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Maharsih, Inggit Kresna
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Journal : Insyst : Journal of Intelligent System and Computation

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Prediction of Physico-Chemical Characteristics in Batu Tangerine 55 Based on Reflectance-Fluorescence Computer Vision Ariani, Safitri Diah Ayu; Maharsih, Inggit Kresna; Al Riza, Dimas Firmanda
Intelligent System and Computation Vol 6 No 1 (2024): INSYST: Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v6i1.363

Abstract

Oranges (Citrus sp.) are one of the most abundant agricultural commodities in Indonesia. One of the popular local citruses is Batu Tangerine 55. Harvesting tangerines begins 252 days after the flowers bloom. Conventionally, we still determine the level of maturity by observing the color, shape, and hardness. The results of manual grouping tend to be subjective and less accurate. Destructive testing could be carried out and provide objective results; however, it would require sampling and damaging the fruits. Computer vision could be used to evaluate the maturity level of the fruit non-destructively. Dual imaging computer vision, i.e., reflectance-fluorescence mode, could be used to enhance the accuracy of the prediction. This study aims to develop a classification model and predict the physico-chemical characteristics of Batu Tangerine 55. Destructive testing is still being carried out to determine the value of TPT, the degree of acidity, and the firmness of the fruit. Non-destructive testing was carried out to obtain reflectance and fluorescence images. Once we obtain the destructive and non-destructive data, we will incorporate them into the classification and prediction models. The machine learning method for maturity classification uses three models, namely KNN, SVM, and Random Forest. The best results on the reflectance data (RGB) SVM model resulted in an accuracy of 1 for training data and 0.97 for testing data. The maturity parameter prediction method uses the PLS method. The best results for the predicted Brix/Acidity ratio R2 parameter are 0.81 and RMSE 3.4.
Co-Authors Ali Djamhuri Ariani, Safitri Diah Ayu Armanda, Zidan Asep Bayu Dani Nandiyanto Asnawi Asnawi Christina Wahyu Kartikowati Dimas Firmanda Al Riza Dinia Rizqi Dwijayanti Djamaludin, Heder Elfirta, Rizki Rabeca Ernawati, Kadek Lusi Fadhil Muhammad Tarmidzi Fajri Anugroho, Fajri Ferdian, Pamungkas Rizki Huda, Surya Inayatussakinah, Inayatussakinah Kartika Dyah Palupi Kusdiyarlistio, Andhika Alfanda Laksono, Andromeda Dwi Mufidah, Elya Muhammad, Defghi Arsy Nimas Mayang Sabrina Sunyoto, Nimas Mayang Sabrina Notonegoro, Hartoyo Novi Sylvia Pusfitasari, Memik Dian Putra, Ade Wahyu Yusariarta Rahayu, Puspita Reza, Mutia Ria Dewi Andriani Riza Alviany Ruri Agung Wahyuono Sari, Thalia Winda Wahyunanto Agung Nugroho Wardhani, Ranti Kusuma Widjaja, Stephanie Kezia Wijayanti, Ulimaz Rahma Yuli, Ahmad Yusron Sugiarto Yusuf Wibisono