Validation of agronomic UAV and field measurements for tomato varieties Academic Article uri icon

abstract

  • © 2019 Elsevier B.V. Unmanned aerial vehicles (UAV) have been recognized as excellent tools to provide real time feedback of temporal and spatial conditions found in agricultural fields throughout the growing season. UAVs have also allowed accelerating breeding programs by screening varieties or by selecting agronomic traits that confer resistance to biotic and abiotic stresses and selecting the best management practices that optimize the management of soil and water resources. The main objectives of this study were to assess the potential use of UAVs to determine crop height, canopy cover, and NDVI during the tomato growing season for three tomato varieties; to validate tomato height obtained with a UAV; and evaluate the correlation between leaf area index and canopy cover determined with the UAV. The UAV was flown over a tomato trial planted with 90 plots that contained eight different tomato varieties; 3 roma and 5 round replicated three times per row and planted in three rows. The plots of the tomato varieties TAM-HOT, Shourouq, and Mykonos were selected for validation with the UAV. Commitment field measurements of plant height, leaf area index, and NDVI were collected weekly (from April 27 to June 22, 2017). All the tomato varieties were healthy without diseases and the NDVI values estimated with the UAV peaked between 90 and 110 days after transplanting (DAP). A coefficient of determination of 0.72 was observed between canopy cover estimated with the UAV and leaf area index measured with the ceptometer. The coefficient of correlation between the estimated and measured crop heights were 0.9845, 0.9766 and 0.9949 for the TAM-HOT, Shourouq and Mykonos, respectively. In addition, the calculated paired t test statistic showed no significant difference (P ≥ 0.05) between the estimated, the UAV and manually measured crop heights. In the future, UAV crop growth and NDVI monitoring could be improved through temporally dense data acquisition, increasing the number of ground samples and their geometric coincidence with the grids in UAV images, removal of weather effects, and other systematic errors caused from image quality and grid size.

author list (cited authors)

  • Enciso, J., Avila, C. A., Jung, J., Elsayed-Farag, S., Chang, A., Yeom, J., ... Chavez, J. C.

publication date

  • January 1, 2019 11:11 AM