Prediction of strawberry yield based on receptacle detection and Bayesian inference

Sunghyun Yoon; Jung Su Jo; Steven B Kim; Ha Seon Sim; Sung Kyeom Kim; Dong Sub Kim

doi:10.1016/j.heliyon.2023.e14546

Prediction of strawberry yield based on receptacle detection and Bayesian inference

Heliyon. 2023 Mar 13;9(3):e14546. doi: 10.1016/j.heliyon.2023.e14546. eCollection 2023 Mar.

Authors

Sunghyun Yoon¹, Jung Su Jo^{2

3}, Steven B Kim⁴, Ha Seon Sim², Sung Kyeom Kim^{2

3}, Dong Sub Kim⁵

Affiliations

¹ Department of Artificial Intelligence, Kongju National University, Cheonan 31080, South Korea.
² Department of Horticultural Science, College of Agricultural & Life Science, Kyungpook National University, Daegu 41566, South Korea.
³ Institute of Agricultural Science and Technology, Kyungpook National University, Daegu 41566, South Korea.
⁴ Department of Mathematics and Statistics, California State University, Monterey Bay, Seaside 93955, CA, USA.
⁵ Department of Horticulture, Kongju National University, Yesan 32439, South Korea.

Abstract

The receptacle of strawberry is a more direct part than the flower for predicting yield as they eventually become fruits. Thus, we tried to predict the yield by combining an AI technique for receptacle detection in images and statistical analysis on the relationship between the number of receptacles detected and the strawberry yield over a period of time. Five major cultivars were cultivated to consider the cultivar characteristics and environmental factors for two years were collected to consider the climate difference. Faster R-CNN based object detector was used to estimate the number of receptacles per strawberry plant in given two-dimensional images, which achieved a mAP of 0.6587 for our dataset. However, not all receptacles appear on the two-dimensional images, and Bayesian analysis was used to model the uncertainty associated with the number of receptacles missed by the AI. After estimating the probability of fruiting per receptacle, prediction models for the total strawberry yield at the end of harvest season were evaluated. Even though the detection accuracy was not perfect, the results indicated that counting the receptacles by object detection and estimating the probability of fruiting per receptacle by Bayesian modeling are more useful for predicting the total yield per plant than knowing its cumulative yield during the first month.

Keywords: Bayesian statistical analysis; Faster R–CNN; Object detection; Receptacle; Two seasons.