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.

As the raw material for tea making, the quality of tea leaves directly affects the quality of finished tea. The quality of fresh tea leaves is mainly assessed by manual judgment or physical and chemical testing of the content of internal components. Physical and chemical methods are more mature, and the test results are more accurate and objective, but traditional chemical methods for measuring the biochemical indexes of tea leaves are time-consuming, labor-costly, complicated, and destructive. With the rapid development of imaging and spectroscopic technology, spectroscopic technology as an emerging technology has been widely used in rapid non-destructive testing of the quality and safety of agricultural products. Due to the existence of spectral information with a low signal-to-noise ratio, high information redundancy, and strong autocorrelation, scholars have conducted a series of studies on spectral data preprocessing. The correlation between spectral data and target data is improved by smoothing noise reduction, correction, extraction of feature bands, and so on, to construct a stable, highly accurate estimation or discrimination model with strong generalization ability. There have been more research papers published on spectroscopic techniques to detect the quality of tea fresh leaves. This study summarizes the principles, analytical methods, and applications of Hyperspectral imaging (HSI) in the nondestructive testing of the quality and safety of fresh tea leaves for the purpose of tracking the latest research advances at home and abroad. At the same time, the principles and applications of other spectroscopic techniques including Near-infrared spectroscopy (NIRS), Mid-infrared spectroscopy (MIRS), Raman spectroscopy (RS), and other spectroscopic techniques for non-destructive testing of quality and safety of fresh tea leaves are also briefly introduced. Finally, in terms of technical obstacles and practical applications, the challenges and development trends of spectral analysis technology in the nondestructive assessment of tea leaf quality are examined.

Fresh aquatic products, due to their high water activity, are susceptible to microbial contamination and spoilage, resulting in a short shelf life. Drying is a commonly used method to extend the shelf life of these products by reducing the moisture content, inhibiting microbial growth, and slowing down enzymatic and chemical reactions. However, the drying process of aquatic products involves chemical reactions such as oxidation and hydrolysis, which pose challenges in obtaining high-quality dried products. This paper provides a comprehensive review of drying processing techniques for aquatic products, including drying preprocessing, drying technologies, and non-destructive monitoring techniques, and discusses their advantages and challenges. Furthermore, the impact of the drying process on the quality attributes of dried products, including sensory quality, nutritional components, and microbial aspects, is analyzed. Finally, the challenges faced by drying processing techniques for aquatic products are identified, and future research prospects are outlined, aiming to further advance research and innovation in this field.

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.

In order to improve the efficiency of fine segmentation and obstacle removal in the road of tea plantation in hilly areas, a lightweight and high-precision DR-YOLO instance segmentation algorithm is proposed to realize environment awareness. Firstly, the road data of tea gardens in hilly areas were collected under different road conditions and light conditions, and data sets were generated. YOLOv8n-seg, which has the highest operating efficiency, was selected as the basic model. The MSDA-CBAM and DR-Neck feature fusion network were added to the YOLOv8-seg model to improve the feature extraction capability of the network and the feature fusion capability and efficiency of the model. Experimental results show that, compared with the YOLOv8-seg model, the DR-YOLO model proposed in this study has 2.0% improvement in AP@0.5 and 1.1% improvement in Precision. In this study, the DR-YOLO model is pruned and quantitatively compressed, which greatly improves the model inference speed with little reduction in AP. After deploying on Jetson, compared with the YOLOv8n-seg model, the Precision of DR-YOLO is increased by 0.6%, the AP@0.5 is increased by 1.6%, and the inference time is reduced by 17.1%, which can effectively improve the level of agricultural intelligent automation and realize the efficient operation of the instance segmentation model at the edge.

Conveyor belts are widely used in ore transportation in large-scale mines for their long transportation range, high safety, and strong economic applicability. Coal mine belt conveyors are not only traditional, simple mechanical conveying devices but also automatic control system operating devices that integrate safety, stability, and low power consumption. In the process of coal mining, a conveyor belt control system also needs to be closely integrated with modern industrial systems and information systems, which greatly improves its work efficiency. The purpose of this article is to improve the methods for designing automatic control systems for electric motors in order to obtain mechanical characteristics close to a constant power line, which would ensure the reliable operation of belt conveyors. An automatic control system was designed based on the controller Siemens S7-1200; then, a mathematical model of an automated electric drive was developed. Based on the mathematical model, a simulation model of an automatic electric drive was built, and the modes were modeled. After designing, the obtained plots of transients completely corresponded to the required transients, which means that the Siemens frequency converter automatic control system (ACS) parameters were calculated quite accurately and the conveyor electric drive met all requirements.