Precision agriculture increasingly relies on autonomous unmanned ground vehicles (UGVs) to handle complex tasks. Path planning is the key to empowering intelligent automation, directly improving efficiency and reducing energy consumption by generating globally optimal paths. However, traditional path planning algorithms typically rely on predefined rules or fixed parameter configuration, which limits their adaptability in complex and heterogeneous scenarios. To address these limitations, we propose a novel intelligent agent framework that employs large language models (LLMs) as the decision module for adaptive path planning. TheLLM-based agent path planning (Agri-LAPP) framework encodes the features of the grid map into structured descriptors as the input prompt. It then employs chain-of-thought (CoT) reasoning to analyze terrain characteristics and task constraints, dynamically selecting an appropriate planning algorithm from an algorithm library and tuning their parameters accordingly. The proposed framework effectively unifies map feature extraction, complexity modeling, prompt-based reasoning and path execution module under a coherent Agri-LAPP architecture. This hierarchical decision architecture enables Agri-LAPP to adapt its planning strategy without retraining or specific tuning. Extensive experiments on synthetic grid maps with increasing difficulty levels demonstrate that Agri-LAPP consistently outperforms classical planners in terms of overall planning quality and robustness. Specifically, Agri-LAPP outperforms all baseline methods, achieving average improvements of 7.58% in path length, 10.68% in path cost, and 5.9% in smoothness, while maintaining a success rates above 98% across all difficulty levels. These results indicate that Agri-LAPP produces smoother and more reliable trajectories than conventional planning methods, highlighting the benefit of LLM-based decision making in complex robotic path planning problems.

In response to the issues of high energy consumption, limited functionality, and uneven soil–fertilizer mixing in mechanical operations for trenching and fertilizing in hilly orchards, this study proposes the design of a crawler-type self-propelled machine, integrating three main functions: trenching, fertilizing, and soil covering. The key components of the trenching device, fertilizing device, and soil-covering device were designed. Three fertilizing simulation models (pre-plant, mid-plant, and post-plant) were established using EDEM discrete element software. The soil–fertilizer mixing effects under each mode were analyzed, with results indicating that the post-plant fertilizing mode better meets the soil–fertilizer mixing requirements for deep organic fertilizer application. Using trenching speed, forward speed, and bending angle of the trenching knife as experimental factors, with operating power consumption and soil–fertilizer mixing uniformity as evaluation indicators, a Box–Behnken experiment was conducted to optimize the parameters of the trenching and fertilizing components. A regression model was established to analyze the interaction between experimental factors and indicators. The optimal operational parameter combination was determined as follows: trenching speed of 265.03 r/min, forward speed of 0.40 m/s, and bending angle of trenching knife of 130°. Under these parameters, the trenching power consumption and soil–fertilizer mixing uniformity were 1.74 kW and 77.15%, respectively. Orchard verification tests on the machine showed that under the optimal parameters, the relative errors in trenching power consumption and soil–fertilizer mixing uniformity between the field tests and simulations were 7.40% and 4.50%, respectively. These results meet the agronomic requirements for trenching and fertilizing, and the study provides valuable references for the application of related technologies in orchard trenching and fertilizing operations.

Path planning is a fundamental challenge for autonomous robots, particularly in unstructured environments, where issues such as low search efficiency, suboptimal path quality, and local optima often arise. To address these challenges and enable a nonholonomic orchard robot to accomplish tasks safely and efficiently, this paper proposes a novel HPS-RRT* algorithm based on hybrid exploration and optimization mechanisms to enhance path planning performance. A hybrid sampling strategy adapted to the environmental characteristics is proposed to improve the search efficiency, and an extended step size based on Lévy distribution is designed to balance exploration and optimization. Moreover, a pruning strategy is incorporated to reduce redundant points during the search process, enhancing the efficiency of the exploration tree and reducing unnecessary expansion. Furthermore, a novel leader-based sparrow optimization algorithm is proposed to ensure that the planned path is suitable for the nonholonomic orchard robot. It can overcome the limitations of traditional smoothing methods by simultaneously optimizing curvature and path length. Compared with existing RRT*-based algorithms in environments of varying complexity, the proposed HPS-RRT* reduces the final path length by 1.7% to 27%, improves planning efficiency by 77.7% to 93.3%, and enhances path smoothness by 27.9% to 41.7%, while maintaining a 100% success rate. Furthermore, its feasibility for a nonholonomic orchard robot is validated through a multi-target planning task with curvature constraints.

To address the lack of mechanical orchard operation-aid platforms that assist in the horticultural management tasks such as pruning, spraying, thinning flowers and fruits, and harvesting in litchi and longan orchards, this paper proposes an orchard operation-aid platform specifically tailored for hilly and mountainous orchards. The platform is optimized for orchards with tree and row spacing not exceeding 6 m and slopes not exceeding 15°. By considering the planting patterns and operational topography parameters of litchi and longan, the key components were meticulously designed, including the chassis, lifting device, extension device, and slope operation support device. The driving stability, slope operation stability, and the reachable workspace of the orchard operation-aid platform were analyzed, followed by a prototype experiment. The results demonstrate that the platform achieves an in situ turning radius of 1.2 m with no deviation in the turning path. It satisfies the passability and operational slope requirements of hilly terrains with both driving and operational slopes exceeding 15°. Additionally, the platform features a working height of 4.0 m and an operating radius of 3.7 m, meeting the operational requirements for multiple tasks. This research provides a practical and effective solution for enhancing operational efficiency in multiple stages of fruit cultivation, demonstrating significant practical value and potential for widespread application.

To regulate the energy flow in orchard ecosystems and maintain the environment, weeding has become a necessary measure for fruit farmers, and the use of automated mowers can help reduce labor costs and improve the economic efficiency of orchards. However, due to the complexity of the geographic and spatial environment of the orchard, in particular, the loose and undulating road surface, the interference of satellite signals by large trees, etc., which decreases the positioning accuracy and stability of the positioning system of the mower, and the high cost of the sensor also affect the popularization of intelligent mowers for these applications. To address the above problems, this paper constructs a positioning system through a low-cost global navigation satellite system (GNSS), inertial measurement unit (IMU), and odometry, and utilizes the Kalman filter algorithm based on the error state for a combined GNSS/IMU positioning so that the inertial navigation system can maintain a more accurate positioning when the GNSS signals are poor. Considering the side-slip and error accumulation problems of the odometry of the traction mower, the combined GNSS/IMU positioning information is used to optimize the odometry model and improve the navigation and positioning accuracy. To reduce the measurement error of the IMU and the problem of error accumulation, this paper utilizes the nonholonomic constraint (NHC) of a lawn mower to suppress the dispersion of IMU measurement errors and constructs periodic and nonperiodic zero-velocity updating (ZUPT) strategies in combination with the travel paths of lawn mower navigation operations in the region to update the IMU data to improve the positioning accuracy and stability of the positioning system. The experiments show that the average error of the constructed positioning system is controlled within 0.15 m, the maximum error is maintained at approximately 0.3 m, and the positioning system constructed by using low-cost sensors can achieve a positioning accuracy similar to that of the differential global navigation satellite system (DGNSS), which is beneficial for the promotion and application of intelligent mowers in orchards.

Soil temperature is one of the important environmental factors for the underground parts of plants. It is important to detect soil temperature in agricultural production. Acoustic waves serve as effective carriers of soil information, providing a reliable means to detect soil physical properties. In order to detect the temperature of sandy loam soil based on acoustic technology, this study extends the application of Wyllie model to the temperature measurement of sandy loam soil. The relationship between soil temperature and acoustic velocity is explored. A model of soil moisture content-soil temperature-acoustic velocity (MTV) for temperature measurement in sandy loam soil is proposed by extending the temperature–velocity of sound relationship with the introduction of a variable empirical coefficient related to soil moisture β(θ). In order to determine the parameters of proposed model, a pulsed acoustic velocity detection system was built in this paper. The influence of temperature on the acoustic velocity in sandy loam soil was analyzed through experiments. Based on the experimental results, the key parameters of the MTV model are refined. Finally, a validation experiment was carried out on sandy loam soil. The maximum estimation error of soil temperature based on the MTV model for sandy loam soil temperature estimation is 8.55 %. The results indicate that the MTV model proposed in this study can be used for temperature estimation in sandy loam soil, providing a theoretical basis for the development of acoustic soil physical information detection sensors.

This study examined the characteristics of soil moisture movement and wetted body water distribution under drip irrigation conditions. The findings provided a crucial foundation for designing a precision drip irrigation control system for greenhouse tomatoes, enhancing control accuracy and conserving water. An indoor point source infiltration test was conducted to assess the impact of varying dripper flow rates and initial water content on soil moisture movement and wet body alterations. A mathematical model of wet front movement was established, and agricultural Internet of Things technology was utilized to design a precision drip irrigation control system for greenhouse tomatoes. This was then compared with a traditional drip irrigation control system. The results indicated that when wetted bodies reach the same wet depth, both irrigation time and total water consumption were influenced by flow rate and initial water content. Increasing the initial water content could effectively reduce both irrigation time and total water consumption. The Horizontal wetted front X value and the vertical wetted front Z value during the drip irrigation process exhibited a strong power function relationship with time, with R² exceeding 0.98. As irrigation time increases, the width-depth ratio of the wet body gradually transitioned from large to small, and increasing the initial water content aided in soil moisture vertical infiltration. After redistribution, the average water content in the wet body ranged between 68%𝜃𝐹𝐶 and 75%𝜃𝐹𝐶 , and the water uniformity coefficient exceeded 90%, aligning with normal growth conditions for tomatoes. The actual wet volume surpassed the planned wetted volume, and when the same initial water content was used, smaller total water consumption corresponded to smaller overflow volume. The target wetting depth is established based on the depth of tomato root distribution. When compared to the conventional drip irrigation control system, the precision drip irrigation control system developed in this study exhibited superior accuracy in wetting depth control and a more effective water-saving effect. The error in wetting depth during the flowering and fruit setting periods, as well as the fruiting period of the tomato, was reduced by 8.2% and 15.8% respectively, resulting in water savings of 16.2% and 22.6%.

IntroductionPath planning algorithms are challenging to implement with mobile robots in orchards due to kinematic constraints and unstructured environments with narrow and irregularly distributed obstacles.MethodsTo address these challenges and ensure operational safety, a local path planning method for orchard mowers is proposed in this study. This method accounts for the structural characteristics of the mowing operation route and utilizes a path-velocity decoupling method for local planning based on following the global reference operation route, which includes two innovations. First, a depth-first search method is used to quickly construct safe corridors and determine the detour direction, providing a convex space for the optimization algorithm. Second, we introduce piecewise jerk and curvature restriction into quadratic programming to ensure high-order continuity and curvature feasibility of the path, which reduce the difficulty of tracking control. We present a simulation and real-world evaluation of the proposed method.ResultsThe results of this approach implemented in an orchard environment show that in the detouring static obstacle scenario, compared with those of the dynamic lattice method and the improved hybrid A* algorithm, the average curvature of the trajectory of the proposed method is reduced by 2.45 and 3.11 cm–1, respectively; the square of the jerk is reduced by 124 and 436 m2/s6, respectively; and the average lateral errors are reduced by 0.55 cm and 4.97 cm, respectively, which significantly improves the path smoothness and facilitates tracking control. To avoid dynamic obstacles while traversing the operation route, the acceleration is varied in the range of -0.21 to 0.09 m/s2. In the orchard environment, using a search range of 40 m × 5 m and a resolution of 0.1 m, the proposed method has an average computation time of 9.6 ms. This is a significant improvement over the open space planning algorithm and reduces the average time by 12.4 ms compared to that of the dynamic lattice method, which is the same as that of the structured environment planning algorithm.DiscussionThe results show that the proposed method achieves a 129% improvement in algorithmic efficiency when applied to solve the path planning problem of mower operations in an orchard environment and confirm the clear advantages of the proposed method.

Soluble solids content (SSC) measurements are crucial for managing longan production and post-harvest handling. However, most traditional SSC detection methods are destructive, cumbersome, and unsuitable for field applications. This study proposes a novel field detection model (Brix-back propagation neural network, Brix-BPNN), designed for longan SSC grading based on an improved BP neural network. Initially, nine preprocessing methods were combined with six classification algorithms to develop the longan SSC grading prediction model. Among these, the model preprocessed with Savitzky–Golay smoothing and the first derivative (SG-D1) demonstrated a 7.02% improvement in accuracy compared to the original spectral model. Subsequently, the BP network structure was refined, and the competitive adaptive reweighted sampling (CARS) algorithm was employed for feature wavelength extraction. The results show that the improved Brix-BPNN model, integrated with the CARS, achieves the highest prediction performance, with a 2.84% increase in classification accuracy relative to the original BPNN model. Additionally, the number of wavelengths is reduced by 92% compared to the full spectrum, making this model both lightweight and efficient for rapid field detection. Furthermore, a portable detection device based on visible-near-infrared (Vis-NIR) spectroscopy was developed for longan SSC grading, achieving a prediction accuracy of 83.33% and enabling fast, nondestructive testing in field conditions.

In plant horticulture, furrow fertilizing is a common method to promote plant nutrient absorption and to effectively avoid fertilizer waste. Considering the high resistance caused by soil compaction in southern orchards, an energy-saving ditching device was proposed. A standard ditching blade with self-excited vibration device was designed, and operated in sandy clay with a tillage depth of 30cm. To conduct self-excited vibration ditching experiments, a simulation model of the interaction between soil and the ditching mechanism was established by coupling the ADAMS and EDEM software. To begin with, the ditching device model was first set up, taking into account its motion and morphological characteristics. Then, the MBD-DEM coupling method was employed to investigate the interaction mechanism and the effect of ditching between the soil particles and the ditching blade. Afterwards, the time-domain and frequency-domain characteristics of vibration signals during the ditching process were analyzed using the fast fourier transform (FFT) method, and the energy distribution characteristics were extracted using power spectral density (PSD). The experimental results revealed that the vibrations ditching device has reciprocating displacement in the Dx direction and torsional displacements in the θy and θz directions during operation, verifying the correctness of the coupling simulation and the effectiveness of vibrations ditching resistance reduction. Also, a load vibrations ditching bench test was conducted, and the results demonstrated that the self-excited vibrations ditching device, compared with common ditching device, achieved a reduction in ditching resistance of up to 12.3%. The reasonable parameters of spring stiffness, spring damping, and spring quality in self-excited vibrations ditching device can achieve a satisfied ditching performance with relatively low torque consumption at an appropriate speed.

A vibration ditching machine is a machine that can effectively reduce ditching resistance and energy consumption. In this paper, taking a self-developed, self-excited vibration ditching machine as the research object, we explore its internal dynamic vibration characteristics upon excitement when ditching, which reduces its resistance and energy consumption. The vibration characteristics of a ditching machine with three degrees of freedom (Y, Ry, and Rx directions), which are generated by the vibration of the self-excited ditching machine, are evaluated; the rotating speed, spring stiffness, spring damping coefficient, and blade weight are taken as factors, and their effects on the vibration characteristics are analyzed by an Adams–Edem coupling simulation model and a theoretical dynamics model of the self-excited ditching machine. Finally, a comparative analysis of the ditching machine of self-excited and nonself-excited ditching machines is conducted. The results of the analysis show that the rotating speed, spring stiffness, spring damping coefficient, and blade weight are important factors affecting the vibration characteristics. The theoretical dynamics model and the Adams–Edem coupling simulation model can represent the internal vibration mechanism of the self-excited ditching machine during ditching. The self-excited vibrating ditching machine is helpful in reducing the energy consumption of ditching.

In complex orchard environments, orchard mowing robots are prone to longitudinal slippage because of the characteristics of tires and the adhesion conditions of the road surface, which makes it difficult for the robots to maintain high-precision path tracking and autonomous navigation positioning. This not only affects the accuracy of path tracking but also leads to unstable motion for the mowing robots. To solve the above problems, we take an orchard mowing robot as the control object and establish a cascaded path-tracking controller and an adaptive time domain model based on a kinematics model. By designing a linear error model, an objective function, and constraint conditions for the mowing robot, the optimal linear velocity and angular velocity of the mower are obtained and converted into the speed of the driving wheel. Then, an anti-slip driving controller is designed based on fuzzy control of the slip rate. The slip-rate-based fuzzy controller is constructed according to the real-time speed of the mower and the reference speed of the driving wheel solved by the model predictive controller, and anti-slip driving control is implemented through a combination of a PID controller and a tire dynamics model. To verify the effectiveness of the proposed method, simulation and field experiments are conducted. The experimental results show that the slip rate of the driving wheel of the mower remains within the target slip rate range in the orchard working environment, avoiding excessive driving wheel sliding. Furthermore, the average lateral error of the path-tracking controller is controlled within 0.05 m, and the average value of the longitudinal error is kept within 0.04 m, which satisfies the control accuracy requirements of lawn mower operations. The proposed method provides a reference optimization scheme for improving the path-tracking and motion stability of a mowing robot.

In orchard scenes, the complex terrain environment will affect the operational safety of mowing robots. For this reason, this paper proposes an improved local path planning algorithm for an artificial potential field, which introduces the scope of an elliptic repulsion potential field as the boundary potential field. The potential field function adopts an improved variable polynomial and adds a distance factor, which effectively solves the problems of unreachable targets and local minima. In addition, the scope of the repulsion potential field is changed to an ellipse, and a fruit tree boundary potential field is added, which effectively reduces the environmental potential field complexity, enables the robot to avoid obstacles in advance without crossing the fruit tree boundary, and improves the safety of the robot when working independently. The path length planned by the improved algorithm is 6.78% shorter than that of the traditional artificial potential method, The experimental results show that the path planned using the improved algorithm is shorter, smoother and has good obstacle avoidance ability.

Litchi leaf diseases and pests can lead to issues such as a decreased Litchi yield, reduced fruit quality, and decreased farmer income. In this study, we aimed to explore a real-time and accurate method for identifying Litchi leaf diseases and pests. We selected three different orchards for field investigation and identified five common Litchi leaf diseases and pests (Litchi leaf mite, Litchi sooty mold, Litchi anthracnose, Mayetiola sp., and Litchi algal spot) as our research objects. Finally, we proposed an improved fully convolutional one-stage object detection (FCOS) network for Litchi leaf disease and pest detection, called FCOS for Litch (FCOS-FL). The proposed method employs G-GhostNet-3.2 as the backbone network to achieve a model that is lightweight. The central moment pooling attention (CMPA) mechanism is introduced to enhance the features of Litchi leaf diseases and pests. In addition, the center sampling and center loss of the model are improved by utilizing the width and height information of the real target, which effectively improves the model’s generalization performance. We propose an improved localization loss function to enhance the localization accuracy of the model in object detection. According to the characteristics of Litchi small target diseases and pests, the network structure was redesigned to improve the detection effect of small targets. FCOS-FL has a detection accuracy of 91.3% (intersection over union (IoU) = 0.5) in the images of five types of Litchi leaf diseases and pests, a detection rate of 62.0/ms, and a model parameter size of 17.65 M. Among them, the detection accuracy of Mayetiola sp. and Litchi algal spot, which are difficult to detect, reached 93.2% and 92%, respectively. The FCOS-FL model can rapidly and accurately detect five common diseases and pests in Litchi leaf. The research outcome is suitable for deployment on embedded devices with limited resources such as mobile terminals, and can contribute to achieving real-time and precise identification of Litchi leaf diseases and pests, providing technical support for Litchi leaf diseases’ and pests’ prevention and control.

With the development of Internet of Things (IoT) technology, modern agriculture is moving in the direction of 4.0. The Agricultural IoT is inseparable from wireless communication. However, in traditional agricultural IoT router and gateway site selection, the influence of the actual terrain on transmission loss is not considered, which results in node power wastage and increased maintenance costs. Based on a multi-sensor fusion algorithm, a fast terrain sampler is designed in this study to collect point-cloud data of the experimental site terrain. A reasonable objective function is then designed under the premise of consideration of the electromagnetic wave free-space and diffraction losses, and the locations of the routers and gateway are optimized based on k-means and particle swarm optimization (PSO) algorithm. Simulations show that the running time of the PSO algorithm is very sensitive to the changes in the execution parameters, and the improved PSO algorithm converges faster than the genetic algorithm (GA) in all three initialization methods. After collecting field terrain data, five interpolation methods were compared, and the nearest-neighbor algorithm is used to obtain the terrain model. On-site collection of received signal-strength indication (RSSI) shows that the communication quality of the optimal point selected via this algorithm is significantly higher than those of nearby points. At the same time, it is proved that the RSSI data has serious discontinuities, so the traditional gradient descent method is not suitable for solving the objective function in this work. Therefore, the algorithm used herein is of great significance for the site selection of agricultural Internet of Things nodes. However, since there are many ways to calculate the diffraction loss, the objective function of this work still needs more correction studies. The tool proposed in this work can obtain a 3D model of farmland faster than traditional surveying and mapping methods. With future research, the 3D model may be applied to soil moisture analysis, rainfall and moisture direction inversion, and plant light exposure prediction.

As a mainstream spraying technology, air-assisted spraying can increase the penetration and droplet deposition in the tree canopy; however, there seems to be less research on the maximum deposition volume of leaves. In this paper, the maximum deposition volume of a single leaf and the attenuation characteristics of droplets in the canopy were studied. By coupling them, the prediction equation of the total canopy droplet retention volume was obtained. The single-leaf test results showed that too small a surface tension reduced the total volume of droplet deposition on the leaf. In this paper, when the Weber number was equal to 144.3, the deposition form changed from particles to a water film, yielding the best deposition effect. The canopy droplet penetration test results show that the air velocity at the outlet increased first and then decreased, and the best effect was achieved when the air velocity at the outlet was 10 m/s. At the same time, when the surface tension of pesticides was 50 mN/m, the effect of canopy droplet deposition was better, which was consistent with the results of the single-leaf test. An average relative error of prediction equation of the total canopy droplet retention volume with 15.6% was established.

Detecting litchis in a complex natural environment is important for yield estimation and provides reliable support to litchi-picking robots. This paper proposes an improved litchi detection model named YOLOv5-litchi for litchi detection in complex natural environments. First, we add a convolutional block attention module to each C3 module in the backbone of the network to enhance the ability of the network to extract important feature information. Second, we add a small-object detection layer to enable the model to locate smaller targets and enhance the detection performance of small targets. Third, the Mosaic-9 data augmentation in the network increases the diversity of datasets. Then, we accelerate the regression convergence process of the prediction box by replacing the target detection regression loss function with CIoU. Finally, we add weighted-boxes fusion to bring the prediction boxes closer to the target and reduce the missed detection. An experiment is carried out to verify the effectiveness of the improvement. The results of the study show that the mAP and recall of the YOLOv5-litchi model were improved by 12.9% and 15%, respectively, in comparison with those of the unimproved YOLOv5 network. The inference speed of the YOLOv5-litchi model to detect each picture is 25 ms, which is much better than that of Faster-RCNN and YOLOv4. Compared with the unimproved YOLOv5 network, the mAP of the YOLOv5-litchi model increased by 17.4% in the large visual scenes. The performance of the YOLOv5-litchi model for litchi detection is the best in five models. Therefore, YOLOv5-litchi achieved a good balance between speed, model size, and accuracy, which can meet the needs of litchi detection in agriculture and provides technical support for the yield estimation and litchi-picking robots.

It is necessary to develop automatic picking technology to improve the efficiency of litchi picking, and the accurate segmentation of litchi branches is the key that allows robots to complete the picking task. To solve the problem of inaccurate segmentation of litchi branches under natural conditions, this paper proposes a segmentation method for litchi branches based on the improved DeepLabv3+, which replaced the backbone network of DeepLabv3+ and used the Dilated Residual Networks as the backbone network to enhance the model’s feature extraction capability. During the training process, a combination of Cross-Entropy loss and the dice coefficient loss was used as the loss function to cause the model to pay more attention to the litchi branch area, which could alleviate the negative impact of the imbalance between the litchi branches and the background. In addition, the Coordinate Attention module is added to the atrous spatial pyramid pooling, and the channel and location information of the multi-scale semantic features acquired by the network are simultaneously considered. The experimental results show that the model’s mean intersection over union and mean pixel accuracy are 90.28% and 94.95%, respectively, and the frames per second (FPS) is 19.83. Compared with the classical DeepLabv3+ network, the model’s mean intersection over union and mean pixel accuracy are improved by 13.57% and 15.78%, respectively. This method can accurately segment litchi branches, which provides powerful technical support to help litchi-picking robots find branches.

The biomechanical properties of plant stalk play an important role in the increase of crop production, the development of post-harvest mechanical equipment and the comprehensive utilization of biomass resources. Under the action of external forces in different forms and different loading directions, there are differences in the internal biomechanical properties of the stalk. The cutting force of some plant stalks has been reported in the literature, which is helpful to understand the mechanical properties of the stalks of field crop varieties, but the report on the biomechanical properties of banana bunch stalk is still blank. In the current research, we have established discrete element mechanical models of banana bunch stalk by the combination of discrete element analysis and physical experiment testing. The shear, tension, compression and bending mechanical properties of banana bunch stalk of different varieties (Brazilian banana and plantain banana), wet basis (WB) moisture contents of 93% and 84%, and internode positions (from 1 to 6) were studied. According to the experimental results, the established discrete element mechanical models were optimized. Our research provides new insights on how to use the discrete element method to analyze and research the biomechanical properties of banana bunch stalk, and visualize the micromechanics of banana bunch stalk and its nonlinear damage behavior. In addition, our results clearly show that the mechanical strength and elastic modulus of banana bunch stalk increase with the increase of the internode position on the stalk, and decrease with the decrease of the stalk moisture. For all the biomechanical properties of banana bunch stalk, the two factors of internode position and moisture content have a significant effect (P < 0.05), while the effect of the factor of the variety is not significant (P > 0.05). These results provide useful information about the evolution of banana bunch stalk biomechanical properties and the mechanism of deformation and failure, and provide a basis for the increase in banana production, the design and development of postharvest machinery, and the comprehensive utilization of agricultural residual biomass resources.

In the operations of the banana postharvesting process, the design and development related to the dehanding machine, the cutting and crushing machine of bunch stalks, and the fiber extraction machine of bunch stalks are in the initial stages. In addition, with the development of society and urbanization, the aging populations in hilly and mountainous areas, where bananas are planted, are becoming a more and more serious problem. The basic physical characteristic parameters of banana bunches, banana hands, and bunch stalks are the basis for studying their biomechanical properties and designing and developing the corresponding mechanical equipment. We measured the diameter, thickness of rind, curvature, density, moisture content, diameter of vascular bundle, weight of bunch stalk, and axial distance and circumferential angle of Brazilian and plantain banana hands using experiments and statistical analysis. Through the combination of physical experiments and numerical statistics, we obtained the value range and changing law of the physical characteristic parameters of banana bunches.