Tomato Yield Estimation Based on Object Detection

Jun Liu

doi:10.20965/jaciii.2018.p1120

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JACIII Vol.22 No.7 pp. 1120-1125

doi: 10.20965/jaciii.2018.p1120

(2018)

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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

Received:

May 22, 2018

Accepted:

July 10, 2018

Published:

November 20, 2018

Keywords:

tomato, yield estimation, object detection, regression analysis

Abstract

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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References

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This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] R. Girshick, “Fast R-CNN,” IEEE Int. Conf. on Computer Vision (ICCV), pp. 1440-1448, 2015.

[2] [2] S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: towards real-time object detection with region proposal networks,” Int. Conf. on Neural Information Processing Systems, pp. 91-99, 2015.

[3] [3] K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask R-CNN,” IEEE Int. Conf. on Computer Vision (ICCV), pp. 2980-2988, 2017.

[4] [4] H. Liu, W. Kang, S. Ustin et al., “Prediction of crop yield based on time series hyperspectral remote sensing images at field scale,” Spectroscopy and Spectral Analysis, Vol.36, No.8, pp. 2585-2589, 2016.

[5] [5] H. Cui, H. Xu, W. Zhang et al., “Prediction Model of Lodging Winter Wheat Yield Based on Hyperspectral,” J. of Chinese Crop Sciences, Vol.35, No.8, pp. 1155-1160, 2015.

[6] [6] X. Liang, Y. Zhang, and X. Wang, “Prediction of per capita grain yield in China based on BP neural network and Logistic model,” J. of South China Normal University (Social Science Edition), No.5, pp. 102-106, 2015.

[7] [7] C. Fan, P. Cao, Y. Guo et al., “Prediction of Grain Yield Based on Greyscale Extreme Learning Machine,” Jiangsu Agricultural Sciences, Vol.46, No.5, pp. 212-214, 2018.

[8] [8] J. Li and Z. Liu, “Object detection based on visual saliency map and parameter,” J. of Computer Applications, Vol.35, No.12, pp. 3560-3564, 2015.

[9] [9] Y. Zhou and X. Wang, “A method of automatic segmentation of robust image saliency,” CN104809729A, 2015.

[10] [10] F. Yang, J. Li, X. Li et al., “Saliency detection algorithm based on multi-task deep convolutional neural network,” J. of Computer Applications, No.1, pp. 91-96, 2018.

[11] [11] R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation,” IEEE Conf. on Computer Vision and Pattern Recognition, pp. 580-587, 2014.