Early bruise detection on pomegranate (Punica granatum L.), using hyperspectral imaging coupled with artificial neutral network algorithm

Abstract

Fruit quality might suffer from bruising before and after harvest. Global focus is being paid to fruit bruise detection, especially early damage detection. In this study, a method was developed to identify bruised pomegranate fruit (cv. 'Wonderful') immediately after bruising. After splitting 60 pomegranates into bruised and unbruised parts, hyperspectral images were taken immediately, 7 and 14 d afterwards. Two 30-fruit groups were sampled. Group A had bruised pomegranates (dropped from a height of 60 cm onto a ceramic surface), whereas Group B had unbruised counterparts. The study analyzed a broad spectrum of wavelengths to collect vital information regarding the effects of injury. This was accomplished with Vis-NIR (400–1,000 nm) and SWIR (1,000–2,500 nm) line scan mode equipment. The line scan mode was selected due to its compatibility with conveyor belt systems typically utilized in fruit packaging lines. The classification prediction model employed the 2-Layer Feedforward artificial neural network due to its advantageous characteristics of simplicity and robustness. This study confirmed early bruise detection for pomegranate fruit with an accuracy of 88.3% and 86.7% based on full and selected wavelengths, respectively. Storage duration improves bruise recognition. Bruises are hard to spot early and become more visible with time. Hence, this technique's capacity to do so is a major benefit in the post-harvest handling. This research reduced the VNIR and SWIR input dimensions from 186 and 288 to five, resulting in a quicker and more compact classification algorithm. This will make it easier to create a cutting-edge sorting and grading system for bruise detection.

Keywords

• nondestructive techniques
• machine learning
• fruit internal quality
• defect identification
• classification accuracy
• horticultural produce