Systemic Crop Signaling for Automatic Recognition of Transplanted Lettuce and Tomato under Different Levels of Sunlight for Early Season Weed Control
Abstract
:1. Introduction
2. Research Analyses of Rh–B in Lettuce and Tomato
2.1. Fluorescence Signals
2.2. Photostability of Chlorophyll
2.3. Visibility of Rh–B under Full Sunlight Irradiation
2.4. Effects of Light Conditions on Rh–B Photostability
2.5. Effects of Rh–B on Plant Growth
3. Materials and Methods
3.1. Fluorescence Macroscope and Spectroscopic Sensor
3.2. Plant Treatment and Imaging
3.3. Visualization of Fluorescence Signals
4. Discussion
5. Conclusions
Funding
Conflicts of Interest
References
- Fennimore, S.A.; Slaughter, D.C.; Siemens, M.C.; Leon, R.G.; Saber, M.N. Technology for automation of weed control in specialty crops. Weed Technol. 2016, 30, 823–837. [Google Scholar] [CrossRef]
- Mennan, H.; Jabran, K.; Zandstra, B.H.; Pala, F. Non-Chemical Weed Management in Vegetables by Using Cover Crops: A Review. Agronomy 2020, 10, 257. [Google Scholar] [CrossRef] [Green Version]
- Westwood, J.H.; Charudattan, R.; Duke, S.O.; Fennimore, S.A.; Marrone, P.; Slaughter, D.C.; Swanton, C.; Zollinger, R. Weed management in 2050: Perspectives on the future of weed science. Weed Sci. 2018, 66, 275–285. [Google Scholar] [CrossRef] [Green Version]
- Tillett, N.; Hague, T.; Grundy, A.; Dedousis, A. Mechanical within-row weed control for transplanted crops using computer vision. Biosyst. Eng. 2008, 99, 171–178. [Google Scholar] [CrossRef]
- Wortman, S.E.; Forcella, F.; Lambe, D.; Clay, S.A.; Humburg, D. Profitability of abrasive weeding in organic grain and vegetable crops. Renew. Agric. Food Syst. 2020, 35, 215–220. [Google Scholar] [CrossRef]
- Lee, W.S.; Slaughter, D.; Giles, D. Robotic weed control system for tomatoes. Precis. Agric. 1999, 1, 95–113. [Google Scholar] [CrossRef]
- Fennimore, S.A.; Cutulle, M. Robotic weeders can improve weed control options for specialty crops. Pest Manag. Sci. 2019, 75, 1767–1774. [Google Scholar] [CrossRef]
- Su, W.-H. Advanced Machine Learning in Point Spectroscopy, RGB- and Hyperspectral-Imaging for Automatic Discriminations of Crops and Weeds: A Review. Smart Cities 2020, 3, 767–792. [Google Scholar] [CrossRef]
- Slaughter, D.C. The biological engineer: Sensing the difference between crops and weeds. In Automation: The Future of Weed Control in Cropping Systems; Springer: Berlin, Germany, 2014; pp. 71–95. [Google Scholar]
- Thorp, K.; Tian, L. A review on remote sensing of weeds in agriculture. Precis. Agric. 2004, 5, 477–508. [Google Scholar] [CrossRef]
- Su, W.-H.; Bakalis, S.; Sun, D.-W. Potato hierarchical clustering and doneness degree determination by near-infrared (NIR) and attenuated total reflectance mid-infrared (ATR-MIR) spectroscopy. J. Food Meas. Charact. 2019, 13, 1218–1231. [Google Scholar] [CrossRef]
- Slaughter, D.; Lanini, W.; Giles, D. Discriminating weeds from processing tomato plants using visible and near-infrared spectroscopy. Trans. ASAE 2004, 47, 1907. [Google Scholar] [CrossRef]
- Shapira, U.; Herrmann, I.; Karnieli, A.; Bonfil, D.J. Field spectroscopy for weed detection in wheat and chickpea fields. Int. J. Remote Sens. 2013, 34, 6094–6108. [Google Scholar] [CrossRef] [Green Version]
- Su, W.-H.; Sun, D.-W. Advanced analysis of roots and tubers by hyperspectral techniques. In Advances in Food and Nutrition Research; Elsevier: Amsterdam, The Netherlands, 2019; Volume 87, pp. 255–303. [Google Scholar]
- Su, W.-H.; Bakalis, S.; Sun, D.-W. Fingerprinting study of tuber ultimate compressive strength at different microwave drying times using mid-infrared imaging spectroscopy. Dry. Technol. 2019, 37, 1113–1130. [Google Scholar] [CrossRef]
- Su, W.-H.; Sun, D.-W. Evaluation of spectral imaging for inspection of adulterants in terms of common wheat flour, cassava flour and corn flour in organic Avatar wheat (Triticum spp.) flour. J. Food Eng. 2017, 200, 59–69. [Google Scholar] [CrossRef]
- Su, W.-H.; Sun, D.-W. Facilitated wavelength selection and model development for rapid determination of the purity of organic spelt (Triticum spelta L.) flour using spectral imaging. Talanta 2016, 155, 347–357. [Google Scholar] [CrossRef]
- Su, W.-H.; Bakalis, S.; Sun, D.-W. Chemometric determination of time series moisture in both potato and sweet potato tubers during hot air and microwave drying using near/mid-infrared (NIR/MIR) hyperspectral techniques. Dry. Technol. 2019, 38, 1–18. [Google Scholar] [CrossRef]
- Su, W.-H.; Bakalis, S.; Sun, D.-W. Chemometrics in tandem with near infrared (NIR) hyperspectral imaging and Fourier transform mid infrared (FT-MIR) microspectroscopy for variety identification and cooking loss determination of sweet potato. Biosyst. Eng. 2019, 180, 70–86. [Google Scholar] [CrossRef]
- Su, W.-H.; Sun, D.-W. Chemical imaging for measuring the time series variations of tuber dry matter and starch concentration. Comput. Electron. Agric. 2017, 140, 361–373. [Google Scholar] [CrossRef]
- Su, W.-H.; Sun, D.-W. Potential of hyperspectral imaging for visual authentication of sliced organic potatoes from potato and sweet potato tubers and rapid grading of the tubers according to moisture proportion. Comput. Electron. Agric. 2016, 125, 113–124. [Google Scholar] [CrossRef]
- Su, W.-H.; He, H.-J.; Sun, D.-W. Non-destructive and rapid evaluation of staple foods quality by using spectroscopic techniques: A review. Crit. Rev. Food Sci. Nutr. 2017, 57, 1039–1051. [Google Scholar] [CrossRef]
- Su, W.-H.; Sun, D.-W. Comparative assessment of feature-wavelength eligibility for measurement of water binding capacity and specific gravity of tuber using diverse spectral indices stemmed from hyperspectral images. Comput. Electron. Agric. 2016, 130, 69–82. [Google Scholar] [CrossRef]
- Zhang, Y.; Staab, E.S.; Slaughter, D.C.; Giles, D.K.; Downey, D. Automated weed control in organic row crops using hyperspectral species identification and thermal micro-dosing. Crop Prot. 2012, 41, 96–105. [Google Scholar] [CrossRef]
- Zhang, Y.; Slaughter, D.C.; Staab, E.S. Robust hyperspectral vision-based classification for multi-season weed mapping. ISPRS J. Photogramm. Remote Sens. 2012, 69, 65–73. [Google Scholar] [CrossRef]
- Bakhshipour, A.; Jafari, A. Evaluation of support vector machine and artificial neural networks in weed detection using shape features. Comput. Electron. Agric. 2018, 145, 153–160. [Google Scholar] [CrossRef]
- Lee, S.H.; Chan, C.S.; Wilkin, P.; Remagnino, P. Deep-Plant: Plant Identification With Convolutional Neural Networks. In Proceedings of the 2015 IEEE International Conference on Image Processing (ICIP), Quebec, QC, Canada, 27–30 September 2015; pp. 452–456. [Google Scholar]
- Cho, H.; Kim, M.S.; Kim, S.; Lee, H.; Oh, M.; Chung, S.H. Hyperspectral Determination of Fluorescence Wavebands for Multispectral Imaging Detection of Multiple Animal Fecal Species Contaminations on Romaine Lettuce. Food Bioprocess Technol. 2018, 11, 774–784. [Google Scholar] [CrossRef]
- Su, W.H.; Sun, D.W. Multispectral imaging for plant food quality analysis and visualization. Compr. Rev. Food Sci. Food Saf. 2018, 17, 220–239. [Google Scholar] [CrossRef] [Green Version]
- Larsson, M.; Favilla, R.; Strömberg, T. Assessment of advanced glycated end product accumulation in skin using auto fluorescence multispectral imaging. Comput. Biol. Med. 2017, 85, 106–111. [Google Scholar] [CrossRef] [Green Version]
- Han, D.; Yao, H.; Hruska, Z.; Kincaid, R.; Rajasekaran, K.; Bhatnagar, D. Development of High-Speed Dual-Camera System for Batch Screening of Aflatoxin Contamination of Corn Using Multispectral Fluorescence Imaging. Trans. ASABE 2019, 62, 381–391. [Google Scholar] [CrossRef]
- Kennedy, H.; Fennimore, S.A.; Slaughter, D.C.; Nguyen, T.T.; Vuong, V.L.; Raja, R.; Smith, R.F. Crop signal markers facilitate crop detection and weed removal from lettuce and tomato by an intelligent cultivator. Weed Technol. 2019, 34, 1–32. [Google Scholar] [CrossRef]
- Inoue, J.; Chamberlain, K.; Bromilow, R.H. Physicochemical factors affecting the uptake by roots and translocation to shoots of amine bases in barley. Pestic. Sci. 1998, 54, 8–21. [Google Scholar] [CrossRef]
- Hsu, F.C.; Marxmiller, R.L.; Yang, A.Y. Study of root uptake and xylem translocation of cinmethylin and related compounds in detopped soybean roots using a pressure chamber technique. Plant Physiol. 1990, 93, 1573–1578. [Google Scholar] [CrossRef] [Green Version]
- Peterson, C.A.; Emanuel, M.E.; Humphreys, G. Pathway of movement of apoplastic fluorescent dye tracers through the endodermis at the site of secondary root formation in corn (Zea mays) and broad bean (Vicia faba). Can. J. Bot. 1981, 59, 618–625. [Google Scholar] [CrossRef]
- Raja, R.; Slaughter, D.C.; Fennimore, S.A.; Nguyen, T.T.; Vuong, V.L.; Sinha, N.; Tourte, L.; Smith, R.F.; Siemens, M.C. Crop signalling: A novel crop recognition technique for robotic weed control. Biosyst. Eng. 2019, 187, 278–291. [Google Scholar] [CrossRef]
- Su, W.-H.; Fennimore, S.A.; Slaughter, D.C. Computer Vision Technology for Identification of Snap Bean Crops using Systemic Rhodamine B. In Proceedings of the ASABE Annual International Meeting, Boston, MA, USA, 7–10 July 2019; American Society of Agricultural and Biological Engineers: Saint Joseph, MI, USA, 2019; p. 1. [Google Scholar]
- Su, W.-H.; Fennimore, S.A.; Slaughter, D.C. Fluorescence imaging for rapid monitoring of translocation behaviour of systemic markers in snap beans for automated crop/weed discrimination. Biosyst. Eng. 2019, 186, 156–167. [Google Scholar] [CrossRef]
- Wang, Z.; Amirkhani, M.; Avelar, S.A.; Yang, D.; Taylor, A.G. Systemic Uptake of Fluorescent Tracers by Soybean (Glycine max (L.) Merr.) Seed and Seedlings. Agriculture 2020, 10, 248. [Google Scholar] [CrossRef]
- Su, W.-H.; Fennimore, S.A.; Slaughter, D.C. Development of a systemic crop signalling system for automated real-time plant care in vegetable crops. Biosyst. Eng. 2020, 193, 62–74. [Google Scholar] [CrossRef]
- Salanenka, Y.A.; Taylor, A.G. Seedcoat permeability: Uptake and post-germination transport of applied model tracer compounds. HortScience 2011, 46, 622–626. [Google Scholar] [CrossRef]
- Qu, P.; Zhao, J.; Shen, T.; Hidaka, H. TiO2-assisted photodegradation of dyes: A study of two competitive primary processes in the degradation of RB in an aqueous TiO2 colloidal solution. J. Mol. Catal. A Chem. 1998, 129, 257–268. [Google Scholar] [CrossRef]
- Watanabe, T.; Takizawa, T.; Honda, K. Photocatalysis through excitation of adsorbates. 1. Highly efficient N-deethylation of rhodamine B adsorbed to cadmium sulfide. J. Phys. Chem. 1977, 81, 1845–1851. [Google Scholar] [CrossRef]
- Wilhelm, P.; Stephan, D. Photodegradation of rhodamine B in aqueous solution via SiO2@ TiO2 nano-spheres. J. Photochem. Photobiol. A Chem. 2007, 185, 19–25. [Google Scholar] [CrossRef]
- Song, L.; Hennink, E.; Young, I.T.; Tanke, H.J. Photobleaching kinetics of fluorescein in quantitative fluorescence microscopy. Biophys. J. 1995, 68, 2588–2600. [Google Scholar] [CrossRef]
- Su, W.-H.; Slaughter, D.C.; Fennimore, S.A. Non-destructive evaluation of photostability of crop signaling compounds and dose effects on celery vigor for precision plant identification using computer vision. Comput. Electron. Agric. 2020, 168, 105155. [Google Scholar] [CrossRef]
- O’brien, J.; Wilson, I.; Orton, T.; Pognan, F. Investigation of the Alamar Blue (resazurin) fluorescent dye for the assessment of mammalian cell cytotoxicity. Eur. J. Biochem. 2000, 267, 5421–5426. [Google Scholar] [CrossRef] [PubMed]
- Borsuk, A.M.; Brodersen, C.R. The spatial distribution of chlorophyll in leaves. Plant Physiol. 2019, 180, 1406–1417. [Google Scholar] [CrossRef] [Green Version]
- Sadiq, I.M.; Pakrashi, S.; Chandrasekaran, N.; Mukherjee, A. Studies on toxicity of aluminum oxide (Al2O3) nanoparticles to microalgae species: Scenedesmus sp. and Chlorella sp. J. Nanoparticle Res. 2011, 3287–3299. [Google Scholar] [CrossRef]
- Falco, W.; Botero, E.; Falcão, E.; Santiago, E.; Bagnato, V.; Caires, A. In vivo observation of chlorophyll fluorescence quenching induced by gold nanoparticles. J. Photochem. Photobiol. A Chem. 2011, 225, 65–71. [Google Scholar] [CrossRef]
- Rico, C.M.; Hong, J.; Morales, M.I.; Zhao, L.; Barrios, A.C.; Zhang, J.-Y.; Peralta-Videa, J.R.; Gardea-Torresdey, J.L. Effect of cerium oxide nanoparticles on rice: A study involving the antioxidant defense system and in vivo fluorescence imaging. Environ. Sci. Technol. 2013, 47, 5635–5642. [Google Scholar] [CrossRef]
- Lawson, T.; Vialet-Chabrand, S. Chlorophyll fluorescence imaging. In Photosynthesis; Covshoff, S., Ed.; Springer: Berlin, Germany, 2018; pp. 121–140. [Google Scholar]
- Su, W.-H.; Fennimore, S.A.; Slaughter, D.C. Development of a Novel Root Treatment Technique Using Systematic Fluorescent Compounds for Precision Weed Control. In Proceedings of the 2020 ASABE Annual International Virtual Meeting, Omaha, NE, USA, 13–15 July 2020; ASABE: St. Joseph, MI, USA, 2020; p. 1. [Google Scholar]
- Raja, R.; Nguyen, T.T.; Slaughter, D.C.; Fennimore, S.A. Real-time robotic weed knife control system for tomato and lettuce based on geometric appearance of plant labels. Biosyst. Eng. 2020, 194, 152–164. [Google Scholar] [CrossRef]
- Raja, R.; Nguyen, T.T.; Slaughter, D.C.; Fennimore, S.A. Real-time weed-crop classification and localisation technique for robotic weed control in lettuce. Biosyst. Eng. 2020, 192, 257–274. [Google Scholar] [CrossRef]
- Hoebe, R.; Van Oven, C.; Gadella, T.W.; Dhonukshe, P.; Van Noorden, C.; Manders, E. Controlled light-exposure microscopy reduces photobleaching and phototoxicity in fluorescence live-cell imaging. Nat. Biotechnol. 2007, 25, 249–253. [Google Scholar] [CrossRef]
© 2020 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Su, W.-H. Systemic Crop Signaling for Automatic Recognition of Transplanted Lettuce and Tomato under Different Levels of Sunlight for Early Season Weed Control. Challenges 2020, 11, 23. https://doi.org/10.3390/challe11020023
Su W-H. Systemic Crop Signaling for Automatic Recognition of Transplanted Lettuce and Tomato under Different Levels of Sunlight for Early Season Weed Control. Challenges. 2020; 11(2):23. https://doi.org/10.3390/challe11020023
Chicago/Turabian StyleSu, Wen-Hao. 2020. "Systemic Crop Signaling for Automatic Recognition of Transplanted Lettuce and Tomato under Different Levels of Sunlight for Early Season Weed Control" Challenges 11, no. 2: 23. https://doi.org/10.3390/challe11020023
APA StyleSu, W. -H. (2020). Systemic Crop Signaling for Automatic Recognition of Transplanted Lettuce and Tomato under Different Levels of Sunlight for Early Season Weed Control. Challenges, 11(2), 23. https://doi.org/10.3390/challe11020023