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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.
Laser， as an emerging artificial light source， is one of the greatest inventions of the 20th century， featuring high power density， excellent directionality， and superb monochromaticity. It has been widely applied in the agricultural sector. This article provides an in-depth analysis of the research progress and development trends of laser technology in agriculture. In plant production， it discusses applications in plant mutation breeding， promoting plant growth and development， enhancing yield and quality， plant protection， plant detection， and phenotyping. In animal management， the focus is on the use of laser technology in animal genetic breeding， growth， medical care， and product testing. For fungal research， it summarizes the use of laser technology in fungal breeding， growth， detection， and identification. The article also addresses the challenges and difficulties faced when integrating laser technology with agricultural production， proposing directions for future development， such as improving precision in plant production， optimizing animal management， and advancing scientific fungal production. Although laser technology has already achieved significant results in agriculture， future research should explore more innovative applications and integrate with artificial intelligence and big data technologies to further advance the deep integration of laser technology with the agricultural industry， leading to more efficient and intelligent agricultural production management and offering new opportunities and breakthroughs for modern agriculture.
As agricultural production continues to increase its requirements for field management， traditional agricultural machinery and equipment have gradually become difficult to meet the production needs of modern smart agriculture. In this context， soil moisture monitoring technology， as a key means of obtaining soil moisture information in modern agricultural management， is playing an important role in promoting the development of agricultural machinery towards intelligence and automation. Therefore， this study thoroughly reviews and analyzes the current research status of soil moisture monitoring technology at home and abroad， focusing on the research progress of three aspects of soil moisture monitoring methods and principles， model construction algorithms， and signal processing methods. Through comparative analysis， the differences and similarities in monitoring methods， principles， model construction algorithms， and signal processing methods at home and abroad， as well as the problems and challenges in practical applications， are summarized. The future development trends of soil moisture monitoring technology in these three aspects are proposed： in terms of soil moisture monitoring methods， a multi-source soil moisture monitoring information platform is constructed to achieve more comprehensive data collection and analysis； In terms of model construction algorithms， machine learning and deep learning algorithms are adopted to customize model algorithm modules for different soil environments and working scenarios， improving the accuracy and applicability of monitoring equipment； In terms of signal processing， the application of multi-source signal fusion technology is strengthened to reduce the impact of the working environment on monitoring equipment.
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 cold chain in the production area of fruits and vegetables is the primary link to reduce product loss and improve product quality， but it is also a weak link. With the application of big data technology in cold chain logistics， intelligent devices， and technologies have become important carriers for improving the efficiency of cold chain logistics in fruit and vegetable production areas， extending the shelf life of fruits and vegetables， and reducing fruit and vegetable losses. They have many advantages in fruit and vegetable pre-cooling， sorting and packaging， testing， warehousing， transportation， and other aspects. This article summarizes the rapidly developing and widely used intelligent technologies at home and abroad in recent years， including automated guided vehicle intelligent handling based on electromagnetic or optical technology， intelligent sorting based on sensors， electronic optics， and other technologies， intelligent detection based on computer vision technology， intelligent transportation based on perspective imaging technology， etc. It analyses and studies the innovative research and achievements of various scholars in applying intelligent technology in fruit and vegetable cold chain storage， sorting， detection， transportation， and other links， and improves the efficiency of fruit and vegetable cold chain logistics. However， applying intelligent technology in fruit and vegetable cold chain logistics also faces many problems. The challenges of high cost， difficulty in technological integration， and talent shortages have limited the development of intelligent technology in the field of fruit and vegetable cold chains. To solve the current problems， it is proposed that costs be controlled through independent research and development， technological innovation， and other means to lower the entry threshold for small enterprises. Strengthen integrating intelligent technology and cold chain logistics systems to improve data security and system compatibility. At the same time， the government should introduce relevant policies， provide necessary financial support， and establish talent training mechanisms. Accelerate the development and improvement of intelligent technology standards in the field of cold chain logistics. Through technological innovation， cost control， talent cultivation， and policy guidance， we aim to promote the upgrading of the agricultural industry and provide ideas for improving the quality and efficiency of fruit and vegetable cold chain logistics.
To effectively identify tea buds in complex environments and improve the precision of intelligent harvesting while minimizing damage to tea trees， this study addresses the issues of low detection accuracy and poor robustness exhibited by traditional target detection algorithms in tea gardens， and proposes YOLOv7-tea model for tea bud identification and detection based on an improved YOLOv7， so as to achieve rapid recognition and detection of tea buds.First， tea bud images were collected and annotated， and data augmentation was performed to construct a tea bud dataset. Next， the CBAM attention mechanism module was introduced into three feature extraction layers of the YOLOv7 backbone network to enhance the model's feature extraction capability； the SPD-Conv module was used to replace the SConv module in the neck network's downsampling module to reduce the loss of small object features； and the EIoU loss function was employed to optimize box regression， thereby improving the accuracy of the predicted boxes. Finally， a comparative experiment was conducted between other target detection models and the YOLOv7-tea model using the tea bud image dataset as a sample， and the recognition effect of tea buds shot at different distances and angles was tested.The experimental results show that the YOLOv7-tea network model outperforms the YOLOv7 model in terms of precision （P）， recall （R）， and mean average precision （mAP） by 2.87， 6.91， and 8.69 percentage points， respectively. Additionally， the model has a faster detection speed and exhibits higher confidence scores in the recognition and detection of tea buds in complex backgrounds.The YOLOv7-tea model constructed in this study demonstrates better recognition performance for small-sized tea leaf buds， reducing instances of missed detection and false alarms. It exhibits good robustness and real-time performance， offering valuable insights for estimating tea yield and implementing intelligent harvesting.
In China， the hilly and mountainous areas account for 34.62% of the total cultivated land and 34.20% of the total crop sown area， covering 54.2% of the population， while contributing only 30% to the national GDP. With the overall agricultural mechanization level below 50%， it is urgently needed to accelerate the development of agricultural mechanization in hilly and mountainous areas to build an agricultural powerhouse. Based on the agricultural tillage system zoning in China， this paper conducts a zoning of agricultural mechanization in hilly and mountainous areas， taking into account factors such as the distribution of hilly and mountainous areas. Based on the resource endowment conditions， economic and social conditions and industrial economic benefits， this paper summarizes the development status of agricultural mechanization， analyzes the special scenarios of agricultural machinery application from the aspects of cultivated land conditions， soil and agronomy， and analyzes the constraints and bottlenecks of agricultural mechanization development in combination with the factors of agricultural machinery research and development， manufacturing， promotion and application in hilly and mountainous areas of China. In the context of the application scenario in hilly and mountainous areas， ten key agricultural machinery general technology needs are proposed， such as compact and lightweight specialized engines， lightweight and anti-adhesion and anti-friction materials， and simplified machine structures. Ten applicable agricultural machinery equipment needs， such as efficient and low-cost tillage equipment in sticky soil， seeding equipment for grain， oil， sugar crops， and vegetables in mountainous areas for heavy and sticky soil in gentle slopes， and transplanting equipment for rape and vegetables in mountainous areas for heavy and sticky soil in gentle slopes， are also discussed. Drawing on the experience of agricultural mechanization in hilly and mountainous areas of Japan， Republic of Korea， etc.， this paper puts forward a systematic approach to promoting agricultural mechanization in hilly and mountainous areas， focusing on the integration of good machines， good seeds， good methods， good farmland， and good systems， and the four-in-one approach of research， production， promotion， and use. This approach aims to promote the high-quality development of agricultural mechanization in hilly and mountainous areas.
Rice seedlings need sufficient light for normal growth. The increasing temperature demand of early spring seedling has promoted the development of greenhouses， greenhouses and factory facilities. However， due to the reflection and absorption of sunlight by glass and plastic， the lack of light in facility seedlings is prominent. Supplementary lighting can better solve the problem of lack of light in facility seedling， LED as the representative of the supplementary light source has been promoted and applied in facility seedling， but it still has high energy consumption problem. Laser is the only artificial light source with parallel light characteristics of sunlight， with good correlation， good monochromism， good directivity， high brightness， large energy， photoelectric conversion efficiency， energy saving and so on. Studies have shown that the special light quality of laser can effectively improve the photosynthesis efficiency of seedlings. The traditional laser light source represented by He-Ne laser is large in volume， high in cost， and difficult to be popularized. Using semiconductor laser combined with uniform light technology to develop a new laser light source， small size， low cost， can achieve a large area of uniform laser irradiation of rice seedlings， the single lamp irradiation area can reach 60-70 square meters. At the same time， the new laser light source retains the traditional laser correlation， monochromism and directivity， and also has the characteristics of high efficiency and energy saving， and the energy consumption can be only 1/30 of the traditional LED light source. The results showed that reasonable irradiation of new laser light source in the process of rice seedling cultivation can improve the quality of seedlings， fast greening after transplanting， more tillers， early heading， and finally achieve stable and increased rice yield. Since 2021， the test results in many places across the country have shown that the use of a new laser light source to irradiate rice seedlings for about 20 days before transplanting can achieve a yield increase of more than 10%. This result not only provided important technical support and guarantee for improving the quality and yield of rice in China， but also provided a new method and means for other plants to fill light at various stages.
In order to address the issues of large inclination angles and poor pass ability encountered by small-scale combine harvesters operating in hilly regions， a design and experimental study were conducted on an automatic lifting hydraulic system for the combine's chassis. This research developed the overall structure of the automatic lifting hydraulic system for the chassis， designed and selected key components such as hydraulic cylinders with built-in displacement sensors and directional proportional valves， and proposed methods for leveling and lifting the chassis by extending or retracting the hydraulic cylinders based on the transverse inclination angle. The study employed two leveling modes： automatic and manual， to level the chassis. The hydraulic system was simulated using AMEsim software， revealing stable pressure and flow within the hydraulic cylinders during direction reversing， along with smooth displacement velocity. A leveling control strategy centered on a PLC controller and PID control method was designed， which adjusted the length of the hydraulic cylinders to regulate the chassis's attitude and to conduct a static leveling experiment. Results indicated the proportional directional valve， when adjusted to a position between 20% and 45%， it can effectively regulate the velocity of both the extension and retraction movements of the hydraulic cylinders. The adjustment time increased with larger inclination angles， but the overall adjustment error remained below 0.5° within an inclination range of 7.7°， which can meet the requirements for transverse leveling of the combine harvester's chassis. This research introduced a hydraulic system and control method to realize leveling based on the transverse angle， providing a reference for the design of the chassis leveling hydraulic system for small-scale combine harvesters.
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.
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.
Nitrogen is one of the important elements for the growth and development of rice， and accurate estimation of nitrogen concentration is crucial for guiding precise fertilization and assisting in the selection of nitrogen efficient varieties in rice. Traditional field sampling methods make it difficult to obtain real-time nitrogen concentration in rice. With the rapid development of information technology， establishing the relationship between unmanned aerial vehicle hyperspectral data and nitrogen concentration through machine learning methods is currently one of the main technical routes for crop nitrogen nutrition diagnosis. This study constructs an inversion model by using the feature bands of unmanned aerial vehicle canopy hyperspectral data selected by the continuous projection algorithm as input and the measured nitrogen concentration data as output. Extreme learning machine （ELM） has the advantages of fast speed and strong generalization ability compared to similar machine learning methods. However， due to its randomly generated connection weights and neuron thresholds， its training stability is insufficient and it is prone to falling into local optima. The beluga whale optimization （BWO） is a competitive algorithm inspired the behavior of beluga whales to solve single modal and multimodal optimization problems. In this study， the BWO-ELM rice nitrogen concentration unmanned aerial vehicle hyperspectral inversion model was constructed to achieve rapid estimation of rice nitrogen concentration by optimizing the connection weight between the input layer and the hidden layer of the ELM， as well as the initial weight of the hidden layer through the BWO. The research results show that the continuous projection algorithm filters out 10 feature bands， which are 673， 703， 727， 823， 850， 877， 895， 952， 961， and 985 nm， respectively. The training set R2 and RMSE of the nitrogen concentration inversion model constructed based on BWO-ELM are 0.742 5 and 0.382 6%， respectively， while the testing set R2 and RMSE are 0.702 8 and 0.487 7%， respectively. The predictive ability is superior to that of the nitrogen concentration inversion model constructed based on ELM. In summary， the rice nitrogen concentration unmanned aerial vehicle hyperspectral inversion model based on BWO-ELM can quickly and accurately obtain rice nitrogen concentration， providing a new method for rice nutrition monitoring.
The realization of automatic identification and positioning of tea buds is the basis for the development of high-quality tea intelligent picking equipment. Aiming at the problems of tiny tea buds and the picking environment which is greatly affected by light， this research proposes a tea bud recognition method based on a deep learning network model to carry out the light source design of the recognition system， which can provide technical support for the realization of all-weather and high-efficiency intelligent tea bud picking equipment. First， an aluminum alloy frame was constructed to provide a closed and shaded dark environment； then， three combinations of heights and three combinations of light intensities were created by adjusting the height of the crossbar and the brightness of the light source； finally， the image datasets of tea buds in different combinations were collected， and recognition tests were carried out on one bud and one leaf and one bud and two leaves by using the improved YOLOv5 model. The experimental results show that the overall accuracy of YOLOv5s is 77.13%， and the overall average precision mean is 86.14%， the overall accuracy of the improved recognition model YOLOv5s-SPD is 80.30%， and the overall average precision mean is 87.3%， and the average detection time of a single image is 5.7 ms， which meets the requirement of real-time detection， and is better than the original YOLOv5s with an overall accuracy improvement of 3.17% and an overall average precision mean of 1.16%， which effectively improves the recognition performance of tea buds. Under the condition of height 90 cm and luminance L7（0.164~0.328 μmol/m2）， the detection accuracy， recall and AP average of one bud and two leaves were 86.70%， 92.45% and 95.00%， respectively. The method proposed in this paper can effectively and quickly detect tea buds， and the light source design scheme supports the development of all-weather high-quality tea intelligent picking equipment.
Panax Notoginseng is one of the most extensively cultivated and utilized bulk medicinal materials in China， and Yunnan Province is the main producing area for Panax Notoginseng in the country. Due to the influence of terrain and agronomic requirements， traditional agricultural machinery faces difficulties in entering the planting areas for operation， resulting in the current manual operation of Panax Notoginseng transplantation. Therefore， the development of a Panax Notoginseng transplanting machine is crucial for promoting the industrialization of Panax Notoginseng. As the crucial load-bearing structure of the transplanting machine， the frame significantly affects the overall performance of the vehicle. This paper focuses on the structural analysis of the transplanting machine frame， aiming to improve the performance of the frame and the entire vehicle， and provide theoretical basis for frame structural design. A three-dimensional model of the frame is established using SolidWorks software and imported into ANSYS software for static finite element analysis. After determining the relative error range between the stress values obtained from static electrical tests and experimental stress values， the dynamic loading performance of the frame under different working conditions and the modal vibration deformation analysis of the first eight modes are conducted. The analysis results indicate that the frame exhibits good strength performance， but significant deformation occurs at the seat position， indicating insufficient stiffness of the structure. Topology optimization is employed to optimize the frame design， aiming to reduce frame deformation while ensuring reasonable stress distribution. By altering the arrangement of diagonal brace brackets， the goal of reducing deformation is achieved. According to the optimization design results， the total mass of the frame increases by 8.739%， while the deformation is reduced by 88.268%， and the maximum stress is decreased by 11.693%. The improved frame exhibits reduced maximum stress and significantly improved deformation， demonstrating the applicability of finite element method and topology optimization technology in guiding the structural design of transplanting machine frames.
Traditional angle sensors have problems such as insufficient accuracy and complex installation and debugging when measuring the deflection angle of tractor guide wheels， which hinder the high precision and stability requirements of tractor automatic navigation operations. Therefore， this paper aims to construct and validate a novel system based on the principles of Global Navigation Satellite System （GNSS） and Inertial Navigation System （INS）， which is capable of real-time calculation of dynamic angles of the tractor's steering wheel. First， a in-depth analysis of GNSS/INS fusion principles was conducted， and corresponding GNSS/INS data measurement bench tests were designed to obtain measurement values from angle sensors. Subsequently， three filtering algorithms， namely Kalman Filter （KF）， Extended Kalman Filter （EKF）， and Unscented Kalman Filter （UKF）， were employed to filter the angle measurement values， evaluating metrics such as overshoot， response time， and stability， and the results showed that UKF performed best in this system. Second， steering and straight-line performance tests were conducted under different conditions （asphalt road and farmland） for tractor automatic navigation. In the steering performance tests， fixed measurements at different angles of 1°， 5°， and 15° were conducted， and the results showed that the response time of the two environments was the smallest at 1°， but both produced the largest overshoot. At the same time， an increase in angle demonstrated a significant reduction in steady-state error. In the straight-line automatic navigation performance tests， the maximum lateral deviations for asphalt road and farmland were 17.51 mm and 18.52 mm， respectively， with system errors of 10.45 mm （2σ） and 21.07 mm （2σ）. A comparison between the two conditions indicated superior performance metrics on asphalt road， yet both conditions met the requirements of precision， response time， and stability for automatic navigation systems， thus suitable for tractor automatic navigation operations.
In response to the problems of poor speed stability and degraded spraying quality of 4WID high-clearance self-propelled electric sprayers caused by changes in external road gradients and lowered payloads resulted from internal liquid spraying under complex operating conditions， a fixed-speed cruise control algorithm with a layered control approach is proposed based on an analysis of the structure and longitudinal dynamic characteristics of 4WID high-clearance self-propelled electric sprayers. The control algorithm model receives the user-specified desired speed and inputs acceleration control signals to the longitudinal dynamic system model through algorithmic calculation to realize the tracking of the sprayer to the desired speed. The structure of the longitudinal dynamic system mainly includes five parts： the inverse longitudinal kinematic model， the acceleration-braking switching model， the torque allocation model， the motor model， and the longitudinal kinematic model. The inverse longitudinal kinematic and longitudinal kinematic models of the sprayer can be obtained by analyzing the force on the body of the sprayer under the condition of driving on a slope. Meanwhile， in order to establish a reasonable four-wheel torque allocation strategy， the analysis is conducted under the condition that the sprayer has both pitching and tilting motions of the body， and the slip rate of each driving wheel is used as the basis for allocation， ensuring the optimal torque moment of each wheel under different operating conditions and ensuring the balanced power of the sprayer. The speed cruise control adopts a layered control approach， which realizes the effective tracking of the speed of the spraying machine through the establishment of the upper PID control and the lower fuzzy PID control. By defining fuzzy control rules， the PID parameters of the lower layer controller are automatically adjusted to ensure the good adaptability of the speed cruise control system to various complex operating conditions. A control model is established using Matlab/Simulink， and the control system is simulated and analyzed. The experimental results show that the designed speed cruise control system can effectively control the speed of the sprayer under typical operating conditions. Specifically， the performance of the system is excellent under conditions of external disturbance and self-weight variation， with overshoot less than 2%， response time less than 0.2 s， and steady-state error approaching 0， verifying the accuracy of the control algorithm used.
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.
With the advancement of agriculture， the economic losses and food safety threats caused by major plant diseases and pests to agriculture and forestry are becoming increasingly severe. In 2024， China's main food crops face challenges from 22 types of diseases and pests， with an expected affected area of 155 411 khm2 and a yield loss exceeding 1.5×1011 t. This article provides an overview of the current status of crop disease and pest warning and emergency control technologies， where disease and pest warning technologies include the Internet of Things， remote sensing technology， and computer vision recognition technology； emergency control technologies cover the understanding of chemical communication mechanisms for the development of new pesticides and equipment， innovation in physical extermination and intelligent control products， utilization and product innovation of biological control resources， research on pest damage-type variation mechanisms and sustainable use of plant resistance varieties， research and development of pesticide resistance mechanisms and control technologies， as well as research and application technologies for the ecological regulation of diseases and pests. The aim is to achieve effective control of diseases and pests through interdisciplinary cooperation， while reducing environmental impact and promoting sustainable agricultural development. The article also discusses the application of disease and pest warning and emergency control technologies in the agricultural field， analyzes their important role in the development of agricultural science and technology， and summarizes the challenges faced in each field， such as the insufficient accuracy of unmanned aerial vehicle remote sensing monitoring technology， difficulties in sample collection for disease and pest image automatic recognition technology， the failure of chemical control to achieve precise application， the lagging production processes and market promotion of green formulations， and the incomplete construction of disease and pest warning systems. In the future， the application of Internet of Things technology to disease and pest warning should be accelerated， the construction of smart agriculture and disease and pest warning systems should be promoted， and research should be conducted on unmanned aerial vehicle remote sensing monitoring technology， disease and pest image automatic recognition technology， chemical control technology， and biological control technology.
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.
