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This paper presents a machine vision system (MVS) for visual quality inspection of beans which is composed by a set of software and hardware. The software was built from proposed approaches for segmentation, classification, and defect detection, and the hardware consists of equipment developed with low-cost electromechanical materials. Experiments were conducted in two modes: offline and online. For offline experiments, aimed at evaluating the proposed approaches, we composed a database containing 270 images of samples of beans with different mixtures of skin colors and defects. In the online mode, the beans contained in a batch, for example, a bag of 1 kg, are spilled continuously on the conveyor belt for the MVS to perform the inspection, similar to what occurs in an automated industrial visual inspection process. In the offline experiments, our approaches for segmentation, classification, and defect detection achieved, respectively, the average success rates of 99.6%, 99.6%, and 90.0%. In addition, the results obtained in the online mode demonstrated the robustness and viability of the proposed MVS, since it is capable to analyze an image of 1280 × 720 pixels, spending only 1.5 s, with average successes rates of 98.5%, 97.8%, and 85.0%, respectively, to segment, classify, and detect defects in the grains contained in each analyzed image.