Abstract: Apple is one of the main producing fruits and the main economic crops in many areas. Detecting and grading apples through the image of apple trees under natural environment is helpful to promote the modernization of fruit industry. Combining deep learning with traditional methods, this paper proposes a fruit detection and grading method combining color and apple diameter. In order to improve the detection rate of unobvious targets and the precision of bounding boxes when illumination or fruit coloration is uneven, the convolutional neural network is used to construct an apple detection model and detect apple on feature maps of two scales. b*, (1.8b* -L*) color components of the image in bounding boxes in CIELAB color space are extracted, the image is binarized, and the target contour is accurately extracted to correct the bounding boxes. Experimental results show that the precision is 91.60% and the F1-score value is 87.62%. According to the image and actual size mapping method, the apple diameter is calculated to achieve the apple grading. Experimental results show that the grading accuracy is 90%.

Key words: apple detection, apple grading, convolutional neural network, fruit-diameter, color component