Realizing the integration of intelligent fruit picking and grading for apple harvesting robots is an inevitable requirement for the future development of smart agriculture and precision agriculture. Therefore, an apple maximum diameter estimation model based on RGB-D camera fusion depth information was proposed in the study. Firstly, the maximum diameter parameters of Red Fuji apples were collected, and the results were statistically analyzed. Then, based on the Intel RealSense D435 RGB-D depth camera and LabelImg software, the depth information of apples and the two-dimensional size information of fruit images were obtained. Furthermore, the relationship between fruit depth information, two-dimensional size information of fruit images, and the maximum diameter of apples was explored. Based on Origin software, multiple regression analysis and nonlinear surface fitting were used to analyze the correlation between fruit depth, diagonal length of fruit bounding rectangle, and maximum diameter. A model for estimating the maximum diameter of apples was constructed. Finally, the constructed maximum diameter estimation model was experimentally validated and evaluated for imitation apples in the laboratory and fruits on the Red Fuji fruit trees in modern apple orchards. The experimental results showed that the average maximum relative error of the constructed model in the laboratory imitation apple validation set was ±4.1%, the correlation coefficient (R²) of the estimated model was 0.98613, and the root mean square error (RMSE) was 3.21 mm. The average maximum diameter estimation relative error on the modern orchard Red Fuji apple validation set was ±3.77%, the correlation coefficient (R²) of the estimation model was 0.84, and the root mean square error (RMSE) was 3.95 mm. The proposed model can provide theoretical basis and technical support for the selective apple-picking operation of intelligent robots based on apple size grading.

In order to improve the perception accuracy of the apple tree fruit recognition model and to reduce the model size, a lightweight apple target recognition method based on an improved YOLOv5s artificial intelligence algorithm was proposed, and relevant experiments were designed. The Depthwise Separable Convolution (DWConv) module has many advantages: (1) It has high computational efficiency, reducing the number of parameters and calculations in the model; (2) It makes the model lightweight and easy to deploy in hardware; (3) DWConv can be combined with other modules to enhance the multi-scale feature extraction capability of the detection network and improve the ability to capture multi-scale information; (4) It balances the detection accuracy and speed of the model; (5) DWConv can flexibly adapt to different network structures. Because of its efficient computing modes, lightweight design, and flexible structural adaptation, the DWConv module has significant advantages in multi-scale feature extraction, real-time performance improvement, and small-object detection. Therefore, this method improves the original YOLOv5s network architecture by replacing the embedded Depthwise Separable Convolution in its Backbone network, which reduces the size and parameter count of the model while ensuring detection accuracy. The experimental results show that for the test-set images, the proposed improved model has an average recognition accuracy of 92.3% for apple targets, a recognition time of 0.033 s for a single image, and a model volume of 11.1 MB. Compared with the original YOLOv5s model, the average recognition accuracy was increased by 0.8%, the recognition speed was increased by 23.3%, and the model volume was compressed by 20.7%, effectively achieving lightweight improvement of the apple detection model and improving the accuracy and speed of detection. The detection algorithm proposed in the study can be extended to the intelligent measurement of apple biological and physical characteristics, including for size measurement, shape analysis, and color analysis. The proposed method can improve the intelligence level of orchard management and horticultural technology, reduce labor costs, assist precision agriculture technology, and promote the transformation of the horticultural industry toward sustainable development.

Deep learning technology can automatically learn features from large amounts of data, with powerful feature extraction and pattern recognition capabilities, thereby improving the accuracy and efficiency of object detection. [The objective of this study]: In order to improve the accuracy and speed of mask wearing deep learning detection models in the post pandemic era, the [Problem this study aimed to resolve] was based on the fact that no research work has been reported on standardized detection models for mask wearing with detecting nose targets specially. [The topic and method of this study]: A mask wearing normalization detection model (towards the wearing style exposing the nose to outside, which is the most obvious characteristic of non-normalized style) based on improved YOLOv5s (You Only Look Once v5s is an object detection network model) was proposed. [The improved method of the proposed model]: The improvement design work of the detection model mainly includes (1) the BottleneckCSP (abbreviation of Bottleneck Cross Stage Partial) module was improved to a BottleneckCSP-MASK (abbreviation of Bottleneck Cross Stage Partial-MASK) module, which was utilized to replace the BottleneckCSP module in the backbone architecture of the original YOLOv5s model, which reduced the weight parameters’ number of the YOLOv5s model while ensuring the feature extraction effect of the bonding fusion module. (2) An SE module was inserted into the proposed improved model, and the bonding fusion layer in the original YOLOv5s model was improved for better extraction of the features of mask and nose targets. [Results and validation]: The experimental results indicated that, towards different people and complex backgrounds, the proposed mask wearing normalization detection model can effectively detect whether people are wearing masks and whether they are wearing masks in a normalized manner. The overall detection accuracy was 99.3% and the average detection speed was 0.014 s/pic. Contrasted with original YOLOv5s, v5m, and v5l models, the detection results for two types of target objects on the test set indicated that the mAP of the improved model increased by 0.5%, 0.49%, and 0.52%, respectively, and the size of the proposed model compressed by 10% compared to original v5s model. The designed model can achieve precise identification for mask wearing behaviors of people, including not wearing a mask, normalized wearing, and wearing a mask non-normalized.