Design of Crop Leaf Area Measurement using Webcam and Raspberry Pi

Authors

  • Hidayat Universitas Komputer Indonesia
  • Muhammad Mahardiansyah PT. Cakrawala Lintas Kepri, Tanjungpinang

DOI:

https://doi.org/10.31358/techne.v21i2.327

Keywords:

image processing, leaf area measurement, raspberry PI

Abstract

This paper describes an electronic system for measuring the number of leaves and the total leaf area. This system is built to make it easier for users to compute several leaves and measure leaf area electronically. The number of leaves and leaf area are used to determine the level of fertility of a plant. Several important factors in measuring leaf area are the accuracy and the speed of measurement. The stages conducted in this research consist of needs analysis, design, implementation and testing. The system built uses a mini PC Raspberry Pi as the data processor and a webcam to capture images. Moreover, the image is processed using Binary threshold and Otsu threshold methods. The results showed that the designed system was functioning properly with an error rate of 0% for the number of leaves calculation and a 0.39% error rate in the of leaf area measurement.

Downloads

Download data is not yet available.

References

G. Yan et al., “Review of indirect optical measurements of leaf area index: Recent advances, challenges, and perspectives,” Agric. For. Meteorol., vol. 265, no. 265, pp. 390–411, 2019, doi: 10.1016/j.agrformet.2018.11.033.

C. O. Dimkpa, J. Fugice, U. Singh, and T. D. Lewis, “Development of fertilizers for enhanced nitrogen use efficiency–Trends and perspectives,” Sci. Total Environ., vol. 731, p. 139113, 2020.

H. Ullah, R. Santiago-Arenas, Z. Ferdous, A. Attia, and A. Datta, “Improving water use efficiency, nitrogen use efficiency, and radiation use efficiency in field crops under drought stress: A review,” Adv. Agron., vol. 156, pp. 109–157, 2019.

A. Simic Milas, M. Romanko, P. Reil, T. Abeysinghe, and A. Marambe, “The importance of leaf area index in mapping chlorophyll content of corn under different agricultural treatments using UAV images,” Int. J. Remote Sens., vol. 39, no. 15–16, pp. 5415–5431, 2018.

H. Fang, F. Baret, S. Plummer, and G. Schaepman?Strub, “An overview of global leaf area index (LAI): Methods, products, validation, and applications,” Rev. Geophys., vol. 57, no. 3, pp. 739–799, 2019.

M. C. Singh, K. Singh, and J. Singh, “Indirect method for measurement of leaf area and leaf area index of soilless cucumber crop,” Adv. Plants Agric. Res., vol. 8, no. 2, pp. 188–191, 2018, doi: 10.15406/apar.2018.08.00311.

S. K. Pandey and H. Singh, “A Simple, Cost-Effective Method for Leaf Area Estimation,” J. Bot., vol. 2011, pp. 1–6, 2011, doi: 10.1155/2011/658240.

C. Igathinathane, B. Chennakesavulu, K. Manohar, A. R. Womac, and L. O. Pordesimo, “Photovoltaic leaf area meter development and testing,” Int. J. Food Prop., vol. 11, no. 1, pp. 53–67, 2008, doi: 10.1080/10942910600954739.

D. Engin and M. Engin, “Design of a plant leaf area meter using PV cell and embedded microcontroller,” Adv. Mater. Sci. Eng., vol. 2013, 2013, doi: 10.1155/2013/393045.

B. Chen, Z. Fu, Y. Pan, J. Wang, and Z. Zeng, “Single leaf area measurement using digital camera image,” IFIP Adv. Inf. Commun. Technol., vol. 345 AICT, no. PART 2, pp. 525–530, 2011, doi: 10.1007/978-3-642-18336-2_64.

M. Can, O. Gursoy, B. Akcesme, and F. B. Akcesme, “Leaf Area Assessment By Image Analysis,” Southeast Eur. J. Soft Comput., vol. 1, no. 2, 2012, doi: 10.21533/scjournal.v1i2.54.

M. Alamsyah, “Segmentasi Citra Iris Mata Menggunakan Metode Otsu Thresholding,” J. Inf. Technol. Comput. Sci., vol. 4, no. 1, pp. 23–26, 2019, doi: 10.31328/jo.

B. Baso, D. Nababan, and R. Y. Kolloh, “Segmentasi Citra Tenun Menggunakan Metode Otsu Thresholding dengan Median Filter,” J. Teknol. DAN ILMU Komput. PRIMA, vol. 5, no. 1, pp. 1–6, 2022.

A. Syaeful, M. I. Fadillah, I. Muftadi, and D. Iskandar, “Klasifikasi Citra Bunga Dahlia Berdasarkan Warna Menggunakan Metode Otsu Thresholding Dan Naïve Bayes,” J-SAKTI (Jurnal Sains Komput. dan Inform., vol. 6, no. 1, pp. 575–582, 2022.

M. A. Ansari, D. Kurchaniya, and M. Dixit, “A Comprehensive Analysis of Image Edge Detection Techniques,” Int. J. Multimed. Ubiquitous Eng., vol. 12, no. 11, pp. 1–12, 2017, doi: 10.14257/ijmue.2017.12.11.01.

B. Sinaga, J. Manurung, M. H. Silalahi, and S. Ramen, “Deteksi Tepi Citra Dengan Metode Laplacian of Gaussian Dan Metode Canny,” J. Sains Komput. Inform., vol. 5, no. 2, pp. 1066–1084, 2021.

S. Naik and E. Sudarshan, “Smart healthcare monitoring system using raspberry Pi on IoT platform,” ARPN J. Eng. Appl. Sci., vol. 14, no. 4, pp. 872–876, 2019.

M. D. Mudaliar and N. Sivakumar, “IoT based real time energy monitoring system using Raspberry Pi,” Internet of Things, vol. 12, p. 100292, Dec. 2020, doi: 10.1016/j.iot.2020.100292.

A. A. Alkandari and S. Moein, “Implementation of monitoring system for air quality using raspberry PI: Experimental study,” Indones. J. Electr. Eng. Comput. Sci., vol. 10, no. 1, pp. 43–49, 2018, doi: 10.11591/ijeecs.v10.i1.pp43-49.

I. Desnanjaya and I. N. A. Arsana, “Home security monitoring system with IoT-based Raspberry Pi,” Indones. J. Electr. Eng. Comput. Sci, vol. 22, no. 3, p. 1295, 2021.

H. Zhang, R. Srinivasan, and V. Ganesan, “Low cost, multi-pollutant sensing system using raspberry pi for indoor air quality monitoring,” Sustainability, vol. 13, no. 1, p. 370, 2021.

Downloads

Published

29-09-2022

How to Cite

Hidayat, & Mahardiansyah, M. (2022). Design of Crop Leaf Area Measurement using Webcam and Raspberry Pi. Techné : Jurnal Ilmiah Elektroteknika, 21(2), 285–296. https://doi.org/10.31358/techne.v21i2.327

Issue

Section

Articles