An eye state assessment method for an electronic device, comprising: obtaining a first fundus photography of an eye, wherein the first fundus photography comprises an optic disc image area; obtaining the optic disc image area from the first fundus photography and generating a plurality of optic cup-to-disc ratio assessment results by a plurality of first models based on the optic disc image area; obtaining a first assessment result of the eye based on the optic cup-to-disc ratio assessment results; characterized by: performing a plurality of data augmentation operations on the first fundus photography to generate a plurality of second fundus photographies respectively corresponding to the data augmentation operations; generating a plurality of retinal nerve fiber layer defect assessment results by a plurality of second models based on the second fundus photographies, comprising: inputting one of the second fundus photographies into one of the second models, wherein the one of the second models outputs a confidence value of prediction as one of the retinal nerve fiber layer defect assessment results in response to the one of the second fundus photographies, wherein the confidence value of prediction indicates a degree of abnormality of the eye; obtaining a second assessment result of the eye based on the retinal nerve fiber layer defect assessment results; and obtaining an optic nerve assessment result of the eye based on the first assessment result and the second assessment result. obtaining a first fundus photography of an eye, wherein the first fundus photography comprises an optic disc image area; obtaining the optic disc image area from the first fundus photography and generating a plurality of optic cup-to-disc ratio assessment results by a plurality of first models based on the optic disc image area; obtaining a first assessment result of the eye based on the optic cup-to-disc ratio assessment results; characterized by: performing a plurality of data augmentation operations on the first fundus photography to generate a plurality of second fundus photographies respectively corresponding to the data augmentation operations; generating a plurality of retinal nerve fiber layer defect assessment results by a plurality of second models based on the second fundus photographies, comprising: inputting one of the second fundus photographies into one of the second models, wherein the one of the second models outputs a confidence value of prediction as one of the retinal nerve fiber layer defect assessment results in response to the one of the second fundus photographies, wherein the confidence value of prediction indicates a degree of abnormality of the eye; obtaining a second assessment result of the eye based on the retinal nerve fiber layer defect assessment results; and obtaining an optic nerve assessment result of the eye based on the first assessment result and the second assessment result. performing a plurality of data augmentation operations on the first fundus photography to generate a plurality of second fundus photographies respectively corresponding to the data augmentation operations; generating a plurality of retinal nerve fiber layer defect assessment results by a plurality of second models based on the second fundus photographies, comprising: inputting one of the second fundus photographies into one of the second models, wherein the one of the second models outputs a confidence value of prediction as one of the retinal nerve fiber layer defect assessment results in response to the one of the second fundus photographies, wherein the confidence value of prediction indicates a degree of abnormality of the eye; obtaining a second assessment result of the eye based on the retinal nerve fiber layer defect assessment results; and obtaining an optic nerve assessment result of the eye based on the first assessment result and the second assessment result.