Unmanned Aircraft System- (UAS-) Based High-Throughput Phenotyping (HTP) for Tomato Yield Estimation Academic Article uri icon

abstract

  • Yield prediction and variety selection are critical components for assessing production and performance in breeding programs and precision agriculture. Since plants integrate their genetics, surrounding environments, and management conditions, crop phenotypes have been measured over cropping seasons to represent the traits of varieties. These days, UAS (unmanned aircraft system) provides a new opportunity to collect high-quality images and generate reliable phenotypic data efficiently. Here, we propose high-throughput phenotyping (HTP) from multitemporal UAS images for tomato yield estimation. UAS-based RGB and multispectral images were collected weekly and biweekly, respectively. The shape of the features of tomatoes such as canopy cover, canopy, volume, and vegetation indices derived from UAS imagery was estimated throughout the entire season. To extract time-series features from UAS-based phenotypic data, crop growth and growth rate curves were fitted using mathematical curves and first derivative equations. Time-series features such as the maximum growth rate, day at a specific event, and duration were extracted from the fitted curves of different phenotypes. The linear regression model produced high R 2 values even with different variable selection methods: all variables (0.79), forward selection (0.7), and backward selection (0.77). With factor analysis, we figured out two significant factors, growth speed and timing, related to high-yield varieties. Then, five time-series phenotypes were selected for yield prediction models explaining 65 percent of the variance in the actual harvest. The phenotypic features derived from RGB images played more important roles in prediction yield. This research also demonstrates that it is possible to select lower-performing tomato varieties successfully. The results from this work may be useful in breeding programs and research farms for selecting high-yielding and disease-/pest-resistant varieties.

published proceedings

  • Journal of Sensors

author list (cited authors)

  • Chang, A., Jung, J., Yeom, J., Maeda, M. M., Landivar, J. A., Enciso, J. M., Avila, C. A., & Anciso, J. R.

editor list (cited editors)

  • Wang, J.

publication date

  • January 1, 2021 11:11 AM