Analysis of Constant Values of Leaves of the Durian Cultivars Monthong and Bawor Using Digital Image Processing

##plugins.themes.academic_pro.article.main##

Bayu Dwi Arfiyanto
Farchan Mushaf Al Ramadhani
Sajuri

Abstract

Durian (Durio zibethinus), particularly the cultivars Monthong and Bawor, is a leading horticultural commodity with high economic value. Accurate leaf area estimation is essential for supporting physiological studies and plant growth modeling. However, conventional measurement methods are often characterized by their slow and destructive nature. This study aimed to analyze and identify the constant (k) values of the leaves of durian cultivars Monthong and Bawor using a digital image processing approach. A total of 40 leaf samples from each cultivar were analyzed. Image acquisition was performed using a smartphone camera, while image processing and leaf area measurement were conducted with the ImageJ software. The leaf constant was calculated as the ratio of the digitally measured leaf area to the product of manually measured leaf length and width. The results showed that the mean leaf constant for Monthong durian was 0.702, while for Bawor durian, it was 0.691. These results exhibited narrow value distributions, devoid of any outliers. The correlation between the measured and predicted leaf area yielded very high coefficients of determination (R² of 0.997 for cultivar Monthong and R² of 0.999 for cultivar Bawor). Further statistical evaluation confirmed that the predictive model had very high accuracy, evidenced by its low RMSE values (≤ 1.059), an NRMSE of 0.01, an NSE of at least 0.997, and a Willmott’s index of agreement (d) of at least 0.999. These results indicate that leaf constant values derived from digital image processing can generate precise leaf area estimates and offer a fast, efficient, and non-destructive alternative to conventional measurement methods. In practical terms, this approach enhances precision agriculture by enabling more accurate monitoring of leaf growth dynamics, which is essential for crop management and yield optimization. This finding presents opportunities for further application across other durian cultivars and the broader adoption of similar methods in other plant commodities within the context of precision agriculture and plant growth modeling.

##plugins.themes.academic_pro.article.details##

Author Biographies

Bayu Dwi Arfiyanto, University of Pekalongan

Department of Agrotechnology, Faculty of Agriculture

Farchan Mushaf Al Ramadhani, University of Pekalongan

Department of Agrotechnology, Faculty of Agriculture

Sajuri, University of Pekalongan

Department of Agrotechnology, Faculty of Agriculture

How to Cite
1.
Arfiyanto BD, Ramadhani FMA, Sajuri S. Analysis of Constant Values of Leaves of the Durian Cultivars Monthong and Bawor Using Digital Image Processing. J. appl. agricultural sci. technol. [Internet]. 2025Aug.28 [cited 2025Aug.31];9(3):449-63. Available from: https://jaast.org/index.php/jaast/article/view/462

References

  1. Ketsa S. Durian - Durio zibethinus. In: Rodrigues S, Silva EDO, Brito ES De, editors. Exotic Fruits Reference Guide, London: Elsevier Inc 2018:169–80. https://doi.org/10.1016/B978-0-12-803138-4.00022-8.
  2. Khasan U, Ambar S, Sukma I. Correspondence analysis in forming a consumer image map of durian fruit in Wonosalam, Jombang Regency. Tuijin Jishu/Journal of Propulsion Technology 2024;45:478–82. https://www.propulsiontechjournal.com/index.php/journal/article/view/3993
  3. Arifah AH, Faizah M. Financial feasibility analysis of durian fruit business (Durio zibethinus). MULTIDISCIPLINE - International Conference 2021 2021:111–118. https://ejournal.unwaha.ac.id/index.php/ICMT/article/view/2209
  4. Arsa S, Wipatanawin A, Suwapanich R, Makkerdchoo O, Chatsuwan N, Kaewthong P, et al. Properties of different varieties of durian. Applied Sciences 2021;11:1–19. https://doi.org/10.3390/app11125653.
  5. Wahab L, Kurniawan A, Lestari HA. Evaluasi kesesuaian lahan untuk budidaya durian bawor di Kabupaten Banyumas menggunakan SIG berbasis IoT. Jurnal Ilmiah Rekayasa Pertanian Dan Biosistem 2025;13:83–101. https://doi.org/10.29303/jrpb.v13i1.1138.
  6. Sari VK, Sa’diyah H, Basuki. Morpho-Ecotype characterization of superior local durian (Durio zibethinus L.) in Jember Regency. J Trop Biodivers Biotechnol 2024;9:1–11. https://doi.org/10.22146/jtbb.87810.
  7. Ketsa S, Wisutiamonkul A, Palapol Y, Paull RE. The durian: Botany, horticulture, and utilization. New Jersey: John Wiley & Sons Inc 2020. https://doi.org/10.1002/9781119625407.ch4.
  8. Roth-Nebelsick A, Krause M. The plant leaf: A biomimetic resource for multifunctional and economic design. Biomimetics 2023;8:1–32. https://doi.org/10.3390/biomimetics8020145.
  9. Mao J, Luo Y, Jin C, Xu M, Li X, Tian Y. Response of leaf photosynthesis–transpiration coupling to biotic and abiotic factors in the typical desert shrub Artemisia ordosica. Sustainability 2023;15:1–13. https://doi.org/10.3390/su151310216.
  10. Lv Y, Gu L, Man R, Liu X, Xu J. Response of stomatal conductance, transpiration, and photosynthesis to light and CO2 for rice leaves with different appearance days. Front Plant Sci 2024;15:1–13. https://doi.org/10.3389/fpls.2024.1397948.
  11. Hatfield JL, Dold C. Photosynthesis in the solar corridor system. In: Deichman CL, Kremer RJ, editors. The Solar Corridor Crop System: Implementation and Impacts, London: Elsevier Inc 2019:1–33. https://doi.org/10.1016/B978-0-12-814792-4.00001-2.
  12. Zhang H, Wang L, Jin X, Bian L, Ge Y. High-throughput phenotyping of plant leaf morphological, physiological, and biochemical traits on multiple scales using optical sensing. Crop Journal 2023;11:1303–18. https://doi.org/10.1016/j.cj.2023.04.014.
  13. Osone Y, Ishida A, Tateno M. Correlation between relative growth rate and specific leaf area requires associations of specific leaf area with nitrogen absorption rate of roots. New Phytologist 2008;179:417–27. https://doi.org/10.1111/j.1469-8137.2008.02476.x.
  14. Xu R, Wang L, Zhang J, Zhou J, Cheng S, Tigabu M, et al. Growth rate and leaf functional traits of four broad-leaved species underplanted in Chinese fir plantations with different tree density levels. Forests 2022;13:1–13. https://doi.org/10.3390/f13020308.
  15. Ma J, Zhang J, Wang J, Khromykh V, Li J, Zhong X. Global leaf area index research over the past 75 years: A comprehensive review and bibliometric analysis. Sustainability 2023;15:1–30. https://doi.org/10.3390/su15043072.
  16. Shen B, Guo J, Li Z, Chen J, Fang W, Kussainova M, et al. Comparative verification of leaf area index products for different grassland types in Inner Mongolia, China. Remote Sens (Basel) 2023;15:1–17. https://doi.org/10.3390/rs15194736.
  17. Montgomery EG, Montgomery MB, Correlation studies in corn. Annual report no. 24. Agricultural Experimental Station. Lincoln, NE, USA.: 1911. https://www.scienceopen.com/document?vid=eaf0a177-85e8-4907-82f3-93a00b699ea0
  18. Al Ramadhani FM, Sajuri, Amin R, Lutfiana A. Metode pengukuran luas daun tanaman menggunakan bantuan objek tuntun berbasis pengolahan citra digital. Jurnal Pertanian Agros 2024;26:1677–88. https://doi.org/10.37159/jpa.v26i4.4832.
  19. Wei H, Deng Y, Chen Z, Wang X, Li X. Prediction of leaf area using Montgomery models in ramie. Forest Chemicals Review 2021:1162–76. https://www.forestchemicalsreview.com/index.php/JFCR/article/view/272/258
  20. Al Ramadhani FM. Identifikasi nilai konstanta daun tanaman rambutan dan jambu air berbasis pengolahan citra digital. Jurnal Penelitian Inovatif (JUPIN) 2024;4:655–64. https://doi.org/10.54082/jupin.400.
  21. Shi P, Liu M, Ratkowsky DA, Gielis J, Su J, Yu X, et al. Leaf area–length allometry and its implications in leaf shape evolution. Trees 2019;33:1073–85. https://doi.org/10.1007/s00468-019-01843-4.
  22. Anitha K, Sharathkumar M, Kumar PJ, Jegadeeswari V. A simple, non-destructive method of leaf area estimation in Lisianthus, Eustoma grandiflorum (Raf). Shinn. Current Biotica 2016;9:313–21. https://www.cabidigitallibrary.org/doi/pdf/10.5555/20163237245
  23. Nakanwagi MJ, Sseremba G, Kabod NP, Masanza M, Kizito EB. Accuracy of using leaf blade length and leaf blade width measurements to calculate the leaf area of Solanum aethiopicum Shum group. Heliyon 2018;4:1–12. https://doi.org/10.1016/j.heliyon.2018.e01093.
  24. Yu X, Shi P, Schrader J, Niklas KJ. Nondestructive estimation of leaf area for 15 species of vines with different leaf shapes. Am J Bot 2020;107:1481–90. https://doi.org/10.1002/ajb2.1560.
  25. Sala F, Arsene GG, Iordănescu O, Boldea M. Leaf area constant model in optimizing foliar area measurement in plants: A case study in apple tree. Sci Hortic 2015;193:218–24. https://doi.org/10.1016/j.scienta.2015.07.008.
  26. Gokkus G, Gokkus MK. Leaf area estimation based on ANFIS using embedded system and PV panel. Heliyon 2024;10:1–10. https://doi.org/10.1016/j.heliyon.2024.e34149.
  27. Koyama K. Leaf area estimation by photographing leaves sandwiched between transparent clear file folder sheets. Horticulturae 2023;9:1–20. https://doi.org/10.3390/horticulturae9060709.
  28. Brant V, Krofta K, Zábranský P, Hamouz P, Procházka P, Dreksler J, et al. Relationship between dynamics of plant biometric parameters and leaf area index of Hop (Humulus lupulus L.) plants. Agronomy 2025;15:1–16. https://doi.org/10.3390/agronomy15040823.
  29. Rozentsvet O, Bogdanova E, Nesterov V, Bakunov A, Milekhin A, Rubtsov S, et al. Physiological and biochemical parameters of leaves for evaluation of the potato yield. Agriculture 2022;12:1–13. https://doi.org/10.3390/agriculture12060757.
  30. Al Ramadhani FM, Bowo C, Slameto S. The use of aquacrop model for soybean in various water availability within a lysimeter system. Journal of Applied Agricultural Science and Technology 2023;7:399–413. https://doi.org/10.55043/jaast.v7i4.153.
  31. Sala F, Dobrei A, Herbei MV. Leaf area calculation models for vines based on foliar descriptors. Plants 2021;10:1–15. https://doi.org/10.3390/plants10112453.
  32. Ferreira T, Rasband W. ImageJ User Guide IJ 1.46r. Kanada: National Institute of Health; 2012. https://imagej.net/ij/docs/guide/
  33. Kasuya E. On the use of r and r squared in correlation and regression. Ecol Res 2019;34:235–6. https://doi.org/10.1111/1440-1703.1011.
  34. Zhang J, Cheng J, Liu C, Wu Q, Xiong S, Yang H, et al. Enhanced crop leaf area index estimation via random forest regression: Bayesian optimization and feature selection approach. Remote Sens (Basel) 2024;16:1–21. https://doi.org/10.3390/rs16213917.
  35. Yadav SS, Kumar A, Johri P, Singh JN. Testing effort-dependent software reliability growth model using time lag functions under distributed environment. In: Johri P, Anand A, Vain J, Singh J, Quasim MT, editors. System Assurances Modeling and Management, London: Academic Press; 2022, p. 85–102. https://doi.org/10.1016/B978-0-323-90240-3.00006-0.
  36. Chai T, Draxler RR. Root mean square error (RMSE) or mean absolute error (MAE)? –Arguments against avoiding RMSE in the literature. Geosci Model Dev 2014;7:1247–50. https://doi.org/10.5194/gmd-7-1247-2014.
  37. Iida T. Identifying causes of errors between two wave-related data using performance metrics. Applied Ocean Research 2024;148:1–13. https://doi.org/10.1016/j.apor.2024.104024.
  38. Neves VH, Pace G, Delegido J, Antunes SC. Chlorophyll and suspended solids estimation in Portuguese reservoirs (Aguieira and Alqueva) from Sentinel-2 imagery. Water (Basel) 2021;13:1–21. https://doi.org/10.3390/w13182479.
  39. Nash JE, Sutcliffe J V. River flow forecasting through conceptual models part I — A discussion of principles. J Hydrol (Amst) 1970;10:282–90. https://doi.org/10.1016/0022-1694(70)90255-6.
  40. Shi P, Liu M, Yu X, Gielis J, Ratkowsky DA. Proportional relationship between leaf area and the product of leaf length and width of four types of special leaf shapes. Forests 2019;10:1–13. https://doi.org/10.3390/f10020178.
  41. Ren J, Ji X, Wang C, Hu J, Nervo G, Li J. Variation and genetic parameters of leaf morphological traits of eight families from Populus simonii × P. nigra. Forests 2020;11:1–17. https://doi.org/10.3390/f11121319.
  42. Li Y, Zhang Y, Liao P, Wang T, Wang X, Ueno S, et al. Genetic, geographic, and climatic factors jointly shape leaf morphology of an alpine oak, Quercus aquifolioides Rehder & E.H. Wilson. Ann For Sci 2021;78:1–18. https://doi.org/10.1007/s13595-021-01077-w.
  43. Nakayama H. Leaf form diversity and evolution: a never-ending story in plant biology. J Plant Res 2024;137:547–60. https://doi.org/10.1007/s10265-024-01541-4.
  44. Schrader J, Shi P, Royer DL, Peppe DJ, Gallagher RV, Li Y, et al. Leaf size estimation based on leaf length, width and shape. Ann Bot 2021;128:395–406. https://doi.org/10.1093/aob/mcab078.
  45. Tay AC, Ling JZL. Estimation of individual leaf area by leaf dimension using a linear regression for various tropical plant species. IOP Conf Ser Mater Sci Eng 2020;943:1–5. https://doi.org/10.1088/1757-899X/943/1/012066.
  46. Liu H, Xiang Y, Chen J, Wu Y, Du R, Tang Z, et al. A new spectral index for monitoring leaf area index of winter oilseed rape (Brassica napus L.) under different coverage methods and nitrogen treatments. Plants 2024;13:1–16. https://doi.org/10.3390/plants13141901.
  47. Reza MN, Chowdhury M, Islam S, Kabir MSN, Park SU, Lee GJ, et al. Leaf area prediction of pennywort plants grown in a plant factory using image processing and an artificial neural network. Horticulturae 2023;9:1–17. https://doi.org/10.3390/horticulturae9121346.
  48. Benjamin LR. Growth Analysis, Crops. In : Second Edi, editors. Amsterdam: Elsevier; 2017, p. 23-28. https://doi.org/10.1016/B978-0-12-394807-6.00225-2.
  49. Guo S, Wu L, Cao X, Sun X, Cao Y, Li Y, et al. Simulation model construction of plant height and leaf area index based on the overground weight of greenhouse tomato: Device development and application. Horticulturae 2024;10:1–16. https://doi.org/10.3390/horticulturae10030270.
  50. Jo WJ, Shin JH. Effect of leaf-area management on tomato plant growth in greenhouses. Hortic Environ Biotechnol 2020;61:981–8. https://doi.org/10.1007/s13580-020-00283-1.
  51. Raya V, Parra M, Cid M del C, Santos B, Ríos D. Effect of different intensities of leaf removal on tomato development and yield. Horticulturae 2024;10:1–21. https://doi.org/10.3390/horticulturae10111136.
  52. Bowman CS, Traband R, Wang X, Knowles SP, Lo S, Jia Z, et al. Multiple Leaf Sample Extraction System (MuLES): A tool to improve automated morphometric leaf studies. Appl Plant Sci 2023;11:1–7. https://doi.org/10.1002/aps3.11513.
  53. Lei G, Zeng W, Yu J, Huang J. A comparison of physical-based and machine learning modeling for soil salt dynamics in crop fields. Agricultural Water Manag 2023;277:1–16. https://doi.org/10.1016/j.agwat.2022.108115.
  54. Valbuena R, Hernando A, Manzanera JA, Görgens EB, Almeida DRA, Silva CA, et al. Evaluating observed versus predicted forest biomass: R-squared, index of agreement or maximal information coefficient? Eur J Remote Sens 2019;52:1–14. https://doi.org/10.1080/22797254.2019.1605624.
  55. Jierula A, Wang S, Oh TM, Wang P. Study on accuracy metrics for evaluating the predictions of damage locations in deep piles using artificial neural networks with acoustic emission data. Applied Sciences 2021;11:1–21. https://doi.org/10.3390/app11052314.
  56. Blanc É. Statistical emulators of maize, rice, soybean and wheat yields from global gridded crop models. Agric For Meteorol 2017;236:145–61. https://doi.org/10.1016/j.agrformet.2016.12.022.
  57. Kothari K, Battisti R, Boote KJ, Archontoulis S V, Confalone A, Constantin J, et al. Evaluating differences among crop models in simulating soybean in-season growth. Field Crops Res 2024;309:109306. https://doi.org/10.1016/j.fcr.2024.109306.
  58. Pandya P, Gontia NK. Early crop yield prediction for agricultural drought monitoring using drought indices, remote sensing, and machine learning techniques. Journal of Water and Climate Change 2023;14:4729–4746. https://doi.org/10.2166/wcc.2023.386.
  59. Aslam FM, Afghani FA. Comparing monthly rainfall prediction in West Sumatra using SARIMA, ETS, LSTM, and XGBoosting methods. Indonesian Journal of Applied Statistics 2024;7:14–26. https://doi.org/10.13057/ijas.v7i1.83187.
  60. Mota MC, Candido LA, Cuadra SV, Marenco RA, de Souza RVA, Maito Tomé A, et al. CROPGRO-soybean model – Validation and application for the southern Amazon, Brazil. Comput Electron Agric 2024;216:108478. https://doi.org/10.1016/j.compag.2023.108478.
  61. Cao HX, Hanan JS, Liu Y, Liu YX, Yue Y Bin, Zhu DW, et al. Comparison of crop model validation methods. J Integr Agric 2012;11:1274–85. https://doi.org/10.1016/S2095-3119(12)60124-5.
  62. Fitriani V, Bowo C, Mandala M, Gandri L. Comparison of empirical methods to estimated reference evapotranspiration. Jurnal Ilmiah Rekayasa Pertanian Dan Biosistem 2024;12:177–92. https://doi.org/10.29303/jrpb.v12i2.629.