An Agricultural Tele-Monitoring Method in Detecting Nutrient Deficiencies of Oil Palm Leaf

  • Authors

    • Muhammad Asraf H.
    • Nooritawati Md Tahir
    • Nur Dalila K.A.
    • Aini Hussain
    2018-10-02
    https://doi.org/10.14419/ijet.v7i4.11.21390
  • Oil palm, deficiencies detection, machine learning classifier, SVM (Support Vector Machine), leaf disease.
  • Nutrient management in oil palm plantation is considered as one of the prominent issues especially for smallholder farmer. The nutrient contained in the tress has always been neglected and untreated and these may cause the trees to suffer from nutrient deficiencies. Therefore, in leveraging the oil yield at the maximum, a telemonitoring system is developed to assess and monitor the lack of nutrients for respective trees. This is done using image processing technique and artificial intelligence in detecting the nutritional deficiencies by analyzing the leaf. The categorization focused by classifying into four major types either as magnesium deficiencies, potassium deficiencies, nitrogen deficiencies or healthy that is based on the oil palm’s leaf surface. This is achieved by extracting the features namely number of red pixels, entropy and correlations. Further, two classifiers specifically support vector machine and artificial neural network is used for classification purpose along with performance measure using accuracy(ACC), Mean Square Error (MSE), Mean Absolute Error (MAE), Sensitivity (SN), Specificity (SP), Positive Predictive Value (PPV), Negative Predictive Value (NPV) based on ten-fold cross-validation. Results attained showed that the best classifier is SVM using RBF kernel (SVM-RBF) that is capable to accurately recognize the nutrient deficiencies with 100% accuracy.

     


  • References

    1. [1] M. J. Webb, P. N. Nelson, L. G. Rogers, and G. N. Curry, "Site specific fertilizer recommendations for oil palm smallholders using information from large plantations", Journal of Plant Nutrition and Soil Science, 174(2), (2011), 311-320.

      [2] B. O. A. Adebowale, "Innovation policies and sector development in Nigeria's oil palm industry: Lessons from Malaysia", International Journal of Learning and Intellectual Capital, 14(2), (2017), 135-153.

      [3] S. Bashir and N. Sharma, "Remote area plant disease detection using image processing", IOSR Journal of Electronics and Communication Engineering, 1(6), (2012), 31-34.

      [4] S. Phadikar, J. Sil, and A. K. Das, "Rice diseases classification using feature selection and rule generation techniques", Computers and Electronics in Agriculture, 90, (2013), 76-85.

      [5] A. N. Rathod, B. A. Tanawala, and V. H. Shah, "Leaf disease detection using image processing and neural network", International Journal of Advance Engineering and Research Development, 1(6), (2014), 2348-640.

      [6] M. Bhange and H. Hingoliwala, "Smart farming: Pomegranate disease detection using image processing", Procedia Computer Science, 58, (2015), 280-288.

      [7] Y. Xia, Y. Li, and C. Li, "Intelligent diagnose system of wheat diseases based on android phone", Journal of Information and Computational Science, 12(18), (2015), 6845-6852.

      [8] A. K. Dey, M. Sharma, and M. Meshram, "Image processing based leaf rot disease, detection of betel vine (Piper BetleL.)", Procedia Computer Science, 85, (2016), 748-754.

      [9] R. Oberti, M. Marchi, P. Tirelli, A. Calcante, M. Iriti, and A. N. Borghese, "Automatic detection of powdery mildew on grapevine leaves by image analysis: Optimal view-angle range to increase the sensitivity", Computers and Electronics in Agriculture, 104, (2014), 1-8.

      [10] H. Sahin and A. Subasi, "Classification of the cardiotocogram data for anticipation of fetal risks using machine learning techniques", Applied Soft Computing, 33, (2015), 231-238.

      [11] X. Yang, Q. Yu, L. He, and T. Guo, "The one-against-all partition based binary tree support vector machine algorithms for multi-class classification", Neurocomputing, 113, (2013), 1-7.

      [12] L. Lazli and M. Boukadoum, "Hidden neural network for complex pattern recognition: A comparison study with multi-neural network based approach", International Journal of Life Science and Medical Research, 3(6), (2013), 234-245.

      [13] M. A. Hairuddin, "Automated vision recognition for classifying nutrient deficiencies based of Elaeis Guineensis leaf", PhD thesis, Universiti Teknologi MARA (2014).

      [14] K. Dalila, H. M. Asraf, A. Iffah, K. Kamarudin, M. Nooritawati, and Rizam M.S.B, "Elaeis Guineensis image recognition and analysis of characterizing nutrient deficiency", Advanced Science Letters, 23(6), (2017), 5536-5541.

      [15] Johannes, A., A. Picon, A. A. Gila, J. Echazarra, S. R. Vaamonde, A. D. Navajas, and A. O. Barredo, "Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case", Computers and Electronics in Agriculture, 138, (2017), 200-209.

  • Downloads

  • How to Cite

    Asraf H., M., Md Tahir, N., Dalila K.A., N., & Hussain, A. (2018). An Agricultural Tele-Monitoring Method in Detecting Nutrient Deficiencies of Oil Palm Leaf. International Journal of Engineering & Technology, 7(4.11), 266-270. https://doi.org/10.14419/ijet.v7i4.11.21390