A method for classifying digital images for monitoring solid objects passing through a shale shaker of a wellbore drilling assembly, the method comprising: receiving, by one or more processors, digital images captured by a digital imaging device mounted to a non-vibrating member of the shale shaker of the wellbore drilling assembly, the shale shaker positioned at a surface of the Earth adjacent a wellbore and configured to receive a solid slurry comprising a mixture of wellbore drilling mud and solid objects found in the wellbore while drilling the wellbore through a subterranean zone, the solid objects comprising drill cuttings and non-drilled solids, the digital imaging device oriented to face a portion of the shale shaker that receives the solid slurry, the digital imaging device configured to capture digital images of the solid objects while the solid slurry is received by the shale shaker; and by implementing image processing techniques on the images, classifying, by the one or more processors, a first solid object captured in a first image as a drill cutting based on comparing dimensions of the first solid object determined from the first image with stored dimensions of known drill cuttings and known non-drilled solids, and classifying, by the one or more processors, a second solid object captured in a second image as a non-drilled solid based on comparing dimensions of the second solid object determined from the second image with stored dimensions of known drill cuttings and known non-drilled solids; wherein implementing the image processing techniques comprises deploying deep learning techniques comprising deploying a convolutional neural network, CNN, model for pattern recognition and image classification of the digital images captured by the digital imaging device, and wherein deploying the CNN model comprises receiving drilling parameters applied to the wellbore drilling assembly to drill the wellbore, applying the received drilling parameters as inputs to the CNN model, wherein the drilling parameters comprise a rate of penetration, a weight on bit, and wellbore drilling mud flow rate, and deploying the CNN model including the received drilling parameters as inputs for the pattern recognition and the image classification of the digital images. receiving, by one or more processors, digital images captured by a digital imaging device mounted to a non-vibrating member of the shale shaker of the wellbore drilling assembly, the shale shaker positioned at a surface of the Earth adjacent a wellbore and configured to receive a solid slurry comprising a mixture of wellbore drilling mud and solid objects found in the wellbore while drilling the wellbore through a subterranean zone, the solid objects comprising drill cuttings and non-drilled solids, the digital imaging device oriented to face a portion of the shale shaker that receives the solid slurry, the digital imaging device configured to capture digital images of the solid objects while the solid slurry is received by the shale shaker; and by implementing image processing techniques on the images, classifying, by the one or more processors, a first solid object captured in a first image as a drill cutting based on comparing dimensions of the first solid object determined from the first image with stored dimensions of known drill cuttings and known non-drilled solids, and classifying, by the one or more processors, a second solid object captured in a second image as a non-drilled solid based on comparing dimensions of the second solid object determined from the second image with stored dimensions of known drill cuttings and known non-drilled solids; classifying, by the one or more processors, a first solid object captured in a first image as a drill cutting based on comparing dimensions of the first solid object determined from the first image with stored dimensions of known drill cuttings and known non-drilled solids, and classifying, by the one or more processors, a second solid object captured in a second image as a non-drilled solid based on comparing dimensions of the second solid object determined from the second image with stored dimensions of known drill cuttings and known non-drilled solids; wherein implementing the image processing techniques comprises deploying deep learning techniques comprising deploying a convolutional neural network, CNN, model for pattern recognition and image classification of the digital images captured by the digital imaging device, and wherein deploying the CNN model comprises receiving drilling parameters applied to the wellbore drilling assembly to drill the wellbore, applying the received drilling parameters as inputs to the CNN model, wherein the drilling parameters comprise a rate of penetration, a weight on bit, and wellbore drilling mud flow rate, and deploying the CNN model including the received drilling parameters as inputs for the pattern recognition and the image classification of the digital images. receiving drilling parameters applied to the wellbore drilling assembly to drill the wellbore, applying the received drilling parameters as inputs to the CNN model, wherein the drilling parameters comprise a rate of penetration, a weight on bit, and wellbore drilling mud flow rate, and deploying the CNN model including the received drilling parameters as inputs for the pattern recognition and the image classification of the digital images.