Multi-Input Deep Learning Model with RGB and Hyperspectral Imaging for Banana Grading

Abstract

Grading is a vital process during the postharvest of horticultural products as it dramatically affects consumer preference and satisfaction when goods reach the market. Manual grading is time-consuming, uneconomical, and potentially destructive. A non-invasive automated system for export-quality banana tiers was developed, which utilized RGB, hyperspectral imaging, and deep learning techniques. A real dataset of pre-classified banana tiers based on quality and size (Class 1 for export quality bananas, Class 2 for the local market, and Class 3 for defective fruits) was utilized using international standards. The multi-input model achieved an excellent overall accuracy of 98.45% using only a minimal number of samples compared to other methods in the literature. The model was able to incorporate both external and internal properties of the fruit. The size of the banana was used as a feature for grade classification as well as other morphological features using RGB imaging, while reflectance values that offer valuable information and have shown a high correlation with the internal features of fruits were obtained through hyperspectral imaging. This study highlighted the combined strengths of RGB and hyperspectral imaging in grading bananas, and this can serve as a paradigm for grading other horticultural crops. The fast-processing time of the multi-input model developed can be advantageous when it comes to actual farm postharvest processes.

Hyperspectral imaging‐based erythema classification in atopic dermatitis

“…HSI exists in the form of a three‐dimensional data cube. Using these HS cubes to extract the features of an object is inefficient because it requires a considerable amount of memory resources to calculate significant amounts of data 20 . Therefore, in this study, the rank of the bands within the data cube was calculated using the Frobenius matrix norm to exclude bands with considerable noise or little feature information while reducing the high dimensionality of the data 21 …”

Advancement of non-destructive spectral measurements for the quality of major tropical fruits and vegetables: a review

“…( Mesa and Chiang, 2021 ) using multi-input deep learning model with RGB and HSI. These models were able to categorize tier-based bananas by 98.45% and an F1 score of 0.97 with only few samples ( Mesa and Chiang, 2021 ). However, this technique is expensive and time consuming due to the use of two cameras.…”

Identification of peanut storage period based on hyperspectral imaging technology

“…Zhiyong ZOU 1 , Jie CHEN 1 , Man ZHOU 2 , Zhitang WANG 3 , Ke LIU 4 , Yongpeng ZHAO 1 , Yuchao WANG 1 , Weijia WU 1 , Lijia XU 1 * (Jin et al, 2013). Mesa & Chiang (2021) used hyperspectral imaging technology combined with RGB to classify bananas (Mesa & Chiang, 2021). Zou et al (2022) used hyperspectral nondestructive testing technology to predict peanut seed vigor with high accuracy (Zou et al, 2022).…”