International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume XLII-2/W13
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 407–411, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-407-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 407–411, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-407-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  04 Jun 2019

04 Jun 2019

PREDICTING BIOMASS AND YIELD AT HARVEST OF SALT-STRESSED TOMATO PLANTS USING UAV IMAGERY

K. Johansen1, M. J. L. Morton2, Y. Malbeteau1, B. Aragon1, S. Al-Mashharawi1, M. Ziliani1, Y. Angel1, G. Fiene2, S. Negrao2,3, M. A. A. Mousa4,5, M. A. Tester2, and M. F. McCabe1 K. Johansen et al.
  • 1Water Desalination and Reuse Center, King Abdullah University of Science and Technology, Thuwal, 23955-6900, Kingdom of Saudi Arabia
  • 2Center for Desert Agriculture, King Abdullah University of Science and Technology, Thuwal, 23955-6900, Kingdom of Saudi Arabia
  • 3School of Biology and Environmental Science, University College Dublin, Belfield, Dublin 4, Ireland
  • 4Department of Arid Land Agriculture, Faculty of Meteorology, Environment & Arid Land Agriculture, King Abdulaziz University, Jeddah, 21589, Kingdom of Saudi Arabia
  • 5Department of Vegetables, Faculty of Agriculture, Assiut University, 71526, Assiut, Egypt

Keywords: UAV, yield, biomass, tomato plants, salinity

Abstract. Biomass and yield are important variables used for assessing agricultural production. However, these variables are difficult to estimate for individual plants at the farm scale and may be affected by abiotic stressors such as salinity. In this study, the wild tomato species, Solanum pimpinellifolium, was evaluated through field and UAV-based assessment of 600 control and 600 salt-treated plants. The aim of this research was to determine, if UAV-based imagery, collected one, two, four, six, seven and eight weeks before harvest could predict fresh shoot mass, tomato fruit numbers, and yield mass at harvest and if predictions varied for control and salt-treated plants. A Random Forest approach was used to model biomass and yield. The results showed that shape features such as plant area, border length, width and length had the highest importance in the random forest models. A week prior to harvest, the explained variance of fresh shoot mass, number of fruits and yield mass were 86.60%, 59.46% and 61.09%, respectively. The explained variance was reduced as a function of time to harvest. Separate models may be required for predicting yield of salt-stressed plants, whereas the prediction of yield for control plants was less affected if the model included salt-stressed plants. This research demonstrates that it is possible to predict biomass and yield of tomato plants up to four weeks prior to harvest, and potentially earlier in the absence of severe weather events.