On-Tree Harumanis Mango Fruit Sizing through Digital Image Analysis
DOI:
https://doi.org/10.36877/aafrj.a0000614Abstract
The study sought to precisely determine the dimensions of Harumanis mango fruit while they are still attached to the tree, using digital image processing methods. Harumanis mangoes images were photographed at varying distances (1, 2, and 3 meters) and heights (0.5, 1, and 1.5 meters) using a camera and then analysed with MATLAB software. The study showed that the lowest percentage errors for fruit length and width were 1.16% and 0.22%, respectively, when the camera was placed 2 meters away and 1 meter above the fruits. The average growth rate of fruit width and length was found to be around 1 cm/week and 0.44 cm/week, respectively, using digital image analysis. The coefficient of determination (R2) values for fruit length and width was 0.79 and 0.88, respectively, indicating the dependability of the estimated results. This method can be utilized to monitor the fruit's sizes, effectively replacing the tedious manual measuring procedure.
References
Ahmad, N. A., Muttalib, M. F. A., Uda, M. N. A., et al. (2023). Measurement of leaf chlorophyll content in Harumanis mango cultivated in a greenhouse using SPAD meter. Materials Today: Proceedings. https://doi.org/10.1016/j.matpr.2023.01.174
Cheng, H. D., Jiang, X. H., Sun, Y., et al. (2001). Color image segmentation: Advances and prospects. Pattern Recognition, 34(12). https://doi.org/10.1016/S0031-3203(00)00149-7
Cheng, H., Damerow, L., Sun, Y., et al. (2017). Early yield prediction using image analysis of apple fruit and tree canopy features with neural networks. Journal of Imaging, 3(1). https://doi.org/10.3390/jimaging3010006
Cohen, M., Savé, R., Biel, C., et al. (1998). Simultaneous measurements of water stress with LVDT sensors and electrotensiometers: application in pepper plants grown in two types of perlites. Acta Horticulturae, 421, 193–200. https://doi.org/10.17660/ActaHortic.1998.421.20
Durán Zuazo, V. H., Tarifa, D. F., Rodríguez, B. C., et al. (2021). Mango fruit quality improvements in response to water stress: Implications for adaptation under environmental constraints. Horticultural Science, 48(1), 1–11. https://doi.org/10.17221/45/2020-HORTSCI
Farook, R. S. M., Hallis, A., Aziz, A., et al. (2012). Data Mining on Climatic Factors for Harumanis Mango Yield Prediction. 115–119. https://doi.org/10.1109/ISMS.2012.51
Ganiron, T. U. (2014). Size properties of mangoes using image analysis. International Journal of Bio-Science and Bio-Technology, 6(2). https://doi.org/10.14257/ijbsbt.2014.6.2.03
Hsu, T. S., & Wang, T. C. (2015). An improvement stereo vision images processing for object distance measurement. International Journal of Automation and Smart Technology, 5(2). https://doi.org/10.5875/ausmt.v5i2.460
Kumar, G., & Bhatia, P. K. (2014). A detailed review of feature extraction in image processing systems. International Conference on Advanced Computing and Communication Technologies, ACCT. https://doi.org/10.1109/ACCT.2014.74
Link, S. O., Thiede, M. E., & Van Bavel, M. G. (1998). An improved strain-gauge device for continuous field measurement of stem and fruit diameter. In Journal of Experimental Botany (Vol. 49, Issue 326). https://academic.oup.com/jxb/article/49/326/1583/532161
Mohd Aziz, R., Shaidatul, A. A. T., Fauzi, J., et al. (2019). Physiological responses, nutrient content and fruit quality of mango (Mangifera indica L.) cv. Harumanis at different agro-climatic zones. 28th Malaysian Society of Plant Physiology Conference (MSPPC 2018), Challenges and Strategies for Plant Productivity and Resilience, Kelantan, Malaysia, 28-30 August 2018, January.
Momin, M. A., Rahman, M. T., Sultana, M. S., et al. (2017). Geometry-based mass grading of mango fruits using image processing. Information Processing in Agriculture, 4(2). https://doi.org/10.1016/j.inpa.2017.03.003
Morandi, B., Manfrini, L., Zibordi, M., et al. (2007). A low-cost device for accurate and continuous measurements of fruit diameter. HortScience, 42(6). https://doi.org/10.21273/hortsci.42.6.1380
Nasron, N., Ghazali, N. S., Shahidin, N. M., et al. (2021). Soil suitability assessment for harumanis mango cultivation in UiTM Arau, Perlis. IOP Conference Series: Earth and Environmental Science, 620(1). https://doi.org/10.1088/1755-1315/620/1/012007
Neupane, C., Pereira, M., Koirala, A., et al. (2023). Fruit Sizing in Orchard: A Review from Caliper to Machine Vision with Deep Learning. Sensors, 23(8), 3868. https://doi.org/10.3390/s23083868
Pal, N. R., & Pal, S. K. (1993). A review on image segmentation techniques. Pattern Recognition, 26(9). https://doi.org/10.1016/0031-3203(93)90135-J
Park, B. & Lu, R. (2015). Hyperspectral imaging technology in food and agriculture. In Hyperspectral Imaging Technology in Food and Agriculture. https://doi.org/10.1007/978-1-4939-2836-1
Sa, I., Ge, Z., Dayoub, F., et al. (2016). Deepfruits: A fruit detection system using deep neural networks. Sensors (Switzerland), 16(8). https://doi.org/10.3390/s16081222
Sani, M. A., Abbas, H., Jaafar, M. N., et al. (2018). Morphological Characterisation of Harumanis Mango (Mangifera indica Linn.) in Malaysia. International Journal of Environmental & Agriculture Research, 4(1).
Spreer, W., & Müller, J. (2011). Estimating the mass of mango fruit (Mangifera indica, cv. Chok Anan) from its geometric dimensions by optical measurement. Computers and Electronics in Agriculture, 75(1). https://doi.org/10.1016/j.compag.2010.10.007
Stein, M., Bargoti, S., & Underwood, J. (2016). Image based mango fruit detection, localisation and yield estimation using multiple view geometry. Sensors (Switzerland), 16(11). https://doi.org/10.3390/s16111915
Talib, S. A. A., Hassan, M. H. M., Rashid, M. A., et al. (2020). Effects of Environmental Temperature and Precipitation Pattern on Growth Stages of Magnifera indica cv. Harumanis Mango. Journal of Agricultural Science, 12(12), 26. https://doi.org/10.5539/jas.v12n12p26
Uda, M. N. A., Gopinath, S. C. B., Hashim, U., et al. (2020). Harumanis Mango: Perspectives in Disease Management and Advancement using Interdigitated Electrodes (IDE) Nano-Biosensor. IOP Conference Series: Materials Science and Engineering, 864(1). https://doi.org/10.1088/1757-899X/864/1/012180
Wang, Z., Koirala, A., Walsh, K., et al. (2018). In field fruit sizing using a smart phone application. Sensors (Switzerland), 18(10). https://doi.org/10.3390/S18103331
Wang, Z., Walsh, K. B., & Verma, B. (2017). On-tree mango fruit size estimation using RGB-D images. Sensors (Switzerland), 17(12). https://doi.org/10.3390/s17122738
Yusuf, S. N. A., Rahman, A. M. A., Zakaria, Z., et al. (2020). Morphological variability identification of harumanis mango (Mangifera indica l.) harvested from different location and tree age. Tropical Life Sciences Research, 31(2). https://doi.org/10.21315/tlsr2020.31.2.6
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Nur Adilah Zulkifli, Muhammad Firdaus Abdul Muttalib, Muhammad Nur Aiman Uda, Zainal Abidin Arsat, Fadhilnor Abdullah, Mohd Khairul Rabani Hashim, Fathin Ayuni Azizan, Mohd Fauzie Jusoh, Syarifah Rokiah Syd Kamaruzaman, Ahmad Azudin Nordin
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Author(s) shall retain the copyright of their work and grant the Journal/Publisher right for the first publication with the work simultaneously licensed under:
Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). This license allows for the copying, distribution and transmission of the work, provided the correct attribution of the original creator is stated. Adaptation and remixing are also permitted.
This broad license intends to facilitate free access to, as well as the unrestricted reuse of, original works of all types for non-commercial purposes.
The author(s) permits HH Publisher to publish this article that has not been submitted elsewhere.
.png){kind=logo}
.jpg)
