Inline detection of citrus rind micro-wounds using contrast-enhanced X-ray imaging: A feasibility study

Abstract

Decay management is crucial in the citrus industry due to the rapid spread of infections through wounds. Despite the urgency, effective methodologies for screening citrus rind micro-wounds are lacking. This study presents a preliminary investigation into real-time detection of citrus rind micro-wounds using contrast-enhanced X-ray imaging. This method highlights and magnifies rind wounds in X-ray images. The process involves immersing fruit in a contrast solution, capturing three sequential X-ray images, and then washing off residual contrast. Satsuma mandarins were used, with potassium iodide (KI) as the contrast agent duo to its distinct contrast properties on rind wounds coupled with high safety levels. A Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model with multi-head attention mechanisms was developed, achieving a detection accuracy of 97.19 %. Post-radiography assessments showed minimal effects on the fruit's external appearance and internal quality, though a slight weight loss was observed. These results demonstrate the proposed method's effectiveness in detecting citrus rind micro-wounds, offering a promising approach for enhancing decay management in the citrus industry.