To realize applications involving low speed and high torque in the high-performance actuator industry, especially in the aerospace field, we propose a novel 70H traveling wave rotary ultrasonic motor (TRUM) with an outer diameter of 70 mm and an aperture ratio of 53% (the ratio between the aperture and outer diameter). The power transmission mechanism between the stator and the rotor is analyzed, and a method for realizing low-speed–high-torque characteristics of TRUMs is proposed. ANSYS software is used to simulate the modal parameters of the stator, and the ratio β between the normal amplitude and the tangential vibration velocity of the stator is proposed. The larger the value of β, the lower the speed and the higher the torque. Furthermore, two prototype motors are fabricated, namely, 70HA and 70HB. Compared with 70HA, the maximum no-load speed of the improved 70HB (50 rpm) is decreased by 23.1% and the maximum stall torque (2.4 N∙m) is increased by 100%. Besides this, with a smaller mass of 210 g and a higher torque density of 11.43 N∙m/kg, 70HB has fundamentally different mechanical characteristics.

It is important to obtain accurate information about kiwifruit vines to monitoring their physiological states and undertake precise orchard operations. However, because vines are small and cling to trellises, and have branches laying on the ground, numerous challenges exist in the acquisition of accurate data for kiwifruit vines. In this paper, a kiwifruit canopy distribution prediction model is proposed on the basis of low-altitude unmanned aerial vehicle (UAV) images and deep learning techniques. First, the location of the kiwifruit plants and vine distribution are extracted from high-precision images collected by UAV. The canopy gradient distribution maps with different noise reduction and distribution effects are generated by modifying the threshold and sampling size using the resampling normalization method. The results showed that the accuracies of the vine segmentation using PSPnet, support vector machine, and random forest classification were 71.2%, 85.8%, and 75.26%, respectively. However, the segmentation image obtained using depth semantic segmentation had a higher signal-to-noise ratio and was closer to the real situation. The average intersection over union of the deep semantic segmentation was more than or equal to 80% in distribution maps, whereas, in traditional machine learning, the average intersection was between 20% and 60%. This indicates the proposed model can quickly extract the vine distribution and plant position, and is thus able to perform dynamic monitoring of orchards to provide real-time operation guidance.

China is a one of the largest countries producing kiwifruit, and there are many kinds of kiwifruit in China. The identification of kiwifruit varieties has important application value in kiwifruit automatic picking robots and kiwifruit sorting factories. The paper studies the classification of kiwifruit varieties based on deep learning, and studies the deployment and application of kiwifruit classification model on the Jetson Nano artificial intelligence development board. A portable device used for classification of kiwifruit varieties was designed and developed. This paper studied the selection of various parts of the portable device of kiwifruit classification and designed the portable device shell. The portable device shell was obtained through the 3D printer, assembled the parts, and obtained the portable kiwi variety classification equipment. The construction of the kiwifruit dataset of “Cuixiang”, “Qinmei”, “Xuxiang” and “Hayward” kiwis was studied and using data augmentation to introduce variations into the data. According to the characteristics of the problem of 4 types of kiwifruit classification, the methods and advantages of transfer learning are discussed. Using transfer learning based on three pre-training models Xception, ResNet50, DenseNet121. Comparative analysis of the model size, training speed, convergence, and recognition accuracy of the transfer learning model based on Xception, ResNet50, and DenseNet121, and concluded that in Xception, ResNet50, and DenseNet121, the transfer learning model based on DenseNet121 pre-trained model has the best effect on the classification of four kiwifruit varieties, with fast convergence speed, smallest model, and high recognition accuracy of 97.79%. This paper studied the deployment and experiment of the deep learning model on the Jetson Nano development board. Using the PyTorch to perform transfer learning based on the DenseNet121 pre-training model. After using the TensorRT acceleration engine, it took an average of 0.035s to classify each image, and the real-time classification FPS reached an average of 30, the accuracy rate of the model on the validation set is 93.63%, achieving fast and accurate classification of kiwi varieties.