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Design and implementation of a computer vision-guided greenhouse crop diagnostics system

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Abstract

An autonomous computer vision-guided plant sensing and monitoring system was designed and constructed to continuously monitor temporal, morphological, and spectral features of lettuce crop growing in a nutrient film technique (NFT) hydroponics system. The system consisted of five main components including (1) a stepper motor-driven camera positioning system, (2) an image acquisition system, (3) a data logger monitoring root and aerial zone of the growing environment, (4) a dynamic SQL database module for data storage, and (5) a host computer running the collection, processing, storage, and analysis functions. Panoramic canopy images were dynamically created from the images collected by color, near-infrared (NIR) and thermal cameras. From these three images, the crop features were registered such that a single extracted crop (or a crop canopy) contained information from each layer. The extracted features were color (red–green–blue, hue-saturation-luminance, and color brightness), texture (entropy, energy, contrast, and homogeneity), Normalized Difference Vegetative Index (NDVI) (as well as other similar indices from the color and NIR channels), thermal (plant and canopy temperature), plant morphology (top projected plant and canopy area), and temporal changes of all these variables. The computer vision-guided system was able to extract these plant features and stored them into a database autonomously. This paper introduces the engineering design and system components in detail. The system’s capability is illustrated with a one-day sample of the lettuce plants growing in the NFT system, presenting the temporal changes of three key crop features extracted, and identification of a stress level and locality detection as example applications.

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References

  1. Helmer, T., Ehret, D.L., Bittman, S.: CropAssist, an automated system for direct measurement of greenhouse tomato growth and water use. Comput. Electron. Agric. 48, 198–215 (2005)

    Article  Google Scholar 

  2. Fitz-Rodriguez, E., Giacomelli, G.A.: Yield prediction and growth mode characterization of greenhouse tomatoes with Neural Networks and Fuzzy Logic. Trans. ASABE 52(6), 2115–2128 (2009)

    Article  Google Scholar 

  3. Kacira, M., Sase, S., Okushima, L., Ling, P.P.: Plant response based sensing and control strategies in sustainable greenhouse production. J. Agric. Meteorol. Japan 61(1), 15–22 (2005)

    Article  Google Scholar 

  4. Seginer, I., Elster, R.T., Goodrum, J.W., Rieger, M.W.: Plant wilt detection by computer-vision tracking of leaf tips. Trans. ASAE 35(5), 1563–1567 (1992)

    Article  Google Scholar 

  5. Meyer, G.E., Troyer, W.W., Fitzgerald, J.B., Paparozzi, E.T.: Leaf nitrogen analysis of poinsettia (Euphorbia Pulcherrima Will D.) using spectral properties in natural and controlled lighting. Appl. Eng. Agric. 8(5), 715–722 (1992)

    Article  Google Scholar 

  6. Shimizu, H., Heins, R.D.: Computer-vision-based system for plant growth analysis. Trans. ASAE 38(3), 959–964 (1995)

  7. Revollon, P., Chasseriaux, G., Riviere, L. M., Gardet, R.: The use of image processing for tracking the morphological modification of Forsythia following an interruption of watering. In: Proceedings of International Conference on Agricultural Engineering, Oslo, pp. 872–873 (1998)

  8. Hetzroni, A., Miles, G.E., Engel, B.A., Hammer, P.A., Latin, R.X.: Machine vision monitoring of plant health. Adv. Space Res. 14(11), 203–212 (1994)

    Article  Google Scholar 

  9. Ling, P.P., Giacomelli, G.A., Russell, T.P.: Monitoring of plant development in controlled environment with machine vision. Adv. Space Res. 18(4–5), 101–112 (1996)

    Article  Google Scholar 

  10. Kurata, K., Yan, J.: Water stress estimation of tomato canopy based on machine vision. Acta Hortic. 440, 389–394 (1996)

    Google Scholar 

  11. Murase, H., Nishiura, Y., Mitani, K.: Environmental control strategies based on plant responses using intelligent machine vision technique. Comput. Electron. Agric. 18, 137–148 (1997)

    Article  Google Scholar 

  12. Kacira, M., Ling, P.P., Short, T.H.: Machine vision extracted plant movement for early detection of plant water stress. Trans. ASAE 45(4), 1147–1153 (2002)

    Article  Google Scholar 

  13. Leinonen, I., Jones, H.G.: Combining thermal and visible imagery for estimating canopy temperature and identifying plant stress. J. Exp. Bot. 55, 1423–1431 (2004)

    Article  Google Scholar 

  14. Ushada, D., Murase, H., Fukuda, H.: Non-destructive sensing and its inverse model for canopy parameters using texture analysis and artificial neural network. Comput. Electron. Agric. 57, 149–165 (2007)

    Article  Google Scholar 

  15. Lee, W.S., Alchanatisb, W., Yang, C., Hirafuji, M., Moshoue, D., Li, C.: Sensing technologies for precision specialty crop production. Comput. Electron. Agric. 74, 2–33 (2010)

    Article  Google Scholar 

  16. Cobb, J., Genevieve, D.: Next-generation phenotyping: requirements and strategies for enhancing our understanding of genotype-phenotype relationships and its relevance to crop improvement. Theor. Appl. Genet. 126, 867–887 (2013)

    Article  Google Scholar 

  17. Sturm, P., Maybank, S.: On plane-based camera calibration: a general algorithm, singularities, applications. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 432–437 (1999)

  18. Bezryadin, S., Bourov, P., Ilinih. D.: Brightness calculation in digital image processing. In: Proceedings of International Symposium on Technologies for Digital Fulfillmen, Las Vegas (2007)

  19. Jain, R., Kasturi, R., Schunck, B.J.: Machine Vision. McGraw-Hill, New York (1995)

    Google Scholar 

  20. Zheng, C., Sun, D.W., Zheng, L.: Recent applications of image texture for evaluation of food qualities—a review. Trends Food Sci. Technol. 17, 113–128 (2006)

    Article  Google Scholar 

  21. Penuelas, J., Filella, I.: Visible and near-infrared reflectance techniques for diagnosing plant physiological status. Trends Plant Sci. 3, 151–156 (1998)

    Article  Google Scholar 

  22. Gamon, J.A., Field, C.B., Goulden, M.L., Griffin, K.L., Hartley, A.E., Joel, G., Peñuelas, J., Valentini, R.: Relationships between NDVI, canopy structure, and photosynthesis in three Californian vegetation types. Ecol. Appl. 5(1), 28–41 (1995)

    Article  Google Scholar 

  23. El-Shikha, D., Waller, P., Hunsaker, D., Clarke, T., Barnes, E.: Ground-based remote sensing for assessing water and nitrogen status of broccoli. Agric. Water Manag. 92, 183–193 (2007)

    Article  Google Scholar 

  24. Ryu, Y., Baldocchi, D., Verfaillie, J., Ma, S., Falk, M., Ruiz-Mercado, I., Hehn, T., Sonnentag, O.: Testing the performance of a novel spectral reflectance sensor, built with light emitting diodes (LEDs), to monitor ecosystem metabolism, structure and function. Agric. For. Meteorol. 150, 1597–1606 (2010)

    Article  Google Scholar 

  25. Hetherington, A.M.: Plant physiology: spreading a drought warning. Curr. Biol. 8(25), 911–913 (1998)

    Article  Google Scholar 

  26. Jones, H.G.: Use of infrared thermometry for estimation of stomatal conductance as a possible aid to irrigation scheduling. Agric. For. Meteorol. 95, 139–149 (1999)

    Article  Google Scholar 

  27. Jones, H.G., Stoll, M., Santos, T., Sousa, C., Chaves, M., Grant, O.: Use of infrared thermography for monitoring stomatal closure in the field: application of grapevine. J. Exp. Bot. 53(378), 2249–2260 (2002)

    Article  Google Scholar 

  28. Cohen, Y., Alchanatis, V., Meron, M., Saranga, Y., Tsipris, J.: Estimation of leaf water potential by thermal imagery and spatial analysis. J. Exp. Bot. 56(417), 1843–1852 (2005)

    Article  Google Scholar 

  29. Ling, P.P., Ruzhitsky, V.N.: Machine vision techniques for measuring the canopy of tomato seedling. J. Agric. Eng. Res. 65(2), 85–95 (1996)

    Article  Google Scholar 

  30. Meyer, G.E., Neto, J.C.: Verification of color vegetation indices for automated crop imaging applications. Comput. Electron. Agric. 63, 282–293 (2008)

    Article  Google Scholar 

  31. Ruiz-Ruiz, G., Gomez-Gil, N., Navas-Gracia, L.M.: Testing different color spaces based on hue for the environmentally adaptive segmentation algorithm (EASA). Comput. Electron. Agric. 68(1), 88–96 (2009)

    Article  Google Scholar 

  32. Guijarro, M., Pajares, G., Riomoros, I., Herrera, P., Burgos-Artizzu, X., Ribeiro, A.: Automatic segmentation of relevant textures in agricultural images. Comput. Electron. Agric. 75, 75–83 (2011)

    Article  Google Scholar 

  33. Otsu, N.: A threshold selection method from gray-level histogram. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)

    Article  MathSciNet  Google Scholar 

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Acknowledgments

The authors would like to thank Neal Barto, Myles Lewis, and Charley Defer for assisting with the construction of the machine vision system. This research was supported by NASA Ralph Steckler Space Colonization Research and Technology Development Program Project funds (NNX10AC28A) and by the Technology and Research Initiative Fund (TRIF) Imaging Fellowship program funding.

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  1. Department of Agricultural and Biosystems Engineering, University of Arizona, Tucson, AZ, 85721-0038, USA

    David Story & Murat Kacira

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  1. David Story
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  2. Murat Kacira
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Correspondence to Murat Kacira.

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Story, D., Kacira, M. Design and implementation of a computer vision-guided greenhouse crop diagnostics system. Machine Vision and Applications 26, 495–506 (2015). https://doi.org/10.1007/s00138-015-0670-5

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  • DOI: https://doi.org/10.1007/s00138-015-0670-5

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