Indirect Quantitative Analysis of Biochemical Parameters in Banana Using Spectral Reflectance Indices Combined with Machine Learning Modeling

Hoda Galal; Salah Elsayed; Aida Allam; Mohamed Farouk

doi:10.3390/horticulturae8050438

Horticulturae (May 2022)

Indirect Quantitative Analysis of Biochemical Parameters in Banana Using Spectral Reflectance Indices Combined with Machine Learning Modeling

Hoda Galal,
Salah Elsayed,
Aida Allam,
Mohamed Farouk

Affiliations

Hoda Galal: Evaluation of Natural Resources Department, Environmental Studies and Research Institute, University of Sadat City, Menofia Governorate, Sadat City 32897, Egypt
Salah Elsayed: Evaluation of Natural Resources Department, Environmental Studies and Research Institute, University of Sadat City, Menofia Governorate, Sadat City 32897, Egypt
Aida Allam: Evaluation of Natural Resources Department, Environmental Studies and Research Institute, University of Sadat City, Menofia Governorate, Sadat City 32897, Egypt
Mohamed Farouk: Agricultural Engineering, Surveying of Natural Resources in Environmental Systems Department, Environmental Studies and Research Institute, University of Sadat City, Menofia Governorate, Sadat City 32897, Egypt

DOI: https://doi.org/10.3390/horticulturae8050438
Journal volume & issue: Vol. 8, no. 5
p. 438

Abstract

Read online

The primary issues in collecting biochemical information in a large area using chemical laboratory procedures are low throughput, hard work, time-consuming, and requiring several samples. Thus, real-time and precise estimation of biochemical variables of various fruits using a proximal remote sensing based on spectral reflectance is critical for harvest time, artificial ripening, and food processing, which might be beneficial economically and ecologically. The main goal of this study was to assess the biochemical parameters of banana fruits such as chlorophyll a (Chl a), chlorophyll b (Chl b), respiration rate, total soluble solids (TSS), and firmness using published and newly developed spectral reflectance indices (SRIs), integrated with machine learning modeling (Artificial Neural Networks; ANN and support vector machine regression; SVMR) at different ripening degrees. The results demonstrated that there were evident and significant differences in values of SRIs at different ripening degrees, which may be attributed to the large variations in values of biochemical parameters. The newly developed two-band SRIs are more effective at measuring different biochemical parameters. The SRIs that were extracted from the visible (VIS), near-infrared (NIR), and their combination showed better R2 with biochemical parameters. SRIs combined with ANN and SVMR would be an effective method for estimating five biochemical parameters in the calibration (Cal.) and validation (Val.) datasets with acceptable accuracy. The ANN-TSS-SRI-13 model was built to determine TSS with greater performance expectations (R2 = 1.00 and 0.97 for Cal. and Val., respectively). Furthermore, the model ANN-Firmness-SRI-15 was developed for determining firmness, and it performed better (R2 = 1.00 and 0.98 for Cal. and Val., respectively). In conclusion, this study revealed that SRIs and a combination approach of ANN and SVMR models would be a useful and excellent tool for estimating the biochemical characteristics of banana fruits.

Published in Horticulturae

ISSN: 2311-7524 (Online)
Publisher: MDPI AG
Country of publisher: Switzerland
LCC subjects: Agriculture: Plant culture
Website: http://www.mdpi.com/journal/horticulturae

About the journal

Abstract

Keywords