# 3-D Image-Driven Morphological Crop Analysis: A Novel Method for Detection of Sunflower Broomrape Initial Subsoil Parasitism

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Image Acquisition and Analysis

#### 2.2. Plant Segmentation and Parameter Extraction

**t**

_{i}at point

**x**

_{i}, is the sum of vectors between a point and its neighbor points:

**x**

_{i}. The first-order tensor facilitates the evaluation of the distribution of points around

**x**

_{i}. A second-order tensor is the sum of the outer products:

**T**

_{i}is the second-order tensor at point i,

**x**

_{i}is the point for which the tensor is computed, and

**x**

_{j}is a point in its neighborhood, σ. The outer product form Equation (1) establishes a symmetric positive-semidefinite matrix whose spectral decomposition into its eigenvectors and eigenvalues can be written as:

**Λ**= diag(λ

_{1}λ

_{2}λ

_{3}), a non-negative diagonal matrix composed of its eigenvalues, and

**V**= [

**v**

_{1}

**v**

_{2}

**v**

_{3}], an orthonormal matrix formed by concatenation of the eigenvectors. Because

**V**preserves lengths and angles (isometry), it is referred to as the tensor orientation, while the λ

_{i}terms are the strengths of the respective orientation. Equation (3) can be rearranged as:

_{1}> 0, and λ

_{2}= λ

_{3}= 0. In a surface-like distribution, λ

_{1}, λ

_{2}> 0, while λ

_{3}= 0. The differences between the first two eigenvalues in such an arrangement provide information about the uniformity of the point distribution. The third eigenvector λ

_{3}reflects the deviation from planarity, and analysis shows that in such a distribution, this value rarely exceeds 10% of the magnitude of the first eigenvector. Therefore, it is possible to perform a classification that is based on a straightforward analysis of the first two eigenvalues, while ensuring that the third one is significantly smaller. For the leaf parts that can be related as surface-like features, points should be evenly distributed on the dominant plane-of-projection, and therefore, we assume λ

_{1}≅ λ

_{2}. For stem-related points, a noticeable variation between the first and second eigenvalues is expected, which indicates an elongated axial form.

_{2}value as compared to the standard stem points (Figure 5D). To define the location of the internodes, we first detected regions along the stem with high λ

_{2}value and then computed the centroid of this set of points and considered it as the internode location.

#### 2.3. Minirhizotron Experiments

#### 2.4. Statistical Analysis

## 3. Results

#### 3.1. O. Cumana Parasitism Dynamics: Minirhizotron Experiments

^{2}= 0.98) between the number of O. cumana attachments and thermal time (Figure 7 and Table 1). The parasitism pattern of O. cumana on sunflower was similar to that observed by other researchers [2,6].

#### 3.2. 3-D Reconstruction and Internode Estimation

^{2}= 0.99, slope (a) = 1.02 (Figure 9)). Furthermore, the RMSE value was below 7 mm, which was ~3.5% error, indicating that the segmentation model provided accurate morphological estimates at the single-organ level.

#### 3.3. Morphological Analysis for O. Cumana Detection

^{3}for the control and infected plants, respectively. The volume difference between infected and control plants was the highest at the end of the study, 125% (Figure 10C).

## 4. Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

- Lati, R.N.; Filin, S.; Eizenberg, H. Plant growth parameter estimation from sparse 3D reconstruction based on highly-textured feature points. Precis. Agric.
**2013**, 14, 586–605. [Google Scholar] [CrossRef] - Ephrath, J.E.; Eizenberg, H. Quantification of the dynamics of Orobanche cumana and Phelipanche aegyptiaca parasitism in confectionery sunflower. Weed Res.
**2010**, 50, 140–152. [Google Scholar] [CrossRef] - Cochavi, A.; Achdari, G.; Smirnov, Y.; Rubin, B.; Eizenberg, H. Egyptian broomrape (Phelipanche aegyptiaca) management in carrot under field conditions. Weed Technol.
**2015**, 29, 519–528. [Google Scholar] [CrossRef] - Aly, R.; Goldwasser, Y.; Eizenberg, H.; Hershenhorn, J.; Golan, S.; Kleifeld, Y. Broomrape (Orobanche cumana) control in sunflower (Helianthus annuus) with imazapic. Weed Technol.
**2009**, 15, 306–309. [Google Scholar] [CrossRef] - Aly, R. Conventional and biotechnological approaches for control of parasitic weeds. In Vitro Cell. Dev. Biol. Plant
**2007**, 43, 304–317. [Google Scholar] [CrossRef] - Eizenberg, H.; Hershenhorn, J.; Achdari, G.; Ephrath, J.E. A thermal time model for predicting parasitism of Orobanche cumana in irrigated sunflower-field validation. Field Crop. Res.
**2012**, 137, 49–55. [Google Scholar] [CrossRef] - Eizenberg, H.; Aly, R.; Cohen, Y. Technologies for smart chemical control of broomrape (Orobanche spp. and Phelipanche spp.). Weed Sci.
**2012**, 60, 316–323. [Google Scholar] [CrossRef] - Bajwa, A.A.; Mahajan, G.; Chauhan, B.S. Nonconventional weed management strategies for modern agriculture. Weed Sci.
**2015**, 63, 723–747. [Google Scholar] [CrossRef] - Eizenberg, H.; Hershenhorn, J.; Ephrath, J.H.; Kanampiu, F. Chemical control. In Parasitic Orobanchaceae; Joel, D.M., Gressel, J., Musselman, L.J., Eds.; Springer: Berlin/Heidelberg, Germany, 2013; pp. 415–432. [Google Scholar]
- Cochavi, A.; Rapaport, T.; Gendler, T.; Karnieli, A.; Eizenberg, H.; Rachmilevitch, S.; Ephrath, J.E. Recognition of orobanche cumana below-ground parasitism through physiological and hyper spectral measurements in sunflower (Helianthus annuus L.). Front. Plant Sci.
**2017**, 8, 909. [Google Scholar] [CrossRef] [PubMed] - Ortiz-Bustos, C.M.; Pérez-Bueno, M.L.; Barón, M.; Molinero-Ruiz, L. Fluorescence imaging in the red and far-Rrd region during growth of sunflower plantlets. diagnosis of the early infection by the parasite Orobanche cumana. Front. Plant Sci.
**2016**, 7, 884. [Google Scholar] [CrossRef] - Chaivivatrakul, S.; Tang, L.; Dailey, M.N.; Nakarmi, A.D. Automatic morphological trait characterization for corn plants via 3D holographic reconstruction. Comput. Electron. Agric.
**2014**, 109, 109–123. [Google Scholar] [CrossRef] - Rose, C.J.; Paulus, S.; Kuhlmann, H. Accuracy analysis of a multi-view stereo approach for phenotyping of tomato plants at the organ level. Sensors
**2015**, 15, 9651–9665. [Google Scholar] [CrossRef] - Paproki, A.; Sirault, X.; Berry, S.; Furbank, R.; Fripp, J. A novel mesh processing based technique for 3D plant analysis. BMC Plant Biol.
**2012**, 12, 63. [Google Scholar] [CrossRef] - Paulus, S.; Dupuis, J.; Mahlein, A.-K.; Kuhlmann, H. Surface feature based classification of plant organs from 3D laserscanned point clouds for plant phenotyping. BMC Bioinform.
**2013**, 14, 238. [Google Scholar] [CrossRef] - McCormick, R.F.; Truong, S.K.; Mullet, J.E. 3D sorghum reconstructions from depth images identify QTL regulating shoot architecture. Plant Physiol.
**2016**, 172, 823–834. [Google Scholar] [CrossRef] - Nguyen, T.T.; Slaughter, C.D.; Max, N.; Maloof, N.J.; Sinha, N. Structured light-based 3D reconstruction system for plants. Sensors
**2015**, 15, 18587–18612. [Google Scholar] [CrossRef] - Salas Fernandez, M.G.; Bao, Y.; Tang, L.; Schnable, P.S. A high-throughput, field-based phenotyping technology for tall biomass crops. Plant Physiol.
**2017**, 174, 2008–2022. [Google Scholar] [CrossRef] [PubMed] - Piron, A.; Leemans, V.; Lebeau, F.; Destain, M.F. Improving in-row weed detection in multispectral stereoscopic images. Comput. Electron. Agric.
**2009**, 69, 73–79. [Google Scholar] [CrossRef] [Green Version] - Telem, G.; Filin, S. Photogrammetric modeling of the relative orientation in underwater environments. ISPRS J. Photogramm. Remote Sens.
**2013**, 86, 150–156. [Google Scholar] [CrossRef] - Hosoi, F.; Omasa, K. Voxel-based 3-D modeling of individual trees for estimating leaf area density using high-resolution portable scanning lidar. IEEE Trans. Geosci. Remote Sens.
**2006**, 44, 3610–3618. [Google Scholar] [CrossRef] - Elnashef, B.; Filin, S.; Lati, R.N. Tensor-based classification and segmentation of three-dimensional point clouds for organ-level plant phenotyping and growth analysis. Comput. Electron. Agric.
**2019**, 156, 51–61. [Google Scholar] [CrossRef] - Eizenberg, H.; Shtienberg, D.; Silberbush, M.; Ephrath, J.E. A new method for in-situ monitoring of the underground development of Orobanche cumana in sunflower (Helianthus annuus) with a mini-rhizotron. Ann. Bot.
**2005**, 96, 1137–1140. [Google Scholar] [CrossRef] [PubMed] - McMaster, G.S.; Wilhelm, W.W. Growing degree-days: One equation, two interpretations. Agric. For. Meteorol.
**1997**, 87, 291–300. [Google Scholar] [CrossRef] - Simmons, A.M.; Yeargan, K.V.; Godfrey, L.D. Ovipositional sites of the potato leafhopper (Homoptera: Cicadellidae) on vegetative stage soybean plants. Environ. Entomol.
**1985**, 14, 165–169. [Google Scholar] [CrossRef] - Peteinatos, G.G.; Weis, M.; Andújar, D.; Rueda Ayala, V.; Gerhards, R. Potential use of ground-based sensor technologies for weed detection. Pest Manag. Sci.
**2014**, 70, 190–199. [Google Scholar] [CrossRef] [PubMed] - Hatfield, J.L.; Prueger, J.H. Temperature extremes: Effect on plant growth and development. Weather Clim. Extrem.
**2015**, 10, 4–10. [Google Scholar] [CrossRef] [Green Version] - Liu, R.; Dai, M.; Wu, X.; Li, M.; Liu, X. Suppression of the root-knot nematode [Meloidogyne incognita (Kofoid & White) Chitwood] on tomato by dual inoculation with arbuscular mycorrhizal fungi and plant growth-promoting rhizobacteria. Mycorrhiza
**2012**, 22, 289–296. [Google Scholar] [CrossRef] [PubMed] - Yamamoto, K.; Guo, W.; Ninomiya, S. Node detection and internode length estimation of tomato seedlings based on image analysis and machine learning. Sensors
**2016**, 16, 1044. [Google Scholar] [CrossRef] - Brichet, N.; Fournier, C.; Turc, O.; Strauss, O.; Artzet, S.; Pradal, C.; Welcker, C.; Tardieu, F.; Cabrera-Bosquet, L. A robot-assisted imaging pipeline for tracking the growths of maize ear and silks in a high-throughput phenotyping platform. Plant Methods
**2017**, 13, 96. [Google Scholar] [CrossRef] - Surov, T.; Aviv, D.; Aly, R.; Joel, D.M.; Goldman-Guez, T.; Gressel, J. Generation of transgenic asulam-resistant potatoes to facilitate eradication of parasitic broomrapes (Orobanche spp.), with the sul gene as the selectable marker. Theor. Appl. Genet.
**1998**, 96, 132–137. [Google Scholar] [CrossRef] - Tan, S.; Evans, R.R.; Dahmer, M.L.; Singh, B.K.; Shaner, D.L. Imidazolinone-tolerant crops: History, current status and future. Pest Manag. Sci.
**2005**, 61, 246–257. [Google Scholar] [CrossRef] - Lin, Y. LiDAR: An important tool for next-generation phenotyping technology of high potential for plant phenomics? Comput. Electron. Agric.
**2015**, 119, 61–73. [Google Scholar] [CrossRef] - Long, D.S.; McCallum, J.D. Mapping straw yield using on-combine light detection and ranging (lidar). Int. J. Remote Sens.
**2013**, 34, 6121–6134. [Google Scholar] [CrossRef] - Zhang, L.; Grift, T.E. A LIDAR-based crop height measurement system for Miscanthus giganteus. Comput. Electron. Agric.
**2012**, 85, 70–76. [Google Scholar] [CrossRef] - Fricke, T.; Wachendorf, M. Combining ultrasonic sward height and spectral signatures to assess the biomass of legume-grass swards. Comput. Electron. Agric.
**2013**, 99, 236–247. [Google Scholar] [CrossRef] - Pittman, J.J.; Arnall, D.B.; Interrante, S.M.; Moffet, C.A.; Butler, T.J. Estimation of biomass and canopy height in Bermudagrass, Alfalfa, and wheat using ultrasonic, laser, and spectral sensors. Sensors
**2015**, 15, 2920–2943. [Google Scholar] [CrossRef] - Paulus, S.; Dupuis, J.; Riedel, S.; Kuhlmann, H. Automated analysis of barley organs using 3D laser scanning: An approach for high throughput phenotyping. Sensors
**2014**, 14, 12670–12686. [Google Scholar] [CrossRef] - Duan, T.; Chapman, S.C.; Holland, E.; Rebetzke, G.J.; Guo, Y.; Zheng, B. Dynamic quantification of canopy structure to characterize early plant vigour in wheat genotypes. J. Exp. Bot.
**2016**, 67, 4523–4534. [Google Scholar] [CrossRef] [Green Version] - Paulus, S.; Schumann, H.; Kuhlmann, H.; Léon, J. High-precision laser scanning system for capturing 3D plant architecture and analysing growth of cereal plants. Biosyst. Eng.
**2014**, 121, 1–11. [Google Scholar] [CrossRef] - Nguyen, T.T.; Slaughter, D.C.; Maloof, J.N.; Sinha, N. Plant phenotyping using multi-view stereo vision with structured lights. In Proceedings of the Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping (SPIE 9866), Baltimore, MD, USA, 17–21 April 2016; p. 986608. [Google Scholar] [CrossRef]
- Oveisi, M.; Yousefi, A.R.; Gonzalez-Andujar, J.L. Spatial distribution and temporal stability of crenate broomrape (Orobanche crenata Forsk) in faba bean (Vicia faba L.): A long-term study at two localities. Crop Prot.
**2010**, 29, 717–720. [Google Scholar] [CrossRef] - Jay, S.; Rabatel, G.; Hadoux, X.; Moura, D.; Gorretta, N. In-field crop row phenotyping from 3D modeling performed using structure from motion. Comput. Electron. Agric.
**2015**, 110, 70–77. [Google Scholar] [CrossRef] - Jiang, Y.; Li, C.; Paterson, A.H. High throughput phenotyping of cotton plant height using depth images under field conditions. Comput. Electron. Agric.
**2016**, 130, 57–68. [Google Scholar] [CrossRef] - Kise, M.; Zhang, Q. Development of a stereovision sensing system for 3D crop row structure mapping and tractor guidance. Biosyst. Eng.
**2008**, 101, 191–198. [Google Scholar] [CrossRef] - Andújar, D.; Fernández-Quintanilla, C.; Dorado, J. Matching the best viewing angle in depth cameras for biomass estimation based on poplar seedling geometry. Sensors
**2015**, 15, 12999–13011. [Google Scholar] [CrossRef] - Nakarmi, A.D.; Tang, L. Automatic inter-plant spacing sensing at early growth stages using a 3D vision sensor. Comput. Electron. Agric.
**2012**, 82, 23–31. [Google Scholar] [CrossRef]

**Figure 2.**Imaging setup and 3-D reconstruction. Camera positions (‘pyramid’ apices), orientations and imaged areas of the acquired data (

**A**) and the 3-D reconstructed model of the entire scene before the pre-processing stage (

**B**).

**Figure 3.**Plant segmentation. The reconstructed 3-D model of the sunflower plant after the pre-processing stage (

**A**), segmented leaf-related points (

**B**) and segmented stem-related points (

**C**).

**Figure 4.**Visualization of a 3-D second-order tensor. The first term in Equation (3) corresponds to a degenerate elongated ellipsoid (linear tensor), with

**v**

_{1}as its curve normal. The second term corresponds to a circular disc (plate tensor) with

**v**

_{3}as the surface normal. The third term corresponds to a structure with no preference of orientation (sphere tensor). The stick tensor is given as $({\lambda}_{1}-{\lambda}_{2}){v}_{1}{v}_{1}^{T}$, the plate tensor is given as $({\lambda}_{2}-{\lambda}_{3})({v}_{1}{v}_{1}^{T}+{v}_{2}{v}_{2}^{T})$, and the sphere tensor is given as ${\lambda}_{3}({v}_{1}{v}_{1}^{T}+{v}_{2}{v}_{2}^{T}+{v}_{3}{v}_{3}^{T})$.

**Figure 5.**Demonstration of the internode point extraction. Originally acquired image used for the plant 3-D model (

**A**) and results of the second-order tensor analysis (

**B**). Extraction of the stem-related points (

**C**), and how analysis of the λ

_{2}values allowed detection of the exact location of the nodes location (

**D**). Note how the junction area yielded points with high values of λ

_{2}(yellow), whereas the rest of the stem points were characterized by small values of λ

_{2}(red).

**Figure 6.**Attachments of O. cumana on sunflower plants under controlled conditions, imaged at 10 cm depth by the minirhizotron system, on four consecutive dates, 550, 750, 940 and 1120 GDD after sunflower planting.

**Figure 7.**Attachment dynamics (attachments per tube) of O. cumana on sunflower plants relative to thermal time measured in GDD.

**Figure 8.**Results of the internode detection algorithm applied on the stem-related points after the segmentation process. Orange dots represent the detected internode points. Setting their position on the stem allowed for estimation of the internode length.

**Figure 9.**Linear regression between first internode length values extracted from the 3-D model and those measured manually, y = 1.02x − 0.38 (R

^{2}= 0.99, RMSE = 0.68).

**Figure 10.**Height (A), first internode length (B), volume (C) and width (D) dynamics of O. cumana-infected (■) versus control (♦) sunflower plants. Vertical bars represent standard error of the mean values. * indicates a significant difference as determined by Tukey-HSD test (n = 5; p ≤ 0.05).

**Table 1.**Attachment dynamics (attachments per tube) evaluated by the minirhizotron system of O. cumana on sunflower plants relative to thermal time, measured in GDD.

Parameter | Estimate | SE |
---|---|---|

a | 6.62 | 0.42 |

x_{0} | 1000.18 | 34.69 |

b | −6.37 | 1.17 |

R^{2} | 0.98 | |

p | <0.0001 | |

RMSE | 0.32 |

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**MDPI and ACS Style**

Lati, R.N.; Filin, S.; Elnashef, B.; Eizenberg, H.
3-D Image-Driven Morphological Crop Analysis: A Novel Method for Detection of Sunflower Broomrape Initial Subsoil Parasitism. *Sensors* **2019**, *19*, 1569.
https://doi.org/10.3390/s19071569

**AMA Style**

Lati RN, Filin S, Elnashef B, Eizenberg H.
3-D Image-Driven Morphological Crop Analysis: A Novel Method for Detection of Sunflower Broomrape Initial Subsoil Parasitism. *Sensors*. 2019; 19(7):1569.
https://doi.org/10.3390/s19071569

**Chicago/Turabian Style**

Lati, Ran Nisim, Sagi Filin, Bashar Elnashef, and Hanan Eizenberg.
2019. "3-D Image-Driven Morphological Crop Analysis: A Novel Method for Detection of Sunflower Broomrape Initial Subsoil Parasitism" *Sensors* 19, no. 7: 1569.
https://doi.org/10.3390/s19071569