Application of terahertz Gouy phase shift from curved surfaces for estimation of crop yield

Federici, John F.; Wample, Robert L.; Rodriguez, David; Mukherjee, S.

doi:10.1364/ao.48.001382

Cited by 26 publications

(19 citation statements)

References 14 publications

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“…Information obtained from multispectral images has been used to forecast yields with good results but is limited to vineyards with uniformity requirements (Martinez-Casasnovas and Bordes [6]). A proof of concept study by Federici et al [5] has shown that terahertz imaging can detect the curved surfaces of grapes and also has the potential to detect these through occluding thin canopy. The challenge for this approach is to achieve fast scan rates to be able to deploy the scanner on a mobile platform.…”

Section: Related Workmentioning

confidence: 99%

Modeling and Calibrating Visual Yield Estimates in Vineyards

Nuske

Gupta

Narasimhan

et al. 2013

Springer Tracts in Advanced Robotics

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Accurate yield estimates are of great value to vineyard growers to make informed management decisions such as crop thinning, shoot thinning, irrigation and nutrient delivery, preparing for harvest and planning for market. Current methods are labor intensive because they involve destructive hand sampling and are practically too sparse to capture spatial variability in large vineyard blocks. Here we report on an approach to predict vineyard yield automatically and non-destructively using images collected from vehicles driving along vineyard rows. Computer vision algorithms are applied to detect grape berries in images that have been registered together to generate high-resolution estimates. We propose an underlying model relating image measurements to harvest yield and study practical approaches to calibrate the two. We report on results on datasets of several hundred vines collected both early and in the middle of the growing season. We find that it is possible to estimate yield to within 4% using calibration data from prior harvest data and 3% using calibration data from destructive hand samples at the time of imaging.

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Section: Related Workmentioning

confidence: 99%

Modeling and Calibrating Visual Yield Estimates in Vineyards

Nuske

Gupta

Narasimhan

et al. 2013

Springer Tracts in Advanced Robotics

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“…1. The few existing methods for detecting crop in vineyards, have been restricted to the laboratory [4] or have relied on color contrast [3] and are therefore not applicable for detecting crop over a similarly colored background of leaves. Lack of color contrast is an important issue that occurs in the white-grape varieties and all the grape varieties prior to véraison (the onset of color development).…”

Section: Introductionmentioning

confidence: 99%

Yield estimation in vineyards by visual grape detection

Nuske¹,

Achar²,

Bates³

et al. 2011

2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

119

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Abstract-The harvest yield in vineyards can vary significantly from year to year and also spatially within plots due to variations in climate, soil conditions and pests. Fine grained knowledge of crop yields would allow viticulturists to better manage their vineyards. The current industry practice for yield prediction is destructive, expensive and spatially sparse -small samples are taken from the vineyards during the growing season and extrapolated to determine overall yield. We present an automated method that uses computer vision to identify and count grape berries. These counts are used to generate per vine estimates of crop yield. Both shape and visual texture are used to detect berries. We demonstrate detection of green berries against a green leaf background. We present crop yield estimation results, with the actual harvest yield as groundtruth for 200 vines (over 450 meters) of two different grape varieties. We calibrate our berry count to yield and find that we can predict yield to within 9.8% of actual crop weight.

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“…The importance of such anomalous phase features can be found in various physical problems, for instance, they contribute defining the resonance frequencies of laser cavities [44,45], they can be used for optical trapping [46][47][48], and for higher harmonic generations [49][50][51]. Such phase anomalies are a general wave phenomenon and were found not just in optical waves, but also in acoustic waves [52], microwaves [38,53], and terahertz waves [54][55][56]. In optical waves, various classes of beams exhibit the Gouy phase.…”

Section: Introductionmentioning

confidence: 99%

Phase anomalies in Talbot light carpets of self-images

Kim¹,

Scharf²,

Menzel³

et al. 2013

Opt. Express

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An interesting feature of light fields is a phase anomaly, which occurs on the optical axis when light is converging as in a focal spot. Since in Talbot images the light is periodically confined in both transverse and axial directions, it remains an open question whether at all and to which extent the phase in the Talbot images sustains an analogous phase anomaly. Here, we investigate experimentally and theoretically the anomalous phase behavior of Talbot images that emerge from a 1D amplitude grating with a period only slightly larger than the illumination wavelength. Talbot light carpets are observed close to the grating. We concisely show that the phase in each of the Talbot images possesses an anomalous axial shift. We show that this phase shift is analogous to a Gouy phase of a converging wave and occurs due to the periodic light confinement caused by the interference of various diffraction orders. Longitudinal-differential interferometry is used to directly demonstrate the axial phase shifts by comparing Talbot images phase maps to a plane wave. Supporting simulations based on rigorous diffraction theory are used to explore the effect numerically. Numerical and experimental results are in excellent agreement. We discover that the phase anomaly, i.e., the difference of the phase of the field behind the grating to the phase of a referential plane wave, is an increasing function with respect to the propagation distance. We also observe within one Talbot length an irregular wavefront spacing that causes a deviation from the linear slope of the phase anomaly. We complement our work by providing an analytical model that explains these features of the axial phase shift. References and links1. F. Talbot, "Facts relating to optical science. No. IV," Philos. Mag. 9, 401-407 (1836). 2. L. Rayleigh, "On copying diffraction gratings and some phenomena connected therewith," Philos. Mag. 11(67), 196-205 (1881). 3. J. C. Bhattacharya, "Measurement of the refractive index using the Talbot effect and a moire technique," Appl.Opt. 28(13), 2600-2604 (1989). 4. G. Spagnolo, D. Ambrosini, and D. Paoletti, "Displacement measurement using the Talbot effect with a Ronchi grating," J. Opt. Soc. A 4(6), S376-S380 (2002). 5. J. R. Leger, M. L. Scott, and W. B. Veldkamp, "Coherent addition of AlGaAs lasers using microlenses and diffractive coupling," Appl. Phys. Lett. 52(21), 1771-1773 (1988). 6. J. R. Leger, "Lateral mode control of an AlGaAs laser array in a Talbot cavity," Appl. Phys. Lett. 55(4), 334-336 (1989 416-419 (1973). 11. E. Bonet, J. Ojeda-Castañeda, and A. Pons, "Imagesynthesis using the Laueffect," Opt. Commun. 81(5), 285-290 (1991). 12. J. C. Barreiro, P. Andrés, J. Ojeda-Castañeda, and J. Lancis, "Multiple incoherent 2D optical correlator," Opt.Commun. 84(5-6), 237-241 (1991). 13. R. F. Edgar, "The Fresnel diffraction images of periodic structures," J. Mod. Opt. 16, 281-287 (1969). 14. J. T. Winthrop and C. R. Worthington, "Theory of Fresnel images. I. Plane periodic objects in monochromatic light," J. Opt...

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Application of terahertz Gouy phase shift from curved surfaces for estimation of crop yield

Cited by 26 publications

References 14 publications

Modeling and Calibrating Visual Yield Estimates in Vineyards

Modeling and Calibrating Visual Yield Estimates in Vineyards

Yield estimation in vineyards by visual grape detection

Phase anomalies in Talbot light carpets of self-images

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