A computer processor implemented method for monitoring a condition associated with operating heavy equipment (104, 160, 180), the method comprising: receiving a plurality of images at an interface of an embedded processor disposed on the heavy equipment, the images providing a view of at least an operating implement of the heavy equipment; and characterized by processing each of the plurality of images using a first neural network implemented on the embedded processor, the first neural network having been previously trained to identify regions of interest (900, 902, 904, 906, 908, 910) within the image; wherein each region of interest has an associated designation as at least one of: a critical region suitable for extraction of critical operating condition information required for operation of the heavy equipment (104, 160, 180); and a non-critical region suitable for extraction of non-critical operating condition information associated with the operation of the heavy equipment (104, 160, 180); causing the embedded processor to initiate further processing of image data associated with critical regions to generate local output operable to alert an operator of the heavy equipment (104, 160, 180) of the associated critical operating condition; transmitting image data associated with non-critical regions to a remote processor, the remote processor being operably configured for further processing of the image data and to generate output signals representing results of the further processing; and receiving the output signals generated by the remote processor at one of the embedded processor or another processor associated with a heavy equipment operations worksite, the output signals being presentable via an electronic user interface based at least in part on said output signals to indicate the results of the further processing. receiving a plurality of images at an interface of an embedded processor disposed on the heavy equipment, the images providing a view of at least an operating implement of the heavy equipment; and characterized by processing each of the plurality of images using a first neural network implemented on the embedded processor, the first neural network having been previously trained to identify regions of interest (900, 902, 904, 906, 908, 910) within the image; wherein each region of interest has an associated designation as at least one of: a critical region suitable for extraction of critical operating condition information required for operation of the heavy equipment (104, 160, 180); and a non-critical region suitable for extraction of non-critical operating condition information associated with the operation of the heavy equipment (104, 160, 180); a critical region suitable for extraction of critical operating condition information required for operation of the heavy equipment (104, 160, 180); and a non-critical region suitable for extraction of non-critical operating condition information associated with the operation of the heavy equipment (104, 160, 180); causing the embedded processor to initiate further processing of image data associated with critical regions to generate local output operable to alert an operator of the heavy equipment (104, 160, 180) of the associated critical operating condition; transmitting image data associated with non-critical regions to a remote processor, the remote processor being operably configured for further processing of the image data and to generate output signals representing results of the further processing; and receiving the output signals generated by the remote processor at one of the embedded processor or another processor associated with a heavy equipment operations worksite, the output signals being presentable via an electronic user interface based at least in part on said output signals to indicate the results of the further processing.