Non-destructive detection of ovary regions in live female mud crabs by machine-learning assisted multispectral imaging

Ovarian fullness of female mud crabs (Scylla paramamosain) is key determinant of market value but is still assessed subjectively by hand. Spectrometry offers an objective alternative, and our previous studies under in vitro and semi-in vivo conditions demonstrated the potential of spectrometric features for discrimination of crab tissues (meat, ovary, hepatopancreas, and shell). However, it was still challenging to apply under in vivo conditions. This study aims to detect the ovary region in live mud crabs while keeping the ‘in vivo’ condition by combining a custom multispectral-imaging system and simple ML techniques. A special optical setup and a concise multispectral camera were included in the system aiming to acquire the transmission image through the intact carapace practically in the crab-farming fields. The ovary region was predicted pixel-wise and patch-wise using conventional classifiers (Logistic Regression, Random Forest, Gradient Boosting, k-NN, and SVM) and Convolutional Neural Networks (CNN), enhanced by Principal Component Analysis (PCA) for feature transformation. The patch-wise random forest model with PCA (7×7 patches) achieved superior performance, with an accuracy of 0.872 and an F1-score of 0.872, outperforming other methods. These findings mark a significant advancement in the application of multispectral imaging for automated, non-destructive quality assessment in live aquaculture specimens.