Machine vision technology has dramatically improved the efficiency, speed, and quality of fruit-picking robots in complex environments. Target recognition technology for fruit is an integral part of the recognition systems of picking robots. The traditional digital image processing technology is a recognition method based on hand-designed features, which makes it difficult to achieve better recognition as it results in dealing with the complex and changing orchard environment. Numerous pieces of literature have shown that extracting special features by training data with deep learning has significant advantages for fruit recognition in complex environments. In addition, to realize fully automated picking, reconstructing fruits in three dimensions is a necessary measure. In this paper, we systematically summarize the research work on target recognition techniques for picking robots in recent years, analyze the technical characteristics of different approaches, and conclude their development history. Finally, the challenges and future development trends of target recognition technology for picking robots are pointed out.

Obstacle avoidance control and navigation in unstructured agricultural environments are key to the safe operation of autonomous robots, especially for agricultural machinery, where cost and stability should be taken into account. In this paper, we designed a navigation and obstacle avoidance system for agricultural robots based on LiDAR and a vision camera. The improved clustering algorithm is used to quickly and accurately analyze the obstacle information collected by LiDAR in real time. Also, the convex hull algorithm is combined with the rotating calipers algorithm to obtain the maximum diameter of the convex polygon of the clustered data. Obstacle avoidance paths and course control methods are developed based on the danger zones of obstacles. Moreover, by performing color space analysis and feature analysis on the complex orchard environment images, the optimal H-component of HSV color space is selected to obtain the ideal vision-guided trajectory images based on mean filtering and corrosion treatment. Finally, the proposed algorithm is integrated into the Three-Wheeled Mobile Differential Robot (TWMDR) platform to carry out obstacle avoidance experiments, and the results show the effectiveness and robustness of the proposed algorithm. The research conclusion can achieve satisfactory results in precise obstacle avoidance and intelligent navigation control of agricultural robots.

Fruit- and vegetable-harvesting robots are a great addition to Agriculture 4.0 since they are gradually replacing human labor in challenging activities. In order to achieve the harvesting process accurately and efficiently, the picking robot’s end-effector should be the first part to come into close contact with the crops. The design and performance requirements of the end-effectors are affected by the fruit and vegetable variety as well as the complexity of unstructured surroundings. This paper summarizes the latest research status of end-effectors for fruit- and vegetable-picking robots. It analyzes the characteristics and functions of end-effectors according to their structural principles and usage, which are classified into clamp, air suction, suction holding, and envelope types. The development and application of advanced technologies, such as the structural design of end-effectors, additional sensors, new materials, and artificial intelligence, were discussed. The typical applications of end-effectors for the picking of different kinds of fruit and vegetables were described, and the advantages, disadvantages, and performance indexes of different end-effectors were given and comparatively analyzed. Finally, challenges and potential future trends of end-effectors for picking robots were reported. This work can be considered a valuable guide to the latest end-effector technology for the design and selection of suitable end-effectors for harvesting different categories of fruit and vegetable crops.

To have a deeper understanding on the moisture migration patterns and microstructural changes of Green Sichuan Pepper during the hot-air drying process, the low-field nuclear magnetic resonance (LF-NMR) technology and scanning electron microscopy (SEM) methodology were adopted to analyze the moisture distribution, migration patterns and microscopic structural changes under different drying temperatures (45, 55 and 65 °C). The LF-NMR scanning results showed that the internal moisture of the Green Sichuan Pepper mainly includes bound water, immobilized water and free water, which can be respectively symbolized by the relaxation time ranges of T₂₁ (0.1–10 ms), T₂₂ (10–500 ms) and T₂₃ (500–10,000 ms). The immobilized water accounts for 83.72% of the internal water, resulting in the significant drying difficulty of Green Sichuan Pepper. During the drying process, the content of immobilized water and free water exhibited a decreasing trend, while the bound moisture content initially increased and then decreased. In addition, the LF-NMR analysis showed that the parameters peak area A₂ demonstrated a high correlation with the moisture content of Green Sichuan Pepper, enabling the prediction of moisture content changes during the drying process. Additionally, the SEM results showed that the pore degree and pore density on the pericarp surface of Green Sichuan Pepper perform significant changes during the drying process, which might be a good explanation for revealing some commonly recognized drying phenomena on Green Sichuan Pepper hot-air drying. In summary, the findings presented in the present work provide some new insights into the moisture distribution, migration patterns and microstructural changes of Green Sichuan Pepper, which can offer theoretical guidance for optimizing the drying process of Green Sichuan Pepper.

Personalized precision medicine is a new direction for medical development, and advanced manufacturing technology can provide effective support for the development of personalized precision medicine. Based on the layered accumulation manufacturing principle, 3D printing technology has unique advantages in personalized rapid manufacturing, and can form complex geometric shape parts at low cost and high efficiency. This article introduces the application progress of 3D printing technology in medical models, surgical navigation templates, invisible aligners, and human implants, analyzes their advantages and limitations, and provides an outlook for the development trend of 3D printing technology in precision medicine.