Rancang Bangun Aplikasi Berbasis Android untuk Perbaikan Kualitas Citra Tanaman Atas Perbedaan Spesifikasi Kamera Smartphone pada Prediksi Kandungan Pigmen Fotosintesis Secara Real-Time

Penulis

  • Felix Adrian Tjokro Atmodjo Universitas Ma Chung, Malang
  • Kestrilia Rega Prilianti Universitas Ma Chung, Malang
  • Hendry Setiawan Universitas Ma Chung, Malang

DOI:

https://doi.org/10.25126/jtiik.2022976771

Abstrak

Pigmen utama yang berperan penting pada fotosintesis, yaitu klorofil, karotenoid dan antosianin dapat dianalisis kandungannya untuk menentukan status kesehatan tanaman. Metode analisis kandungan pigmen yang dilakukan secara destruktif memerlukan penanganan khusus dan biaya yang tinggi. Fuzzy Piction adalah aplikasi Android yang telah dikembangkan sebelumnya untuk prediksi kandungan pigmen utama pada tanaman. Aplikasi tersebut mempunyai kemampuan untuk melakukan prediksi kandungan pigmen pada citra daun secara non-destruktif dengan menggunakan model Convolutional Neural Network (CNN) FP3Net. Namun, Fuzzy Piction masih belum invarian terhadap perbedaan kualitas citra yang dapat terjadi karena perbedaan kualitas atau spesifikasi kamera smartphone. Hal ini ditunjukkan dengan adanya perbedaan hasil prediksi kandungan pigmen pada beberapa smartphone untuk objek daun yang sama. Pada penelitian ini dikembangkan metode perbaikan citra dengan algoritma Partial Least Square Regression (PLSR) sebagai solusi atas permasalahan tersebut. Dengan penambahan metode perbaikan citra, aplikasi Fuzzy Piction dapat memberikan prediksi kandungan pigmen dengan tingkat presisi yang lebih baik. Aplikasi Fuzzy Piction difasilitasi dengan layanan cloud yang dikembangkan menggunakan Flask web service sehingga model perbaikan citra dan prediksi pigmen lebih mudah diperbarui. Hasil perbaikan warna oleh PLSR berhasil menyeragamkan warna citra serta dapat memberikan hasil prediksi kandungan pigmen dengan standar deviasi yang lebih kecil. Variasi prediksi kandungan pigmen dengan 3 jenis smartphone yang berbeda pada objek daun yang sama  dapat diturunkan sebesar 87% setelah citra asal diperbaiki dengan PLSR.


Abstract

Chlorophyll, carotenoids, and anthocyanins are three main pigments that are important for photosynthesis process. Its content can be examined to determine the status of plants health. The destructive approach of evaluating pigment content is expensive and necessitates specialized handling. An Android based application called Fuzzy Piction could predict the content of those pigments nondestructively using the FP3Net, a Convolutional Neural Network (CNN) model. This application predicts the pigment content in plant leaf by its digital images. However, Fuzzy Piction is still not invariant to differences in image quality that can occur due to differences in smartphone camera specifications. This is indicated by the difference in the prediction results of the pigment content on several smartphones for the same leaf object. Therefore, the Partial Least Square Regression (PLSR) technique was used in this work as an image enhancement method to resolve the issue. Eventually, Fuzzy Piction may provide precise predictions of pigment content by embedding PLSR in it. A cloud service made with the Flask web service makes it easy to update the image enhancement and pigment prediction models for the Fuzzy Piction application. The results of color correction by PLSR succeeded in uniforming the color of the image and could provide predictive results of pigment content with a smaller standard deviation. The variation of pigment content prediction with 3 different smartphone types on the same leaf object can be reduced by 87% after the original image is corrected with PLSR.

Downloads

Download data is not yet available.

Referensi

ABDI, H., 2003. Multivariate Analysis. Encyclopedia for research methods for the social sciences. Thousand Oaks: Sage, pp. 699-702.

ABDI, H., 2003. Partial Least Square (PLS) Regression. Encyclopedia for research methods for the social sciences, 6(4), pp. 792-795.

ABDI, H. & WILLIAMS, L. J., 2013. Partial Least Squares Methods: Partial Least Squares Correlation and Partial Least Square Regression. Computational toxicology, pp. 549-579.

AMANI, M. F. H. J. O. V. G. A. A. a. L. F., 2019. Color Calibration on Human Skin Images. International Conference on Computer Vision Systems, pp. 211-223.

C DING, Z. M., 2020. Multi-Camera Color Correction via Hybrid Histogram Matching. IEEE Transactions on Circuits and Systems for Video Technology, 9, pp. 3327-3337.

FOREST, H., 2018. Leaf Pigments | Harvard Forest. [Online]

Available at: https://harvardforest .fas.harvard.edu/leaves/pigment

[Diakses November 2021].

JUSTINE, A., 2020. Pengembangan Aplikasi Prediksi Kandungan Pigmen Daun secara Non-Destruktif Berbasis Android, Malang: Universitas Ma Chung.

JUSTINE, A., 2021. Pengembangan Fuzzy Convolutional Neural Network untuk Pengenalan Warna pada Sistem Prediksi Pigmen Tanaman, Malang: Universitas Ma Chung.

KUSUMA, P. D., 2020. Machine Learning Teori, Program, dan Studi Kasus. Yogyakarta: Deepublish Publisher.

PRILIANTI, K., ANAM, S., BROTOSUDARMO, T. & SURYANTO, A., 2020. Real-time Assessment of Plant Photosynthetic Pigment Contents with An Artificial Intelligence Approach in A Mobile Application. Journal of Agricultural Engineering 51(4), pp. 220-228.

SUNOJ, S. et al., 2018. Color Calibration of Digital Images for Agriculture and Other Applications. ISPRS Journal of Photogrammetry and Remote Sensing, 146, pp. 221–234.

TAO, H. ET AL., 2020. Estimation of the Yield and Plant Height of Winter Wheat Using UAV-Based Hyperspectral Images. Sensors, 4(1231), pp. 20.

TOBIAS, R. D., 1995. An Introduction to Partial Least Square Regression. Proceedings of the twentieth annual SAS users group international conference, 20, pp. 1-8.

VALLE, J. C. D. ET AL., 2018. Digital Photography Provides a Fast, Reliable, and Noninvasive Method to Estimate Anthocyanin Pigment Concentration in Reproductive and Vegetative Plant Tissues. Ecology and Evolution, 8(6), pp. 3064-3076.

VISHNOI, V. K., KUMAR, K. & KUMAR, B., 2021. Plant Disease Detection Using Computational Intelligence and Image Processing. Journal of Plant Diseases and Protection, 128(1), pp. 19-53.

ŽUVELA, P. S. M. J. L. J. B. T. K. R. W. M. A. B. B., 2019. Column Characterization and Selection Systems in Reversed-Phase High-Performance Liquid Chromatography. Chemical reviews, 119(6), pp. 3674-3729.

Diterbitkan

29-12-2022

Cara Mengutip

Rancang Bangun Aplikasi Berbasis Android untuk Perbaikan Kualitas Citra Tanaman Atas Perbedaan Spesifikasi Kamera Smartphone pada Prediksi Kandungan Pigmen Fotosintesis Secara Real-Time. (2022). Jurnal Teknologi Informasi Dan Ilmu Komputer, 9(7), 1679-1688. https://doi.org/10.25126/jtiik.2022976771