A robust automated flower estimation system for grape vines. (August 2018)

• Record Type: Journal Article
• Authors: Liu, Scarlett; Li, Xuesong; Wu, Hongkun; Xin, Bolai; Tang, Julia; Petrie, Paul R.; Whitty, Mark
• Is Part Of: Biosystems engineering. Volume 172(2018)
• Page Start: 110
• Page End: 123
• Publication Date: 2018-08
• Subjects: Flower counting -- Image processing -- Grape vine -- Computer vision -- Grape yield estimation; Bioengineering -- Periodicals; Agricultural engineering -- Periodicals; Biological systems -- Periodicals; Génie rural -- Périodiques; Systèmes biologiques -- Périodiques; 631
• DOI: 10.1016/j.biosystemseng.2018.05.009
• Languages: English
• ISSNs: 1537-5110

Abstract

Abstract : Automated flower counting systems have recently been developed to process images of grapevine inflorescences, which assist in the critical tasks of determining potential yields early in the season and measurement of fruit-set ratios without arduous manual counting. In this paper, we introduce a robust flower estimation system comprised of an improved flower candidate detection algorithm, flower classification and finally flower estimation using calibration models. These elements of the system have been tested in eight aspects across 533 images with associated manual counts to determine the overall accuracy and how it is affected by experimental conditions. The proposed algorithm for flower candidate detection and classification is superior to all existing methods in terms of accuracy and robustness when compared with images where visible flowers are manually identified. For flower estimation, an accuracy of 84.3% against actual manual counts was achieved both in-vivo and ex-vivo and found to be robust across the 12 datasets used for validation. A single-variable linear model trained on 13 images outperformed other estimation models and had a suitable balance between accuracy and manual counting effort. Although accurate flower counting is dependent on the stage of inflorescence development, we found that once they reach approximately EL16 this dependency decreases and the same estimation model can be used within a range of about two EL stages. A global model can be developed across multiple cultivars if they have inflorescences with a similar size and structure. Highlights: Flower detection algorithm had F1 score 0.93 and flower estimation had 84% accuracy. Single-variable linear model calibrated from same cultivar had best performance. Best EL stage for imaging inflorescence is around EL stage 16 for flower estimation. Correct metric for assessing the performance of flower estimation is highlighted. Twelve testing datasets (4 cultivars, 533 photos) published as benchmark.