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As apple harvesting needs large amount of labor, and the seasonality is strong, the night operation of apple harvesting robot is proposed, in order to improve the efficiency of harvesting. The apple's real-time night vision image contains lots of noise, which is captured by image processing system of apple harvesting robot. The noise will influence the operating efficiency and recognition precision, and then influence the harvesting efficiency. Under different artificial lights, apple night vision images are captured, the noises are analyzed through the difference image method, and the type of noise is determined to be mixed noise. The main part of mixed noise is Gaussian noise, accompanied by some salt-pepper noise. Aiming at the problem of Gaussian noise removal, the theory of independent component analysis (ICA) is introduced into the de-noising method for night vision image. The ICA algorithm mostly uses gradient iterative solver, so it has some defects, such as easily trapped in local minimum, slow convergence speed. All of these defects lead to the following phenomena easily, such as the unthoroughness in the de-noising and the long running time. In order to overcome these defects, particle swarm optimization (PSO) algorithm is used to optimize the ICA, further to establish an optimized ICA de-noising method based on PSO (PSO-ICA), applied in night vision image, hoping to minimize noise pollution and improving the operating efficiency of de-noising method. Using the standard Lenna image and apple image captured under nature light, by the simulation experiments, these 2 pictures are added with the Gaussian noise with the variance of 0.05 and the salt-pepper noise with the P value of 0.05, respectively. Compared with the average filtering method and ICA de-noising method, the results show that the de-noising effect of PSO-ICA algorithm is the most ideal. Using peak signal-to-noise ratio (PSNR) to do difference test, the result shows that, under 0.05 significant level, 3 de-noising methods show significant difference. Using different apple night vision images captured to do experiments, the results show that, from the visual evaluation, the low noise image is obtained by PSO-ICA de-noising method, and its noise decreased significantly. In order to evaluate the de-noising effect of night vision image more objectively, taking the natural light image as reference, the concept of relative peak signal-to-noise ratio (RPSNR) is proposed. From the RPSNR evaluation, compared with the original image, the image after average filtering de-noising and that after ICA de-noising, the image based on the method of PSO-ICA de-noising increased on average by 21.28%, 12.41% and 5.53%, respectively. From the run time evaluation, PSO algorithm has greatly improved the efficiency of ICA algorithm. Under incandescent lamp, the night vision image and its de-noised images have the highest RPSNR, so this type of light is suitable for artificial light source. Finally, under the natural light and 3 different artificial lights, 10 images of natural light and 30 night images are captured from 10 sample points. Using all of these images to do the repeated experiments, the trends of experimental results are consistent. In conclusion, PSO-ICA algorithm has unique advantage for night vision image de-noising, which provides a solid foundation for the night operation of apple picking robot. [ABSTRACT FROM AUTHOR]

For yield measurement of an apple orchard or the mechanical harvesting of apples, there needs to be accurate identification of the target apple fruit. However, in a natural scene, affected by the apple's growth posture and camera position, there are many kinds of apple images, such as overlapped apples; mutual shadows or leaves; stems; etc. It is a challenge to accurately locate overlapped apples. They will influence the positioning time and recognition efficiency and then affect the harvesting efficiency of apple-harvesting robots or the accuracy of orchard yield measurement. In response to this problem, an overlapped circle positioning method based on local maxima is proposed. First, the apple image is transformed into the Lab color space and segmented by the K-means algorithm. Second, some morphological processes, like erosion and dilation, are implemented to abstract the outline of the apples. Then image points are divided into central points; edge points; or outer points. Third, a fast algorithm is used to calculate every internal point's minimum distance from the edge. Then, the centers of the apples are obtained by finding the maxima among these distances. Last, the radii are acquired by figuring out the minimum distance between the center and the edge. Thus, positioning is achieved. Experimental results showed that this method can locate overlapped apples accurately and quickly when the apple contour was complete; and this has certain practicability. [ABSTRACT FROM AUTHOR]