Skip to main navigation menu Skip to main content Skip to site footer
Plant Science Today (PST)

Research Articles

Vol. 12 No. sp4 (2025): Recent Advances in Agriculture by Young Minds - III

Detection of nutrient deficiency in plants using radial basis function neural network with a modified adaptive Kalman filter

DOI
https://doi.org/10.14719/pst.8380
Submitted
19 March 2025
Published
23-10-2025

Abstract

The agriculture sector plays a significant role in the national GDP. Global agricultural industries employ advanced techniques for the early detection and characterisation of plant nutrient deficiencies to enhance crop yield. In most agriculture-dependent countries, economic growth is determined by the gross yield of entire plantations. Plants exhibit noticeable symptoms such as changes in leaf colouration, spotting, or altered growth patterns. These symptoms, including yellowing (chlorosis), browning, or stunted growth, indicate potential nutrient deficiencies. Monitoring these variations enables early detection of deficiencies before they significantly affect plant health. Early detection of nutrient deficiencies plays a pivotal role in achieving a speedy recovery from the disease, thereby assuring sufficient improvement in the plant’s yield. This article proposes a method for the early detection of nutrient deficiency in plants based on variations in leaf colour and pattern. It uses 54305 data points with three versions of the same images, differing in colour, greyscale and segmentation. Initially, image preprocessing is carried out using Fuzzy Histogram Equalization (FHE) and a modified adaptive Kalman filter to achieve better results. The pre-processed image datasets are then used to train the proposed Radial Basis Function Neural Network (RBFNN). The proposed RBFNN model demonstrates significant improvements (1.18-16.47 %) in accuracy, precision, specificity, sensitivity and F-score compared to conventional models.

References

  1. 1. Passino KM. Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Systems Magazine. 2002;22(3):52–67. https://doi.org/10.1109/MCS.2002.1004010
  2. 2. Strange RN, Scott PR. Plant disease: A threat to global food security. Annual Review of Phytopathology. 2005;43(1):83–116. https://doi.org/10.1146/annurev.phyto.43.113004.133839
  3. 3. Barbedo JG. Detection of nutrition deficiencies in plants using proximal images and machine learning: A review. Computers and Electronics in Agriculture. 2019;162:482–92. https://doi.org/10.1016/j.compag.2019.04.035
  4. 4. Azimi S, Kaur T, Gandhi TK. A deep learning approach to measure stress level in plants due to nitrogen deficiency. Measurement. 2021;173:108650. https://doi.org/10.1016/j.measurement.2020.108650
  5. 5. Elvanidi K, Augoustaki L. Crop reflectance measurements for nitrogen deficiency detection in a soilless tomato crop. Biosystems Engineering. 2018;176:1–11.
  6. https://doi.org/10.1016/j.biosystemseng.2018.09.019
  7. 6. Rustioni L, Grossi D, Brancadoro L, Failla O. Iron, magnesium, nitrogen and potassium deficiency symptom discrimination by reflectance spectroscopy in grapevine leaves. Scientia Horticulturae. 2018;241:152–9. https://doi.org/10.1016/j.scienta.2018.06.097
  8. 7. Chouhan SS, Kaul A, Singh UP, Jain S. Bacterial foraging optimization based radial basis function neural network (BRBFNN) for identification and classification of plant leaf diseases: An automatic approach towards plant pathology. IEEE Access. 2018;6:8852–63. https://doi.org/10.1109/ACCESS.2018.2800685
  9. 8. Montes Condori RH, Romualdo LM, Martinez Bruno O, de Cerqueira Luz PH. Comparison between traditional texture methods and deep learning descriptors for detection of nitrogen deficiency in maize crops. In: 2017 Workshop of Computer Vision; 2017. p. 7–12. https://doi.org/10.1109/WVC.2017.00009
  10. 9. Hairuddin MA, Tahir NM, Baki SR. Overview of image processing approach for nutrient deficiencies detection in Elaeis guineensis. In: Proceedings of IEEE International Conference on System Engineering and Technology; 2011. p. 116–20. https://doi.org/10.1109/ICSEngT.2011.5993432
  11. 10. Noh H, Zhang Q. Shadow effect on multi-spectral image for detection of nitrogen deficiency in corn. Computers and Electronics in Agriculture. 2012;83:52–7. https://doi.org/10.1016/j.compag.2012.01.014
  12. 11. Romualdo LM, Luz PH, Devechio FF, Marin MA, Zúñiga AM, Bruno OM, et al. Use of artificial vision techniques for diagnostic of nitrogen nutritional status in maize plants. Computers and Electronics in Agriculture. 2014;104:63–70. https://doi.org/10.1016/j.compag.2014.03.009
  13. 12. Shah A, Gupta P, Ajgar YM. Macro-nutrient deficiency identification in plants using image processing and machine learning. In: Proceedings of 3rd International Conference for Convergence in Technology; 2018. p. 1–4. https://doi.org/10.1109/I2CT.2018.8529789
  14. 13. Zermas D, Nelson HJ, Stanitsas P, Morellas V, Mulla DJ, Papanikolopoulos N. A methodology for the detection of nitrogen deficiency in corn fields using high-resolution RGB imagery. IEEE Transactions on Automation Science and Engineering. 2021;18(4):1967–77. https://doi.org/10.1109/TASE.2020.3022868
  15. 14. Moghe R, Zanetti R, Akella MR. Adaptive Kalman filter for detectable linear time-invariant systems. Journal of Guidance, Control and Dynamics. 2019;42(8):1767–75. https://doi.org/10.2514/1.G004359
  16. 15. Huang Y, Zhang Y, Wu Z, Li N, Chambers J. A novel adaptive Kalman filter with inaccurate process and measurement noise covariance matrices. IEEE Transactions on Automatic Control. 2018;63(2):594–601. https://doi.org/10.1109/TAC.2017.2730480
  17. 16. Zidan KA, Jumaa SS. Finger vein recognition using two parallel enhancement approaches based fuzzy histogram equalization. Periodicals of Engineering and Natural Sciences. 2019;7(1):514–29. https://doi.org/10.21533/pen.v7i1.434
  18. 17. Mirbolouk S, Valizadeh M, Amirani MC, Choukali MA. A fuzzy histogram weighting method for efficient image contrast enhancement. Multimedia Tools and Applications. 2021;80(2):2221-41. https://doi.org/10.1007/s11042-020-09801-w
  19. 18. Magudeeswaran V, Ravichandran CG. Fuzzy logic-based histogram equalization for image contrast enhancement. Mathematical Problems in Engineering. 2013;2013:891864. https://doi.org/10.1155/2013/891864
  20. 19. Sun Y, Tong C, He S, Wang K, Chen L. Identification of nitrogen, phosphorus and potassium deficiencies based on temporal dynamics of leaf morphology and colour. Sustainability. 2018;10(3):762. https://doi.org/10.3390/su10030762
  21. 20. Hassan SK, Maji AK, Jasiński M, Leonowicz Z, Jasińska E. Identification of plant-leaf diseases using CNN and transfer-learning approach. Electronics. 2021;10(12):1388. https://doi.org/10.3390/electronics10121388
  22. 21. Mukherjee A. Analysis of diseased leaf images using digital image processing techniques and SVM classifier and disease severity measurements using fuzzy logic. International Journal of Science and Engineering Research. 2020;11(8):1905–12.
  23. https://doi.org/10.14299/ijser.2020.08.12
  24. 22. Chouhan SS, Kaul A, Singh UP. Radial basis function neural network for the segmentation of plant leaf disease. In: Proceedings of 4th International Conference on Information Systems and Computer Networks; 2019. p. 713–6. https://doi.org/10.1109/ISCON47742.2019.9036299
  25. 23. Oskoei AM. Adaptive Kalman filter applied to vision based head gesture tracking for playing video games. Robotics. 2017;6(4):33. https://doi.org/10.3390/robotics6040033
  26. 24. Naikwadi S, Amoda N. Advances in image processing for detection of plant diseases. International Journal of Applied Innovation in Engineering and Management. 2013;2(11):206–12.

Downloads

Download data is not yet available.