Plant Production Science (Oct 2018)

Application of infrared thermography to assess cassava physiology under water deficit condition

  • Piyanan Pipatsitee,
  • Apisit Eiumnoh,
  • Patchara Praseartkul,
  • Kanyarat Taota,
  • Sumaid Kongpugdee,
  • Kampol Sakulleerungroj,
  • Suriyan Cha-um

DOI
https://doi.org/10.1080/1343943X.2018.1530943
Journal volume & issue
Vol. 21, no. 4
pp. 398 – 406

Abstract

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Water deficit stress is a major factor that inhibits the overall growth and development in cassava (Manihot esculenta), leading to decreased storage root yield. We conducted a study to investigate whether thermal sensing could be used to indicate water deficit stress and the health and yield of cassava crops in field. The objective of the study was to use thermal imaging to determine relationship between crop water stress index (CWSI) and physiological changes, and to identify the critical CWSI point in fields of cassava cv. Rayong 9 under well-irrigated and water-deficit conditions. At the time of storage root initiation (85 DAP [day after planting]), thermal imagery was collected and the physiological changes and growth characters were measured prior to storage root harvesting (162 DAP). Thermal infrared imager was used to measure the canopy temperature and CWSI of cassava plants. Net photosynthetic rate (Pn), stomatal conductance (gs) and transpiration rates (Tr) of cassava plants under water deficit conditions for 29 d (114 DAP) were significantly decreased, leading to delayed plant growth as compared to those under well-irrigated conditions. In contrast, air vapor pressure deficit (VPDair) and CWSI in drought-stressed plants were higher than well irrigated plants. High correlations between Tr/gs/Pn and CWSI were observed. The study concludes that CWSI is a sensitive indicator of water deficit stress caused due to stomatal function. Abbreviations: CWSI: crop water stress index; DAP: day after planting; Pn: net photosynthetic rate; gs: stomatal conductance; Tr: transpiration rate; VPDair: air vapor pressure; RMSE: root mean square error

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