Digital image processing for preliminary detection of infected porang (Amorphophallus muelleri) seedlings

Aryanis Mutia Zahra, Noveria Anggi Nurrahmah, Sri Rahayoe, Rudiati Evi Masithoh, Muhammad Fahri Reza Pahlawan and Laila Rahmawati
Additional contact information
Aryanis Mutia Zahra: Department of Agricultural and Biosystems Engineering, Faculty of Agricultural Technology, Universitas Gadjah Mada, Yogyakarta, Indonesia
Noveria Anggi Nurrahmah: Department of Agricultural and Biosystems Engineering, Faculty of Agricultural Technology, Universitas Gadjah Mada, Yogyakarta, Indonesia
Sri Rahayoe: Department of Agricultural and Biosystems Engineering, Faculty of Agricultural Technology, Universitas Gadjah Mada, Yogyakarta, Indonesia
Rudiati Evi Masithoh: Department of Agricultural and Biosystems Engineering, Faculty of Agricultural Technology, Universitas Gadjah Mada, Yogyakarta, Indonesia
Muhammad Fahri Reza Pahlawan: Department of Smart Agriculture Systems, College of Agricultural and Life Science, Chungnam National University, Daejeon, South Korea
Laila Rahmawati: Research Center for Food Technology and Processing, National Research and Innovation Agency, Yogyakarta, Indonesia

Research in Agricultural Engineering, 2024, vol. 70, issue 2, 111-121

Abstract: Porang (Amorphophallus muelleri) is an Indonesian parental plant tuber developed vegetatively from bulbils during dormancy and harvested through petiole detachment for the industrial production of glucomannan. Pathogenic fungi and whiteflies can cause infection during harvesting and storage, destructing plant cells as well as reducing seed quality and crop yields. Therefore, this study aimed to develop a calibration model for detecting infected and non-infected porang bulbils using a computer vision system. Image parameters such as colour (red, green, blue - RGB and hue, saturation, intensity - HSI), texture (contrast, homogeneity, correlation, energy, and entropy), and dimensions (width, area, and height) were evaluated on 90 samples in three positions. The results showed that the majority of image quality properties were significantly associated with non-infected and infected porang bulbils as showed by Pearson correlation values of 0.901 and 0.943, respectively. Discriminant analysis based on image attributes effectively classified non-infected and infected seedlings, achieving a model accuracy of 97.0% for correctly classified cross-validated grouped cases. Therefore, computer vision can be used for the preliminary detection of fungal infection in porang bulbils, as evidenced by its high accuracy and outstanding model performance.

Keywords: discriminant analysis; gray-level cooccurrence matrix; model performance; seed quality; vegetative phase (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://rae.agriculturejournals.cz/doi/10.17221/79/2023-RAE.html (text/html)
http://rae.agriculturejournals.cz/doi/10.17221/79/2023-RAE.pdf (application/pdf)
free of charge

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:caa:jnlrae:v:70:y:2024:i:2:id:79-2023-rae

DOI: 10.17221/79/2023-RAE

Access Statistics for this article

Research in Agricultural Engineering is currently edited by Bc. Michaela Polcarová

More articles in Research in Agricultural Engineering from Czech Academy of Agricultural Sciences
Bibliographic data for series maintained by Ivo Andrle ().