Assessment of mechanical damage in canola using X-ray radiography and deep learning

Abstract

Mechanical damage during post-harvest handling and processing can drastically impair the quality, germination potential, and market value of canola ( Brassica napus L.) seeds. Traditional visual inspection methods are subjective and limited to detecting external defects, often missing internal damage. This study presents a non-destructive approach for classifying mechanical damage in canola seeds using X-ray radiography combined with artificial intelligence techniques. Seeds at three moisture contents (6 %, 8 %, and 12 %) were subjected to four levels of impact energy (0, 3, 3.7, and 4.3 mJ), generating 3,029 radiographic images labelled into four levels of varying mechanical damage (no damage, low damage, medium damage, and high damage). Two analytical pipelines were developed. The first involved machine learning with 37 extracted features, including intensity-based metrics, texture descriptors, frequency-domain features, and Fast Fourier Transform (FFT) features. Support vector machine (SVM) and random forest (RF) models were developed and evaluated; the SVM achieved the highest accuracy of 74.01 % after feature selection. The second pipeline implemented deep learning via transfer learning on five pre-trained convolutional neural networks: MobileNetV2, EfficientNet-B0, Xception, ResNet50, and DenseNet121. Among these, MobileNetV2 achieved the highest accuracy of 91.42 %, outperforming all traditional machine learning models. The results demonstrate the potential of integrating radiographic imaging with artificial intelligence methods for rapid, reliable, and non-destructive classification of mechanical damage in canola seeds, paving the way for intelligent quality control systems in the oilseed supply chain.