomic studies of plant salt tolerance and for optimizing agricultural production under saline conditions.

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“…Recent research applying drone technologies to phenotyping crops (Huang et al, 2013; López‐Granados et al, 2016; Gnädinger and Schmidhalter, 2017; Nasir and Tharani, 2017) builds on the extensive experience of the remote‐sensing community (Rouse et al, 1974; Li et al, 2014; Liebisch et al, 2015; Minervini et al, 2015). Several diverse crops, including tomatoes ( Solanum lycopersicum L.), wheat ( Triticum aestivum L.), and maize ( Zea mays L.), have been phenotyped for traits ranging from plant height to stress responses to vegetative parameters measured at the canopy level (Condorelli et al, 2018; Enciso et al, 2019; Gracia‐Romero et al, 2019; Johansen et al, 2019; Wang et al, 2019). As described by Shi et al (2016), the most common flight trajectory is a tight serpentine over the field with the camera in nadir view.…”

Section: Figurementioning

confidence: 99%

Robust mosaicking of maize fields from aerial imagery

et al. 2020

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Premise Aerial imagery from small unmanned aerial vehicle systems is a promising approach for high‐throughput phenotyping and precision agriculture. A key requirement for both applications is to create a field‐scale mosaic of the aerial imagery sequence so that the same features are in registration, a very challenging problem for crop imagery. Methods We have developed an improved mosaicking pipeline, Video Mosaicking and summariZation (VMZ), which uses a novel two‐dimensional mosaicking algorithm that minimizes errors in estimating the transformations between successive frames during registration. The VMZ pipeline uses only the imagery, rather than relying on vehicle telemetry, ground control points, or global positioning system data, to estimate the frame‐to‐frame homographies. It exploits the spatiotemporal ordering of the image frames to reduce the computational complexity of finding corresponding features between frames using feature descriptors. We compared the performance of VMZ to a standard two‐dimensional mosaicking algorithm (AutoStitch) by mosaicking imagery of two maize (Zea mays) research nurseries freely flown with a variety of trajectories. Results The VMZ pipeline produces superior mosaics faster. Using the speeded up robust features (SURF) descriptor, VMZ produces the highest‐quality mosaics. Discussion Our results demonstrate the value of VMZ for the future automated extraction of plant phenotypes and dynamic scouting for crop management.

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Validation of agronomic UAV and field measurements for tomato varieties

Cited by 51 publications

References 24 publications

Multi-Temporal Predictive Modelling of Sorghum Biomass Using UAV-Based Hyperspectral and LiDAR Data

Multi-Temporal Predictive Modelling of Sorghum Biomass Using UAV-Based Hyperspectral and LiDAR Data

Unmanned Aerial Vehicle-Based Phenotyping Using Morphometric and Spectral Analysis Can Quantify Responses of Wild Tomato Plants to Salinity Stress

Robust mosaicking of maize fields from aerial imagery

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