<![CDATA[Precise and efficient object volume measurement is crucial across diverse industrial domains, including logistics, manufacturing, and agriculture, where traditional methods are often labor-intensive and error-prone. DL and CV technologies offer a compelling alternative, providing greater accuracy, speed, and safety through non-contact, real-time, and adaptive solutions. To address existing knowledge gaps, this SLR is believed to be the first to integrate and analyze the three critical dimensions of DL holistically- and CV-based volume measurement: core methodologies, practical applications across various industries, and outstanding challenges, thereby providing a unified and comprehensive understanding of the state of the art that previous, more fragmented reviews have failed to deliver. Regarding methodology, dominant DL techniques include CNNs, Mask R-CNN, and U-Net for segmentation; GANs for 3D model generation; and PointNet/voxel networks for 3D data processing, with sensor integration impacting model architecture. Applications span agriculture, logistics, manufacturing, and construction, demonstrating high accuracy with error rates as low as 0.75% and MAPE typically ranging from 3.2% to 5%. Challenges involve occlusions, diverse environmental conditions, data scarcity, and computational costs. Prioritized research directions include lightweight models, multi-task learning, improved generalization, greater robustness, and explainable AI. Overall, this SLR comprehensively synthesizes current DL and CV methodologies, their practical applications, and future research directions in object volume measurement.]]>
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