Crop protection machinery plays a crucial role in modern agricultural production. Advanced plant protection machinery is conducive to optimizing pesticide utilization, improving food quality, and supporting sustainable agricultural practices. Globally, pesticide application products from the United States, Western Europe, and Japan are at the leading level internationally. The relevant products have a high degree of automation and intelligence, and the operation efficiency and effect are generally better than the domestic level. They occupy the vast majority of the market share. By comparison, China demonstrates technical advantages primarily in crop protection drones. To accurately capture the current research status and future directions of crop protection machinery, this article reviews the development history of prominent crop protection equipment, including ground, aerial, and field management robots, both at home and abroad. According to the needs and characteristics of field and orchard pest control, plant protection machinery is subdivided into three technical branches: pesticide application, components, and intelligence. The application technology methods such as variable pesticide application, target pesticide application, electrostatic spray, and wind curtain air delivery are summarized, the technical principles of equipment such as chassis, spray boom balance, and robot drive are analyzed, and the application of intelligent technologies such as drone driving, line walking, and visual navigation are explained. From the perspective of remote management, it also explains the intelligent construction of operation planning, scheduling decision-making, operation evaluation, and digital platform. Comparison of domestic and international technology levels suggests that the future development of plant protection machinery should focus on low-volume, precise pesticide application methods, specialized equipment components, and autonomous, intelligent operation modes. As artificial intelligence continues to advance, plant protection equipment with capabilities for intelligent perception, analysis, decision-making, and automation is expected to dominate future development.

Appearance quality is an important indicator in assessing the quality of dried chrysanthemums. To achieve rapid and non-destructive detection of the appearance quality of chrysanthemums during drying, this study applied computer vision technology in the infrared-assisted hot air drying of chrysanthemums and developed a Python-based image processing algorithm to acquire information on the changes in the shrinkage and color of petals and stamens of chrysanthemums at different temperatures (35 ℃, 50 ℃, and 65 ℃). These parameters serve as evaluation indices for the appearance quality and facilitat precise control of the drying process. The kinetic analysis showed that the drying of chrysanthemums had a consistently decreasing drying rate. High drying temperatures significantly reduced drying time and increased drying rates (p<0.05). Evaluation of the fit of mathematical models for thin-layer drying, including the Henderson and Pabis model, the Page model, and the Lewis model, showed that these models were in better agreement with the experimental data and thus more accurately described the drying process of chrysanthemums. Furthermore, changes in shrinkage rate and lightness (L*), red/green (a*), and yellow/blue (b*) values during drying showed that the morphological and color of chrysanthemums depended on drying temperature and time. Lower temperatures and shorter drying times were favorable for maintaining the appearance quality of the chrysanthemums. Linear regression analysis using zero-order, first-order, and first-order fractional models showed that the first-order fractional model provided more accurate predictions of shrinkage and color changes during the drying process of chrysanthemums.

Propelled by the era of big data, intelligent question-answering systems have found widespread applications across various domains, offering users a means to swiftly and efficiently acquire answers. Compared to traditional methods of collecting textual knowledge and utilizing web search engines, these systems demonstrate distinct advantages. With the rapid evolution of knowledge graph technology, intelligent question-answering systems have entered a new stage of development. In response to the call for smart agriculture, this study has undertaken the construction of a knowledge-based question-answering system based on knowledge graphs, aiming to provide users with question-answering services related to crop diseases and pests. The primary tasks include: (1) Acquisition of crop disease and pest data: Employing a distributed web crawling framework to retrieve data from web pages related to crop diseases and pests and preprocessing operations involve data cleaning, analysis, and structuring. (2) Construction of knowledge graph: Following data analysis, defining entity and relationship categories for the knowledge ontology and completing the pattern layer construction of the knowledge graph. Utilizing rule-based triple templates for extracting entities from semi-structured text, building the data layer, and storing triples in the Neo4j graph database. (3) Design of question-answering algorithms: Employing the BERT-BiLSTM-CRF model for question entity recognition and the BERT-RNN model for question classification. Matching templates, executing queries through Cypher statements, and processing answers into natural language forms for return. (4) Implementation and visualization of the question-answering system: Integrating crop disease and pest knowledge graphs with question-answering algorithms, the Flask framework and various web technologies are used to implement functionalities such as user questioning, entity recognition, knowledge retrieval, and answer presentation. Experimental results indicate that the entity recognition and question classification models achieved Precision (P), Recall (R), and F1 scores of 93.22%, 92.69%, 92.21%, and 94.37%, 93.92%, 92.66%, respectively. Compared to other search methods, the question-answering system demonstrated higher accuracy and stability. This study provides an intelligent solution for agricultural informationization, offering a new pathway for users to acquire knowledge on crop diseases and pests.

The orchards usually have rough terrain, dense tree canopy and weeds. It is hard to use GNSS for autonomous navigation in orchard due to signal occlusion, multipath effect, and radio frequency interference. To achieve autonomous navigation in orchard, a visual navigation method based on multiple images at different shooting angles is proposed in this paper. A dynamic image capturing device is designed for camera installation and multiple images can be shot at different angles. Firstly, the obtained orchard images are classified into sky and soil detection stage. Each image is transformed to HSV space and initially segmented into sky, canopy and soil regions by median filtering and morphological processing. Secondly, the sky and soil regions are extracted by the maximum connected region algorithm, and the region edges are detected and filtered by the Canny operator. Thirdly, the navigation line in the current frame is extracted by fitting the region coordinate points. Then the dynamic weighted filtering algorithm is used to extract the navigation line for the soil and sky detection stage, respectively, and the navigation line for the sky detection stage is mirrored to the soil region. Finally, the Kalman filter algorithm is used to fuse and extract the final navigation path. The test results on 200 images show that the accuracy of visual navigation path fitting is 95.5%, and single frame image processing costs 60 ms, which meets the real-time and robustness requirements of navigation. The visual navigation experiments in Camellia oleifera orchard show that when the driving speed is 0.6 m/s, the maximum tracking offset of visual navigation in weed-free and weedy environments is 0.14 m and 0.24 m, respectively, and the RMSE is 30 mm and 55 mm, respectively.

Apple orchard production management is faced with the double challenge of rising labor costs and population aging. In order to improve operational efficiency, reduce production costs and improve fruit quality, the application of mechanization and intelligent technology in apple orchard production management is becoming more and more important. This paper summarizes the research progress of intelligent mechanization technology in apple orchard production and management under the conditions of wide rows and dense planting and inter-logging and stemming cultivation mode; and the research progress of mechanization technology and equipment and intelligent technology and equipment in key links such as orchard mowing, herbicide application, flower and fruit management, fruit harvest and transportation, and fruit tree branch processing is analyzed. it is found that although the mechanization degree of irrigation and fertilization is relatively high at this stage, pruning, flower thinning, fruit thinning, bagging, picking and other links still rely on manual labor and have a low degree of mechanization; and the application of intelligent technologies such as drones and the Internet of Things in orchard management is not yet widespread, and precision agriculture and automated control need to be strengthened; the existing technologies develop independently and lack effective integration, which affects the improvement of production efficiency and fruit quality; the technology integration is not high and the data collection and analysis capabilities are insufficient. For the current stage of intelligent orchard construction technology model, we need to research and develop orchard machinery and equipment adapted to different terrain conditions, and create a demonstration base for the whole process of mechanization; focus on research and development of mechanized intelligent technology for key production links, and promote easy-to-manipulate intelligent machinery; use multi-source information sensing equipment to realize the digital perception of orchard environmental information; carry out intelligent decision-making and precise operation to improve operational efficiency and quality, and realize mission tasks through the intelligent orchard operation and maintenance system. Intelligent orchard operation and maintenance system realizes the demand for task allocation and scheduling, operation control and monitoring, and provides technical support for building typical application scenarios of mechanization of the whole apple orchard production.

Aiming at the problems of difficult weeding operation, low weeding efficiency and low level of intelligence in orchards in hilly and mountainous areas, a small mountain orchard weeding robot was designed. In order to improve the tracking accuracy of the tracked weeding robot's operation path, the research of tracked weeding robot's path tracking control algorithm was carried out. The ‘inverted triangle swivel structure’ was used to design an imitation floating chassis, which carried a ‘Y-type’ blade assembly for crushing and weeding and powered by an extended-range hybrid power system, and a slope steering control strategy based on model predictive control was proposed to address the problem of large slope steering slippage. The round-trip path planning method was used to plan the full coverage path of the orchard. Combined with BDS positioning and navigation technology and full coverage path planning, the crawler weeding robot was ensured to have high tracking accuracy during operation. The kinematic model of the crawler weeding robot was constructed, and the steering dynamics and control strategy of the crawler weeding robot on the slope were simulated and analyzed in MATLAB software. The simulation results show that the average error of the designed slope steering model is only 0.039 m under the condition of 15°, which demonstrates a good accuracy. Field experiments show that the MPC controller proposed in this paper can effectively improve the path tracking effect under slope steering conditions, and the average error under uphill and downhill conditions is reduced by 51.76%and 63.77%, respectively, compared with the PID controller when the slope angle is 15°. The weeding robot integrated with BeiDou navigation function has an effective weeding rate higher than 97% and can walk normally on a 25° slope. The research results provide a reference for the development of weeding robots in hilly and mountainous areas.

Cable-driven parallel robots have become a hot research topic in the field of robotics in recent years due to the characteristics of simple structure, small motion inertia, reconfigurability, and fast response speed, etc. The cable-driven flexible manipulator provides a certain adaptability and softness for operation due to the flexibility and elasticity of driving cable, and consequently it can achieve good dynamic interaction between the operating objects and the manipulators, and improve the ability to prevent broken loss coupling, which has also attracted widespread attention from researchers in the field of agricultural robotics. Due to the working environment of agricultural picking robots has the characteristics of unstructured and uncertainty, and most of the fruit outer skin is fragile and easy to damage, the requirements for picking robots that come into directly contacted with the fruits are stricter, ensuring stable grasping without damaging the fruits. In order to achieve efficient and precise operations of picking robots, lightweight and flexible end execution agencies that interact with biological friendly interaction are key theoretical technologies that urgently need to be broken through. This article first elaborates on the characteristics and development overview of cable-driven flexible manipulators with commercial application prospects. Then, the research progress of the theory such as the design, modeling and control of cable-driven flexible robotics at home and abroad is reviewed in detail. Further the typical application development of cable-driven flexible robots in the field of physical rehabilitation and dexterous grabbing is outlined. A detailed analysis is conducted on the significance of the application of cable-driven flexible manipulators in the field of agricultural picking, and the current research status of cable-driven flexible manipulators in tomato picking, apple picking, strawberry picking, and blackberry picking. Aiming at the problems of low picking accuracy, poor versatility, high cost, damage and low picking efficiency of cable-driven flexible manipulator in agricultural picking at the present stage, it is pointed out that in the future, the commercialization of cable-driven flexible manipulator needs to make innovations in agricultural machinery and agronomy collaboration, modular and reconfigurable design, improving interaction safety, multi-sensor integration, picking sequence planning and strong robust control in this study and it provides new ideas and methods for the design of mechanized non-destructive picking mechanisms for fruits and vegetables.