Automatic diagnosis of strawberry water stress status based on machine vision
Keywords:
automatic diagnosis, water stress, crop water stress index, machine vision, strawberryAbstract
Water stress status of plants is very important for irrigation scheduling. However, plant water stress status monitoring has become the bottleneck of irrigation scheduling. In this study, an automatic water stress status monitoring method for strawberry plant was proposed and realized using combined RGB and infrared image information. RGB image and infrared images were obtained using RGB digital camera and infrared thermal camera, which were placed in a fixed shell in parallel. In the first experimental stage, three kinds of water stress treatments were carried out on three groups of strawberry plants, and each group includes three repetitions. Single point plant temperature, dry surface temperature, wet surface temperature were measured. In the second experimental stage, the infrared and visible light images of the canopy leaves were obtained. Meanwhile, plant temperature, dry surface temperature, wet surface temperature, and stomatal conductance were measured not only for single point but also for plant area temperature measurement. Fusion information of infrared image and visible light image was analyzed using image processing technology, to calculate the average temperature of plant areas. Based on single point temperature, area temperature, dry surface temperature and wet surface temperature of the plant, single point crop water stress index (CWSI) and area CWSI were calculated. Through analysis of variance (ANOVA), the experimental results showed that CWSI measured for plants under different treatments, were significantly different. Through correlation analysis, the experimental results showed that, determination coefficient between area CWSI and the corresponding stomatal conductance of three strawberry groups were 0.8834, 0.8730 and 0.8851, respectively, which were larger than that of single-point CWSI and stomatal conductance. The results showed that area CWSI is more suitable to be used as the criteria for automatic diagnosis of plants. Keywords: automatic diagnosis, water stress, crop water stress index, machine vision, strawberry DOI: 10.25165/j.ijabe.20191201.4293 Citation: Li H, Yin J, Zhang M, Sigrimis N, Gao Y, Zheng W G. Automatic diagnosis of strawberry water stress status based on machine vision. Int J Agric & Biol Eng, 2019; 12(1): 159–164.References
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[3] Sui R, Fisher D K, Barnes E M. Soil moisture and plant canopy temperature sensing for irrigation application in cotton. Journal of Agricultural Science, 2012; 4(12): 93–105.
[4] Raper T B, Henry C G, Espinoza L, Ismanov M, Oosterhuis D M. Response of two inexpensive commercially produced soil moisture sensors to changes in water content and soil texture. Agricultural Sciences, 2015; 6(10): 1148–1163.
[5] Müller T, Bouleau C R, Perona P. Optimizing drip irrigation for eggplant crops in semi-arid zones using evolving thresholds. Agricultural Water Management, 2016; 177: 54–65.
[6] Allen R G, Pereira L S, Raes D, Smith M. Crop evapotranspiration. FAO Irrigation and Drainage Paper 56. Rome: FAO, 1999.
[7] Jones H G. Plant water relations and implications for irrigation scheduling. Acta Horticulturae, 1990; 278: 67–76.
[8] Idso S B, Jackson R D, Pinter P J, Reginato R J, Hatfield J L. Normalizing the stress-degree-day parameter for environmental variability. Agricultural Meteorology, 1981; 24: 45–55.
[9] Jackson R D, Idso S B, Reginato R, Pinter P J. Canopy temperature as a drought stress indicator. Water Resources Research, 1981; 17: 1133–1138.
[10] Jackson R D, Kustas W P, Choudhury B J. A re-examination of the crop water stress index. Irrig Sci, 1988; 9: 309–317
[11] Jones H G, Stoll M, Santos T, de Sousa C, Chaves M M, Grant O M. Use of infrared thermography for monitoring stomatal closure in the field: application to grapevine. Journal of Experimental Botany, 2002; 53: 2249–2260.
[12] Leinonen I, Jones H G. Combining thermal and visible imagery for estimating canopy temperature and identifying plant stress. Journal of Experimental Botany, 2004; 55(401): 1423–1431.
[13] Meron M, Tsipris J, Charitt D. Remote mapping of crop water status to assess spatial variability of crop stress. Precision Agriculture. Proceedings of the 4th European Conference on Precision Agriculture, Berlin, Germany. Wageningen: Academic Publishers, 2003; pp.405–410.
[14] Maes W H, Baert A, Huete A R, Minchin P E H, Snelgar W P, Steppe K. A new wet reference target method for continuous infrared thermography of vegetations. Agricultural and Forest Meteorology, 2016; 226: 119–131.
[15] Roy S, Ophori D. Estimation of crop water stress index in almond orchards using thermal aerial imagery. Journal of Spatial Hydrology, 2014; 12(1).
[16] Bellvert J, Marsal J, Girona J, Zarco-Tejada P J. Seasonal evolution of crop water stress index in grapevine varieties determined with high-resolution remote sensing thermal imagery. Irrigation Science, 2015; 33(2): 81–93.
[17] Bellvert J, Zarco‐Tejada P J, Marsal J, Girona J, González‐Dugo V, Fereres E. Vineyard irrigation scheduling based on airborne thermal imagery and water potential thresholds. Australian Journal of Grape and Wine Research, 2015.
[18] Gerhards M, Rock G, Schlerf M, Udelhoven T. Water stress detection in potato plants using leaf temperature, emissivity, and reflectance. International Journal of Applied Earth Observation and Geoinformation, 2016; 53: 27–39.
[19] Möller M, Alchanatis V, Cohen Y, Meron M, Tsipris J, Naor A, et al. Use of thermal and visible imagery for estimating crop water status of irrigated grapevine. Journal of Experimental Botany, 2007; 58(4): 827.
[20] Raza S E A. Registration of thermal and visible light images of diseased plants using silhouette extraction in the wavelet domain. Pattern Recognition, 2015; 48(7): 2119–2128.
[21] Kovesi P. Phase congruency detects corners and edges. In Australian Patt. Recog. Soc. Conf. DICTA. Sydney WA, 2003; pp.309–318.
[22] Lowe D. Distinctive image features from scale-invariant key points. International Journal of Computer Vision, 2004; 60(2): 91–110.
[23] Bay H, Ess A, Tuytelaars T, Goola L V. SURF: Speeded-Up Robust Features (SURF), Computer Vision and Image Understanding, 2008; l10(3): 346–359.
[24] Chen F, Wang R. Fast RANSAC with preview model parameters evaluation. Journal of Software, 2005; 16(8): 1431–1437.
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Published
2019-02-01
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Li, H., Yin, J., Zhang, M., Sigrimis, N., Gao, Y., & Zheng, W. (2019). Automatic diagnosis of strawberry water stress status based on machine vision. International Journal of Agricultural and Biological Engineering, 12(1), 159–164. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/4293
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Information Technology, Sensors and Control Systems
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