The “Vein” in a shrimp is its digestive tract filled with grit, sand, and sediments stretching along the back of the abdomen. In most shrimp market forms, presence of vein is highly restricted and limited according to the U.S. standard for imports. This research aims to develop an image‐based approach for detection of improperly deveined shrimps. Two hundred shrimp images were subjected to a sequence of image processing techniques before extracting significant parameters from grayscale images. These parameters include shape measurements and pixel value measurements drawn from an image histogram. In this research, disqualified shrimps were identified by two classification techniques: linear discriminant analysis and support vector machine (SVM). Better than 98% classification accuracy was obtained with the SVM using a polynomial kernel function. The success of this research has filled a void left by past studies to facilitate fully automated shrimp quality inspection. Practical applications Rising wages and labor scarcity are among critical problems to seafood industries, along with low productivity due to ergonomics limitations. Such problems will be even worse in the near future and automated machines are becoming a popular alternative to tackle them. These machines must be driven by an intelligent processing unit capable of handling unavoidable variability naturally found in agricultural products. In most shrimp market forms, presence of veins is highly restricted and limited by the U.S. standards for imports. Deveining always leaves remnants of uncertain length. Employing statistical learning techniques, the approach developed in this study can accurately and automatically discriminate shrimps by acceptability based on the vein. Findings of this research contribute to the development of a fully automated shrimp processing machine, supporting sustainability of the industry by reducing reliance on labor policies and workforce availability.

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Wood is a natural derivative, it possesses randomly distributed inherent defects all over its mass. This complicates the cost estimation process; data collected showed that even planks with similar defect pattern would have different percentage of material loss. Such uncertain loss was caused by changes in cutting parameters. In this study, a Fuzzy Inference (FI) method was employed to predict wood loss in a rubber wooden toy manufacturing cutting process. Notable variables are: length of cut, and area of cut. Prediction accuracy from the FI method was compared with that of other alternative methods. Common practice assumes a constant defective proportion, resulting in inaccurate cost estimation. A regression equation allows the loss to be varied by the cutting parameters; but only one parameter was found to be significant. Experimental results show that the FI method greatly outperformed regression and conventional methods. These findings emphasize the influence of cutting parameters on product cost. Accurate cost estimation enables better planning for efficient pricing strategies and enhances business competitiveness.