Measurement of Photosynthetic Pigment Content using Convolutional Neural Network

Authors

  • Imam Dwi Rezeki Universitas Labuhanbatu, Indonesia
  • Fitri Aini Nasution Universitas Labuhanbatu, Indonesia
  • Angga Putra Juledi Universitas Labuhanbatu, Indonesia

DOI:

10.33395/sinkron.v7i2.11414

Abstract

Estimation of photosynthetic pigment levels from leaves can be done using conventional methods using laboratory equipment such as spectrophotometers and using digital image processing from leaf images with a computational model. In digital image processing methods, various models are used, such as neural network, CNN, and linear regression. Measurement of photosynthetic pigment levels using image processing methods uses color value data from image data as input to the model used. In this study, we will analyze the effect of various types of color space and inpaint preprocessing settings on the accuracy of the CNN model in measuring leaf photosynthetic pigment levels. The color space types being tested are 4 single color spaces RGB, HSV, LAB, and YCbCr, as well as 6 color combination spaces RGB+HSV, RGB+LAB, RGB+YCbCr, HSV+LAB, HSV+YCbCr, and LAB+YCbCr. The choice of the type of color space takes into account the phenomenon of color constancy and the characteristics of the color space on the lighting elements. In addition, image data is divided into two types, namely through inpaint preprocessing and not, so that in total there are 20 types of input data. After the CNN model training process with various types of color spaces and different preprocessing settings as input data, observations were made on the accuracy values, namely the training MAE and the validation MAE for each model. From 20 types of input data, 3 types of input data are obtained which are recommended as input data that provide the best model accuracy value based on MAE validation with values ​​of 0.08761, 0.09252, and 0.09288. The three recommended input data from the sequence of accuracy values ​​are RGB+LAB without inpaint, RGB with inpaint, and LAB+YCbCr without inpaint.

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References

Bezden, T., & Bacanin, N. (2019). Convolutional Neural Network Layers and Architectures. SINTEZA 2019: International Scientific Conference on Information Technology and Data Related Research, (January), 445–451. https://doi.org/10.15308/sinteza-2019-445-451

Bueno, G. E., Valenzuela, K. A., & Arboleda, E. R. (2020). Maturity classification of cacao through spectrogram and convolutional neural network. Jurnal Teknologi Dan Sistem Komputer, 8(3), 228–233. https://doi.org/10.14710/jtsiskom.2020.13733

Chou, S., Chen, B., Chen, J., Wang, M., Wang, S., Croft, H., & Shi, Q. (2020). Estimation of leaf photosynthetic capacity from the photochemical reflectance index and leaf pigments. Ecological Indicators, 110, 105867. https://doi.org/https://doi.org/10.1016/j.ecolind.2019.105867

Concepcion, R. S., Lauguico, S. C., Tobias, R. R., Dadios, E. P., Bandala, A. A., & Sybingco, E. (2020). Estimation of Photosynthetic Growth Signature at the Canopy Scale Using New Genetic Algorithm-Modified Visible Band Triangular Greenness Index. 2020 International Conference on Advanced Robotics and Intelligent Systems (ARIS), 1–6. https://doi.org/10.1109/ARIS50834.2020.9205787

Dewi, C., Santoso, A., Indriati, I., Dewi, N. A., & Arbawa, Y. K. (2021). Evaluasi Performasi Ruang Warna pada Klasifikasi Diabetic Retinophaty Menggunakan Convolution Neural Network. Jurnal Teknologi Informasi Dan Ilmu Komputer, 8(3), 619. https://doi.org/10.25126/jtiik.2021834459

Fitriyah, H., & Maulana, R. (2021). Deteksi Gulma Berdasarkan Warna HSV dan Fitur Bentuk Menggunakan Jaringan Syaraf Tiruan. Jurnal Teknologi Informasi Dan Ilmu Komputer, 8(5), 929. https://doi.org/10.25126/jtiik.2021854719

Foster, D. H. (2011). Color constancy. Vision Research, 51(7), 674–700. https://doi.org/10.1016/j.visres.2010.09.006

Gunawan, I. K., Bayupati, I. P. A., Wibawa, K. S., Sukarsa, I. M., & Kurniawan, L. A. (2021). Indonesian Plate Number Identification Using YOLACT and Mobilenetv2 in the Parking Management System. JUITA: Jurnal Informatika, 9(1), 69. https://doi.org/10.30595/juita.v9i1.9230

Hurlbert, A. (2019). Challenges to color constancy in a contemporary light. Current Opinion in Behavioral Sciences, 30, 186–193. https://doi.org/10.1016/j.cobeha.2019.10.004

Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 147, 70–90. https://doi.org/https://doi.org/10.1016/j.compag.2018.02.016

Kim, H. K., Park, J. H., & Jung, H. Y. (2018). An Efficient Color Space for Deep-Learning Based Traffic Light Recognition. Journal of Advanced Transportation, 2018. https://doi.org/10.1155/2018/2365414

Kučerová, K., Henselová, M., Slováková, Ľ., & Hensel, K. (2019). Effects of plasma activated water on wheat: Germination, growth parameters, photosynthetic pigments, soluble protein content, and antioxidant enzymes activity. Plasma Processes and Polymers, 16(3), 1800131. https://doi.org/https://doi.org/10.1002/ppap.201800131

Morales, F., Ancín, M., Fakhet, D., González-Torralba, J., Gámez, A. L., Seminario, A., … Aranjuelo, I. (2020). Photosynthetic Metabolism under Stressful Growth Conditions as a Bases for Crop Breeding and Yield Improvement. Plants , Vol. 9. https://doi.org/10.3390/plants9010088

Nugroho, B., & Puspaningrum, E. Y. (2021). Kinerja Metode CNN untuk Klasifikasi Pneumonia dengan Variasi Ukuran Citra Input. Jurnal Teknologi Informasi Dan Ilmu Komputer, 8(3), 533. https://doi.org/10.25126/jtiik.2021834515

Odabas, M. S., Simsek, H., Lee, C. W., & İseri, İ. (2017). Multilayer Perceptron Neural Network Approach to Estimate Chlorophyll Concentration Index of Lettuce (Lactuca sativa L.). Communications in Soil Science and Plant Analysis, 48(2), 162–169. https://doi.org/10.1080/00103624.2016.1253726

Priyangka, A. A. J. V., & Kumara, I. M. S. (2021). Classification Of Rice Plant Diseases Using the Convolutional Neural Network Method. Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, 12(2), 123. https://doi.org/10.24843/lkjiti.2021.v12.i02.p06

Sonobe, R., Miura, Y., Sano, T., & Horie, H. (2018). Monitoring Photosynthetic Pigments of Shade-Grown Tea from Hyperspectral Reflectance. Canadian Journal of Remote Sensing, 44(2), 104–112. https://doi.org/10.1080/07038992.2018.1461555

Tommy, T., Siregar, R., Elhanafi, A. M., & Lubis, I. (2021). Implementasi Color Quantization pada Kompresi Citra Digital dengan Menggunakan Model Clustering Berdasarkan Nilai Max Variance pada Ruang Warna RGB. Jurnal Teknologi Informasi Dan Ilmu Komputer, 8(6), 1099. https://doi.org/10.25126/jtiik.2021863490

Zhang, J., Zhang, D., Cai, Z., Wang, L., Wang, J., Sun, L., … Zhao, J. (2022). Spectral technology and multispectral imaging for estimating the photosynthetic pigments and SPAD of the Chinese cabbage based on machine learning. Computers and Electronics in Agriculture, 195, 106814. https://doi.org/https://doi.org/10.1016/j.compag.2022.106814

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How to Cite

Rezeki, I. D., Nasution, F. A. ., & Juledi, A. P. (2022). Measurement of Photosynthetic Pigment Content using Convolutional Neural Network . Sinkron : Jurnal Dan Penelitian Teknik Informatika, 6(2), 611-618. https://doi.org/10.33395/sinkron.v7i2.11414

Issue

Vol. 6 No. 2 (2022): Articles Research Volume 6 Issue 2, April 2022

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Articles

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Copyright (c) 2022 Imam Dwi Rezeki, Fitri Aini Nasution

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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