Early apple bruise recognition based on near-infrared imaging and grayscale gradient images

Early apple bruises, especially those occurring within half an hour, usually have no external symptoms and are difficult to recognize. This study developed a fast and nondestructive detection method for early bruises based on near-infrared camera imaging and image recognition. A total of 31 apple samples were photographed on both sides of each apple. Grayscale images of the sound apples were captured using a near-infrared camera with a wavelength region between 900 and 2350 nm. Images (n = 62) of apples without bruises were collected. The identical apples were artificially damaged and photographed at two stages (0 h, 0.5 h) by the near-infrared camera, and a total of 186 grayscale images were collected. As the light spot on the surface of apples limits the accuracy in detecting defects, a compound image pre-processing method was proposed consisting of nonlinear grayscale transformation and frequency-domain image filtering techniques. Finally, the gradient image is obtained by taking the first-order derivative of the preprocessed image. Since bruise had distinct edges, the gradient grayscale images of apples are more favorable for bruise identification. The compound method obtained a 97.62% classification accuracy for nonbruised apples and apples with fresh bruises. The experimental results show that it is feasible to identify early bruises in apples based on near-infrared camera imaging and gradient grayscale images. In the subsequent study, the method will be further improved, especially the parameters involved in the algorithm could be adjusted to adaptive variables. In addition, the system will be explored to be suitable for online apple bruise detection.