The estimation of leaf area is an important measure for understanding the growth, development, and productivity of tomato plants. In this study, we focused on the leaf area of a potted tomato plant and proposed methods, namely, NP, D², and D³, for estimating its leaf area. In the NP method, we used multiple tomato plant images from different viewing angles to reduce the estimation error of the leaf area, whereas in the D² and D³ methods, we further compensated for the perspective effects. The performances of the proposed methods were experimentally assessed using 40 “Momotaro Peace” tomato plants. The experimental results confirmed that the NP method had a smaller mean absolute percentage error (MAPE) on the test set than the conventional estimation method that uses a single tomato plant image. Likewise, the D² and D³ methods had a smaller MAPE on the test set than the conventional method that did not compensate for perspective effects.

1. [1] D.-P. Guo and Y.-Z. Sun, “Estimation of leaf area of stem lettuce (Lactuca sativa var angustana) from linear measurements,” Indian J. of Agricultural Sciences, Vol.71, No.7, pp. 483-486, 2001.
2. [2] H. Kücükönder, S. Boyaci, and A. Akyüz, “A modelling study with an artificial neural network: Developing estimation models for the tomato plant leaf area,” Turkish J. of Agriculture and Forestry, Vol.40, No.2, pp. 203-212, 2016. https://doi.org/10.3906/tar-1408-28
3. [3] G. Carmassi, L. Incrocci, G. Incrocci, and A. Pardossi, “Non-destructive estimation of leaf area in tomato (Solanum lycopersicum L.) and gerbera (Gerbera jamesonii H. Bolus),” Agricoltura Mediterranea, Vol.137, pp. 172-176, 2007.
4. [4] D. Schwarz and H.-P. Kläring, “Allometry to estimate leaf area of tomato,” J. of Plant Nutrition, Vol.24, No.8, pp. 1291-1309, 2001. https://doi.org/10.1081/PLN-100106982
5. [5] N. Maeda, H. Suzuki, T. Kitajima, A. Kuwahara, and T. Yasuno, “Measurement of Tomato Leaf Area Using Depth Camera,” J. of Signal Processing, Vol.26, No.4, pp. 123-126, 2022. https://doi.org/10.2299/jsp.26.123
6. [6] D. Li, L. Xu, C. Tan, E. Goodman, D. Fu, and L. Xin, “Digitization and Visualization of Greenhouse Tomato Plants in Indoor Environments,” Sensors, Vol.15, No.2, pp. 4019-4051, 2015. https://doi.org/10.3390/s150204019
7. [7] T. Masuda, “Leaf Area Estimation by Semantic Segmentation of Point Cloud of Tomato Plants,” 2021 IEEE/CVF Int. Conf. on Computer Vision Workshops (ICCVW), pp. 1381-1389, 2021. https://doi.org/10.1109/ICCVW54120.2021.00159
8. [8] D. Bolya, C. Zhou, F. Xiao, and Y. J. Lee, “YOLACT: Real-Time Instance Segmentation,” arXiv:1904.02689, 2019. https://doi.org/10.48550/arXiv.1904.02689
9. [9] A. Grunnet-Jepsen and D. Tong, “Depth Post-Processing for Intell® RealSense™ D400 Depth Cameras,” Intel® RealSense™ Documentation, 2020.
10. [10] K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask R-CNN,” IEEE Int. Conf. on Computer Vision, pp. 2980-2988, 2017. https://doi.org/10.1109/ICCV.2017.322
11. [11] B. Russell, A. Torralba, K. Murphy, and W. Freeman, “LabelMe: A Database and Web-Based Tool for Image Annotation,” Int. J. of Computer Vision, Vol.77, Nos.1-3, pp. 157-173, 2008. https://doi.org/10.1007/s11263-007-0090-8
12. [12] K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” 2016 IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 770-778, 2015.
13. [13] O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. S. Bernstein, A. C. Berg, and F.-F. Li, “ImageNet Large Scale Visual Recognition Challenge,” arXiv:1409.0575, 2014. https://doi.org/10.48550/arXiv.1409.0575
14. [14] W. J. Smith, “Modern Optical Engineering: The Design of Optical Systems,” pp. 25-27, McGraw-Hill, 2000.
15. [15] P. Sollich and A. Krogh, “Learning with ensembles: How overfitting can be useful,” D. Touretzky, M. Mozer, and M. Hasselmo (Eds.), Advances in Neural Information Processing Systems 8: Proc. of the 8th Int. Conf. on Neural Information Processing Systems, pp. 190-196, MIT Press, 1995.
16. [16] L. I. Kuncheva and C. J. Whitaker, “Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy,” Machine Learning, Vol.51, pp. 181-207, 2003. https://doi.org/10.1023/A:1022859003006