Poppy Crop Height and Capsule Volume Estimation from a Single UAS Flight

Iqbal, Faheem; Lucieer, Arko; Barry, KM; Wells, Reuben

doi:10.3390/rs9070647

Cited by 40 publications

(31 citation statements)

References 53 publications

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“…The results of the present study support the finding of other studies that high resolution multi-temporal crop height information is needed for successful estimation of crop biomass [10,41]. Alternative plant phenotypic traits, such as plant volume, may even yield more accurate predictors for biomass [40]. Thus, future studies should evaluate the further potential of 3D point cloud analysis for estimating key plant phenotypic traits, which can be used as reliable predictors for plant biomass.…”

Section: Discussionsupporting

confidence: 88%

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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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Section: Discussionsupporting

confidence: 88%

“…Crop height is considered as an important indicator for biomass of crops, such as maize [35], barley [12], sorghum [1], and poppy [40]. The predicted vegetable crop height values of the present study show strong and highly significant relationships to the biomass for all three crops ( Figure 5).…”

Section: Discussionmentioning

confidence: 49%

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

et al. 2018

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“…Comparatively, the coefficient of correlation increased when the experiment was conducted at later stage, e.g., Varela et al [45] achieved an R 2 of 0.79 in the abovementioned experiment, but used images taken at flowering stage, which denotes a positive relationship between both variables. Similarly, Iqbal et al [9] achieved a stronger correlation (R 2 > 0.9) working in the late season with a broad range of poppy crop heights (0.5-0.9 m), and Bendig et al [7] reported higher deviations from the regression line for younger plants. Therefore, the correlations obtained in this paper are considered very satisfactory, since the experiments were carried out in the challenging very early crop growth stage.…”

Section: Obia-based Crop Height Estimationsmentioning

confidence: 99%

An Automatic Random Forest-OBIA Algorithm for Early Weed Mapping between and within Crop Rows Using UAV Imagery

et al. 2018

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Accurate and timely detection of weeds between and within crop rows in the early growth stage is considered one of the main challenges in site-specific weed management (SSWM). In this context, a robust and innovative automatic object-based image analysis (OBIA) algorithm was developed on Unmanned Aerial Vehicle (UAV) images to design early post-emergence prescription maps. This novel algorithm makes the major contribution. The OBIA algorithm combined Digital Surface Models (DSMs), orthomosaics and machine learning techniques (Random Forest, RF). OBIA-based plant heights were accurately estimated and used as a feature in the automatic sample selection by the RF classifier; this was the second research contribution. RF randomly selected a class balanced training set, obtained the optimum features values and classified the image, requiring no manual training, making this procedure time-efficient and more accurate, since it removes errors due to a subjective manual task. The ability to discriminate weeds was significantly affected by the imagery spatial resolution and weed density, making the use of higher spatial resolution images more suitable. Finally, prescription maps for in-season post-emergence SSWM were created based on the weed maps-the third research contribution-which could help farmers in decision-making to optimize crop management by rationalization of the herbicide application. The short time involved in the process (image capture and analysis) would allow timely weed control during critical periods, crucial for preventing yield loss.

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“…Table 2) is comparable to the RMSE of derived plant height based on UAV or LiDAR measurements for other plants (e.g., 3.5 cm for LiDAR measurements on wheat [17], 10-13 cm for UAV-derived plant heights on poppy [18], and 10 cm for winter barley and wheat in [27]) and consistent with the measurements based on the 2014 field experiment presented in [16]. The RMSE of the calibration of the CSM-derived plant height vs. dry biomass regression is consistent if slightly higher when compared to the values achieved in [17] (112 g/m 2 for LiDAR derived plant heights, and 115 g/m 2 for SfM-derived plant heights).…”

Section: Discussionmentioning

confidence: 51%

“…The use of plant height as a predictor for deriving crop biomass is well-established, both for UAV-and LiDAR-based data acquisition [2,[17][18][19] as well as with in-situ measurements [20]. In this study, the CSM-derived plant heights, generated from the oblique stereo RGB imagery, are investigated to validate the potential and the robustness of the newly introduced 3D monitoring system to estimate biomass during plant growth.…”

Section: Introductionmentioning

confidence: 99%

Estimating Barley Biomass with Crop Surface Models from Oblique RGB Imagery

Brocks

Bareth

2018

Remote Sensing

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Non-destructive monitoring of crop development is of key interest for agronomy and crop breeding. Crop Surface Models (CSMs) representing the absolute height of the plant canopy are a tool for this. In this study, fresh and dry barley biomass per plot are estimated from CSM-derived plot-wise plant heights. The CSMs are generated in a semi-automated manner using Structure-from-Motion (SfM)/Multi-View-Stereo (MVS) software from oblique stereo RGB images. The images were acquired automatedly from consumer grade smart cameras mounted at an elevated position on a lifting hoist. Fresh and dry biomass were measured destructively at four dates each in 2014 and 2015. We used exponential and simple linear regression based on different calibration/validation splits. Coefficients of determination R 2 between 0.55 and 0.79 and root mean square errors (RMSE) between 97 and 234 g/m 2 are reached for the validation of predicted vs. observed dry biomass, while Willmott's refined index of model performance d r ranges between 0.59 and 0.77. For fresh biomass, R 2 values between 0.34 and 0.61 are reached, with root mean square errors (RMSEs) between 312 and 785 g/m 2 and d r between 0.39 and 0.66. We therefore established the possibility of using this novel low-cost system to estimate barley dry biomass over time.

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Poppy Crop Height and Capsule Volume Estimation from a Single UAS Flight

Cited by 40 publications

References 53 publications

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

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

An Automatic Random Forest-OBIA Algorithm for Early Weed Mapping between and within Crop Rows Using UAV Imagery

Estimating Barley Biomass with Crop Surface Models from Oblique RGB Imagery

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