The apple target recognition algorithm is one of the core technologies of the apple picking robot. However, most of the existing apple detection algorithms cannot distinguish between the apples that are occluded by tree branches and occluded by other apples. The apples, grasping end-effector and mechanical picking arm of the robot are very likely to be damaged if the algorithm is directly applied to the picking robot. Based on this practical problem, in order to automatically recognize the graspable and ungraspable apples in an apple tree image, a light-weight apple targets detection method was proposed for picking robot using improved YOLOv5s. Firstly, BottleneckCSP module was improved designed to BottleneckCSP-2 module which was used to replace the BottleneckCSP module in backbone architecture of original YOLOv5s network. Secondly, SE module, which belonged to the visual attention mechanism network, was inserted to the proposed improved backbone network. Thirdly, the bonding fusion mode of feature maps, which were inputs to the target detection layer of medium size in the original YOLOv5s network, were improved. Finally, the initial anchor box size of the original network was improved. The experimental results indicated that the graspable apples, which were unoccluded or only occluded by tree leaves, and the ungraspable apples, which were occluded by tree branches or occluded by other fruits, could be identified effectively using the proposed improved network model in this study. Specifically, the recognition recall, precision, mAP and F1 were 91.48%, 83.83%, 86.75% and 87.49%, respectively. The average recognition time was 0.015 s per image. Contrasted with original YOLOv5s, YOLOv3, YOLOv4 and EfficientDet-D0 model, the mAP of the proposed improved YOLOv5s model increased by 5.05%, 14.95%, 4.74% and 6.75% respectively, the size of the model compressed by 9.29%, 94.6%, 94.8% and 15.3% respectively. The average recognition speeds per image of the proposed improved YOLOv5s model were 2.53, 1.13 and 3.53 times of EfficientDet-D0, YOLOv4 and YOLOv3 and model, respectively. The proposed method can provide technical support for the real-time accurate detection of multiple fruit targets for the apple picking robot.

ABSTRACT.Purpose: To investigate the prevalence and causes of distance-visual impairment and near-vision impairment in a rural Chinese population in Inner Mongolia. Methods: A population-based, cross-sectional study design was used to identify visual impairment in the Chinese aged 40 years and older living in Kailu County, Inner Mongolia. Low vision, blindness and near-visual impairment (NVI) were defined according to World Health Organization (WHO) criteria. Results: The overall prevalence of blindness and visual impairment based on the presenting visual acuity (VA) was 2.2% (95% CI: 1.8-2.6) and 9.8% (95% CI: 8.9-10.6), respectively, and was adjusted to 0.9% (95% CI: 0.6-1.2) and 4.7% (95% CI: 4.1-5.3) using best-corrected visual acuity (BCVA), respectively. Taking the presenting VA into consideration, the leading cause of visual impairment and blindness was cataract (40.3%, 40.9%), followed by uncorrected refractive error (26.6%, 28.2%). According to the BCVA, the main cause of visual impairment and blindness was cataract (48.3%, 41.3%) followed by glaucoma (19.0%, 23.9%). Among the examined subjects, 80.3% had NVI, and 51.7% had presbyopia. Major barriers reported by NVI persons without near correction were lack of money to purchase prescription glasses and poor quality of the available ones (43.2%). Conclusion: Visual impairment is a serious public health problem, and the main causes leading to visual impairment are treatable and preventable in the rural Chinese population in Inner Mongolia. Presbyopia, together with the low rate of spectacles and lack of appropriate refractive and presbyopia spectacles, is highly prevalent in rural China.

No abstract

No abstract

In recent years, many agriculture-related problems have been evaluated with the integration of artificial intelligence techniques and remote sensing systems. The rapid and accurate identification of apple targets in an illuminated and unstructured natural orchard is still a key challenge for the picking robot’s vision system. In this paper, by combining local image features and color information, we propose a pixel patch segmentation method based on gray-centered red–green–blue (RGB) color space to address this issue. Different from the existing methods, this method presents a novel color feature selection method that accounts for the influence of illumination and shadow in apple images. By exploring both color features and local variation in apple images, the proposed method could effectively distinguish the apple fruit pixels from other pixels. Compared with the classical segmentation methods and conventional clustering algorithms as well as the popular deep-learning segmentation algorithms, the proposed method can segment apple images more accurately and effectively. The proposed method was tested on 180 apple images. It offered an average accuracy rate of 99.26%, recall rate of 98.69%, false positive rate of 0.06%, and false negative rate of 1.44%. Experimental results demonstrate the outstanding performance of the proposed method.