Efficiency analysis of the use of model matching algorithm for plant counting

Authors

DOI:

https://doi.org/10.33448/rsd-v9i7.4576

Keywords:

Fruit trees; Remote sensing; Drone; Precision agriculture.

Abstract

Often the producer does not know the exact number of fruit trees on his property or is unaware over the years due to the death of many plants. As a result, in order to avoid the need for a field trip for manual counting, this research aimed to use a model matching algorithm in parallel with the use of a low-cost drone to assess its efficiency in automatic counting of spaced canopy plants and joints. The red, green and blue bands captured by the Phantom 4 Advanced were used, and the red band with linear enhancement for the cut option, to facilitate the distinction of the orchard and the rest of the targets in the image and to obtain a better result in the detection of fruit trees. The flight was performed at a height of 80 meters with an overlap between bands of 70% and in the same range of 80%. As a result, 97.98% of fruit trees were detected in plants with well-spaced crowns and 88.52% were identified in plants with crowns together. The numbers of false positives found were small for all situations tested, these false positives being weeds. It is concluded that the technique is efficient for counting plants with fair and spaced crowns, and detection can be improved when there is a good contrast between what you want to detect and the targets that are not of interest.

References

Abidin, H.; Samad, MN.; Ping, LY & Noor, MKAM (2017). Evaluation of ecognition software for oil palm tree counting under different planting conditions and ages. International Conference on Big Data Applications in Agriculture.

Al-Amri, SS.; Kalyankar, NV & Khamitkar, SD (2010). Linear and non-linear contrast enhancement image. International Journal of Computer Science and Network Security, 10(2), 139-143.

Arantes, BHT.; Arantes, LT; Giongo, PR.; Moraes, VH.; Costa, EM & Silva, PC (2020). Eficiência de distribuição do sistema de irrigação, por meio de um veículo aéreo não tripulado de baixo custo/Efficiency of irrigation system distribution through a low-cost unmanned aerial vehicle. Brazilian Journal of Development, 6(4), 20332-20346.

Brunelli, R (2009). Template matching techniques in computer vision: theory and practice. John Wiley & Sons.

Centeno, JAS (2003). Sensoriamento remoto e processamento de imagens digitais. Curitiba: UFPR, 219.

Cheng, G & Han, J (2016). A survey on object detection in optical remote sensing images. ISPRS Journal of Photogrammetry and Remote Sensing, 117, 11-28.

Halazonetis, DJ (2004). What does the histogram of an image show? American journal of orthodontics and dentofacial orthopedics, 125 (2), 220-222.

Hirschmugl, M.; Ofner, M.; Raggam, J & Schardt, M (2007). Single tree detection in very high resolution remote sensing data. Remote Sensing of Environment, 110(4), 533-544.

Isip, MF.; Camaso, EE.; Damian, GB & Alberto, RT (2018). Estimation of Mango Tree Count and Crown Cover Delineation using Template Matching Algorithm, 6(3), 1955-1960.

Li, W.; Fu, H.; Yu, L & Cracknell, A (2017). Deep learning based oil palm tree detection and counting for high-resolution remote sensing images. Remote Sensing, 9(1), 22.

Liu, J.; Zhou, C.; Chen, P & Kang, C (2017). An efficient contrast enhancement method for remote sensing images. IEEE Geoscience and Remote Sensing Letters, 14(10), 1715-1719.

Malek, S.; Bazi, Y.; Alajlan, N.; AlHichri, H & Melgani, F (2014). Efficient framework for palm tree detection in UAV images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(12), 4692-4703.

Norzaki, N & Tahar, KN (2019). A comparative study of template matching, ISO cluster segmentation, and tree canopy segmentation for homogeneous tree counting. International Journal of Remote Sensing, 40(19), 7477-7499.

Poddar, S.; Tewary, S.; Sharma, D.; Karar, V.; Ghosh, A & Pal, SK (2013). Non-parametric modified histogram equalisation for contrast enhancement. IET Image Processing, 7(7), 641-652.

Pratt, WK (2013). Introduction to digital image processing. CRC press.

Rex, FE.; Dalla Corte, AP.; Machado, S. A & Sanquetta, CR (2018). Identificação e extração de copas de Araucaria angustifolia (Bertol.) Kuntze a partir de dados lidar. Advances in Forestry Science, 5(2), 319-323.

Santoro, F.; Tarantino, E.; Figorito, B.; Gualano, S & D'Onghia, AM (2013). A tree counting algorithm for precision agriculture tasks. International Journal of Digital Earth, 6(1), 94-102.

Shafri, HZ.; Hamdan, N & Saripan, MI (2011). Semi-automatic detection and counting of oil palm trees from high spatial resolution airborne imagery. International journal of remote sensing, 32(8), 2095-2115.

SIDRA - Sistema IBGE de Recuperação Automática: Produção Agrícola Municipal. Acesso em 12 de março de 2020, em https://sidra.ibge.gov.br/tabela/5457.

Wan, M.; Gu, G.; Qian, W.; Ren, K.; Chen, Q & Maldague, X (2018). Infrared image enhancement using adaptive histogram partition and brightness correction. Remote Sensing, 10(5), 682.

Wong, CY.; Jiang, G.; Rahman, MA.; Liu, S.; Lin, SCF.; Kwok, N.; Shi, H.; Yu, YH & Wu, T (2016). Histogram equalization and optimal profile compression based approach for colour image enhancement. Journal of Visual Communication and Image Representation, 38, 802-813.

Published

30/05/2020

How to Cite

ARANTES, B. H. T.; ARANTES, L. T.; SANTOS, J. M. dos; VENTURA, M. V. A.; GOMES, L. F. Efficiency analysis of the use of model matching algorithm for plant counting. Research, Society and Development, [S. l.], v. 9, n. 7, p. e668974576, 2020. DOI: 10.33448/rsd-v9i7.4576. Disponível em: https://rsdjournal.org/index.php/rsd/article/view/4576. Acesso em: 23 feb. 2024.

Issue

Section

Engineerings