JACIII Vol.22 No.7 pp. 1120-1125
doi: 10.20965/jaciii.2018.p1120


Tomato Yield Estimation Based on Object Detection

Jun Liu

Facility Horticulture Laboratory of Universities in Shandong, Weifang University of Science and Technology
Xinyizhong Garden, Happy Road, Shouguang, Weifang, Shandong 262700, China

May 22, 2018
July 10, 2018
November 20, 2018
tomato, yield estimation, object detection, regression analysis

At present, the vegetable yield estimation in China is performed by manual sampling and visual observation of vegetable counts. This is not only time-consuming and labor-intensive, but it also has low precision. In this study, we capture video surveillance images of the tomatoes during plant maturation, and use neural networks to identify pictures, extract growing features, identify the number of vegetables hanging from the plants, and establish an estimation model for tomato yield. We then take a sample of the vegetables to be measured. Strains are image-analyzed and processed to predict yield per plant and yield per unit area to obtain an accurate prediction of tomato yield.

Cite this article as:
J. Liu, “Tomato Yield Estimation Based on Object Detection,” J. Adv. Comput. Intell. Intell. Inform., Vol.22, No.7, pp. 1120-1125, 2018.
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Last updated on Dec. 13, 2018