Estimating plant growth parameters using an energy minimization-based stereovision model

Lati, Ran Nisim; Filin, Sagi; Eizenberg, Hanan

doi:10.1016/j.compag.2013.07.012

Cited by 57 publications

(38 citation statements)

References 24 publications

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“…Height may be a useful indicator of yield, carbohydrate storage capacity and susceptibility to lodging [9,10] as well as being an essential parameter for site-specific management practices [11]. Monitoring crop height during development stages is a reflection of cultivar and growing conditions.…”

Section: Phenotypingmentioning

confidence: 99%

High Throughput Field Phenotyping of Wheat Plant Height and Growth Rate in Field Plot Trials Using UAV Based Remote Sensing

et al. 2016

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Abstract:There is a growing need to increase global crop yields, whilst minimising use of resources such as land, fertilisers and water. Agricultural researchers use ground-based observations to identify, select and develop crops with favourable genotypes and phenotypes; however, the ability to collect rapid, high quality and high volume phenotypic data in open fields is restricting this. This study develops and assesses a method for deriving crop height and growth rate rapidly from multi-temporal, very high spatial resolution (1 cm/pixel), 3D digital surface models of crop field trials produced via Structure from Motion (SfM) photogrammetry using aerial imagery collected through repeated campaigns flying an Unmanned Aerial Vehicle (UAV) with a mounted Red Green Blue (RGB) camera. We compare UAV SfM modelled crop heights to those derived from terrestrial laser scanner (TLS) and to the standard field measurement of crop height conducted using a 2 m rule. The most accurate UAV-derived surface model and the TLS both achieve a Root Mean Squared Error (RMSE) of 0.03 m compared to the existing manual 2 m rule method. The optimised UAV method was then applied to the growing season of a winter wheat field phenotyping experiment containing 25 different varieties grown in 27 m 2 plots and subject to four different nitrogen fertiliser treatments. Accuracy assessments at different stages of crop growth produced consistently low RMSE values (0.07, 0.02 and 0.03 m for May, June and July, respectively), enabling crop growth rate to be derived from differencing of the multi-temporal surface models. We find growth rates range from −13 mm/day to 17 mm/day. Our results clearly display the impact of variable nitrogen fertiliser rates on crop growth. Digital surface models produced provide a novel spatial mapping of crop height variation both at the field scale and also within individual plots. This study proves UAV based SfM has the potential to become a new standard for high-throughput phenotyping of in-field crop heights.

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Section: Phenotypingmentioning

confidence: 99%

High Throughput Field Phenotyping of Wheat Plant Height and Growth Rate in Field Plot Trials Using UAV Based Remote Sensing

et al. 2016

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“…Plant height is considered as a good indicator for plant growth and biomass [7,8]. However, the use of classical ground-based height measurements is time consuming, and can rarely be applied non-destructively across larger areas in a repetitive manner [8].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Vegetable Crop Parameter by Multi-temporal UAV-Borne Images

et al. 2018

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3D point cloud analysis of imagery collected by unmanned aerial vehicles (UAV) has been shown to be a valuable tool for estimation of crop phenotypic traits, such as plant height, in several species. Spatial information about these phenotypic traits can be used to derive information about other important crop characteristics, like fresh biomass yield, which could not be derived directly from the point clouds. Previous approaches have often only considered single date measurements using a single point cloud derived metric for the respective trait. Furthermore, most of the studies focused on plant species with a homogenous canopy surface. The aim of this study was to assess the applicability of UAV imagery for capturing crop height information of three vegetables (crops eggplant, tomato, and cabbage) with a complex vegetation canopy surface during a complete crop growth cycle to infer biomass. Additionally, the effect of crop development stage on the relationship between estimated crop height and field measured crop height was examined. Our study was conducted in an experimental layout at the University of Agricultural Science in Bengaluru, India. For all the crops, the crop height and the biomass was measured at five dates during one crop growth cycle between February and May 2017 (average crop height was 42.5, 35.5, and 16.0 cm for eggplant, tomato, and cabbage). Using a structure from motion approach, a 3D point cloud was created for each crop and sampling date. In total, 14 crop height metrics were extracted from the point clouds. Machine learning methods were used to create prediction models for vegetable crop height. The study demonstrates that the monitoring of crop height using an UAV during an entire growing period results in detailed and precise estimates of crop height and biomass for all three crops (R 2 ranging from 0.87 to 0.97, bias ranging from −0.66 to 0.45 cm). The effect of crop development stage on the predicted crop height was found to be substantial (e.g., median deviation increased from 1% to 20% for eggplant) influencing the strength and consistency of the relationship between point cloud metrics and crop height estimates and, thus, should be further investigated. Altogether the results of the study demonstrate that point cloud generated from UAV-based RGB imagery can be used to effectively measure vegetable crop biomass in larger areas (relative error = 17.6%, 19.7%, and 15.2% for eggplant, tomato, and cabbage, respectively) with a similar accuracy as biomass prediction models based on measured crop height (relative error = 21.6, 18.8, and 15.2 for eggplant, tomato, and cabbage).

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“…For plant growth estimation, Lati et al, 2013 used hue-invariant transformation for robust green plant segmentation and adopted dynamic programming (DP) to solve scanline optimization for the smoothness constraint. Hue-invariant transformation requires tuning the threshold for different stages of plant growth.…”

Section: Introductionmentioning

confidence: 99%

GPU-based Parallelization of a Sub-pixel High-resolution Stereo Matching Algorithm for High-throughput Biomass Sorghum Phenotyping

Bao¹,

Tang²,

Schnable³

et al. 2015

2015 ASABE International Meeting

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To automate high-throughput phenotyping for infield biomass sorghum morphological traits characterization, a capable 3D vision system that can overcome challenges imposed by field conditions including variable lighting, strong wind and extreme plant height is needed. Among all available 3D sensors, traditional stereo cameras offer a viable solution to obtaining high-resolution 3D point-cloud data with the use of high-accuracy (sub-pixel) stereo matching algorithms, which, however, are inevitably highly computational. This paper reports a GPU-based parallelized implementation of the PatchMatch Stereo algorithm which reconstructs highly slanted leaf and stalk surfaces of sorghum at high speed from high-resolution stereo image pairs. Our algorithm enhanced accuracy and smoothness by using L2 norm for color distance calculation instead of L1 norm and speeded up convergence by testing the plane of the lowest cost within a local window in addition to the original spatial propagation. To better handle textureless regions, after left-right consistency check, the disparity of an occluded pixel is assigned to that of a nearby non-occluded pixel with the most similar pattern. Some of these occluded pixels in textureless region would survive a following left-right consistency check. Therefore more valid pixels would exist in textureless regions for occlusion filling. Accuracy and performance were evaluated on Middlebury datasets as well as our sorghum datasets. It achieved a high ranking in Middlebury table of subpixel precision and revealed subtle details on leaf and stalk surfaces. The output disparity maps were used to estimate stalk diameters of different varieties and growth stages. The results showed high correlation to hand measurement.

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Estimating plant growth parameters using an energy minimization-based stereovision model

Cited by 57 publications

References 24 publications

High Throughput Field Phenotyping of Wheat Plant Height and Growth Rate in Field Plot Trials Using UAV Based Remote Sensing

High Throughput Field Phenotyping of Wheat Plant Height and Growth Rate in Field Plot Trials Using UAV Based Remote Sensing

Estimation of Vegetable Crop Parameter by Multi-temporal UAV-Borne Images

GPU-based Parallelization of a Sub-pixel High-resolution Stereo Matching Algorithm for High-throughput Biomass Sorghum Phenotyping

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