A mower implement comprising: a frame moveable across a ground surface in a direction of travel during operation; a cutter coupled to the frame and including a blade operable to cut crop material as the frame moves across the ground surface; an image sensor coupled to the frame and positioned to capture an image of cut crop rearward of the cutter relative to the direction of travel during operation; a blade diagnostic controller including a processor and a memory having a diagnostic algorithm stored therein, wherein the processor is configured to execute the diagnostic algorithm to: capture an image of the cut crop rearward of the cutter with the image sensor as the frame moves across the ground surface; identify the cut end (64, 66) of the cut crop in the image via pattern matching and recognition using a convolutional neural network, wherein the convolutional neural network is configured to classify the cut end (64, 66) of the cut crop as one of a sharp cut end and a dull cut end; determine a cut quality of the cut end (64, 66) of the cut crop; calculate a frequency of dull cut ends of the cut crop; determine the cut quality based on the frequency of dull cut ends of the cut crop;correlate the cut quality of the cut end (64, 66) to a blade sharpness index; and communicate an index signal to a communicator, wherein the index signal controls the communicator to generate a communication indicating the blade sharpness index. a frame moveable across a ground surface in a direction of travel during operation; a cutter coupled to the frame and including a blade operable to cut crop material as the frame moves across the ground surface; an image sensor coupled to the frame and positioned to capture an image of cut crop rearward of the cutter relative to the direction of travel during operation; a blade diagnostic controller including a processor and a memory having a diagnostic algorithm stored therein, wherein the processor is configured to execute the diagnostic algorithm to: capture an image of the cut crop rearward of the cutter with the image sensor as the frame moves across the ground surface; identify the cut end (64, 66) of the cut crop in the image via pattern matching and recognition using a convolutional neural network, wherein the convolutional neural network is configured to classify the cut end (64, 66) of the cut crop as one of a sharp cut end and a dull cut end; determine a cut quality of the cut end (64, 66) of the cut crop; calculate a frequency of dull cut ends of the cut crop; determine the cut quality based on the frequency of dull cut ends of the cut crop;correlate the cut quality of the cut end (64, 66) to a blade sharpness index; and communicate an index signal to a communicator, wherein the index signal controls the communicator to generate a communication indicating the blade sharpness index. capture an image of the cut crop rearward of the cutter with the image sensor as the frame moves across the ground surface; identify the cut end (64, 66) of the cut crop in the image via pattern matching and recognition using a convolutional neural network, wherein the convolutional neural network is configured to classify the cut end (64, 66) of the cut crop as one of a sharp cut end and a dull cut end; determine a cut quality of the cut end (64, 66) of the cut crop; calculate a frequency of dull cut ends of the cut crop; determine the cut quality based on the frequency of dull cut ends of the cut crop;correlate the cut quality of the cut end (64, 66) to a blade sharpness index; and communicate an index signal to a communicator, wherein the index signal controls the communicator to generate a communication indicating the blade sharpness index.