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Banana crowns' intricate vascular systems facilitate nutrient transport for fruit growth and provide mechanical support. Analyzing vascular bundle morphology facilitates understanding of its influence on the banana de-handing process. In this study, we employed X-ray Computed Tomography (CT) scanning and microscopic observation of paraffin sections to characterize the morphological traits of the banana crown's vascular tissue system and reconstructed its 3D vascular tissue system throughout the banana bunch. Based on the internal tissue characteristics and external morphology, the banana crown is categorized into three regions: the central stalk–crown transition region (CSCTR), the crown expansion region (CER), and the crown–finger transition region (CFTR). Cutting experiments indicated that variations in the cutting strength and specific cutting energy across positions within the banana bunch were insignificant but significantly distinct among the three regions. Specifically, the CER showed a 19.7% reduction in cutting strength and a 15.5% decrease in energy consumption compared to the other regions. This was due to the unique cross-distribution of fibers within the CER, which were primarily parallel to the cutting blade, significantly reducing cutting forces and energy consumption, making the CER the optimal region for cutting. The orientation of vascular bundles relative to the blade is key to optimizing plant cutting mechanics. [ABSTRACT FROM AUTHOR]

In addressing the challenges of high labor intensity, cost, and potential mechanical damage to banana fruit in orchards, this study presents the design of a banana bunch transport device featuring a lifting mechanism and an automatic fruit shaft bottom-fixing system. The device is tailored to the planting and morphological characteristics of banana bunches, aiming for efficient, low-loss, and labor-saving mechanized transport. Key design considerations included the anti-overturning mechanism and the lifting system based on transportation conditions and the physical dimensions of banana bunches. A dynamic simulation was conducted to analyze the angular velocity and acceleration during the initial conveying stages, forming the basis for the fruit shaft bottom-fixation mechanism. A novel horizontal multi-point scanning method was developed to accurately identify and secure the fruit shaft bottom, complemented by an automated control system. Experimental results showed a 95.83% success rate in identification and fixation, validated by field trials that confirmed the necessity and stability of the fixation mechanism. To enhance the durability of the fruit shaft bottom-fixation mechanism, a multi-factor test was conducted, optimizing the device's maximum travel speed and minimizing the banana bunch's oscillation angle. Field tests showed an oscillation angle of 8.961°, closely matching the simulated result of 9.526°, demonstrating the reliability of the response surface analysis model. This study offers a practical and efficient solution for banana bunch transport in orchards, showcasing significant practical value and potential for wider adoption. [ABSTRACT FROM AUTHOR]

Adaptability to unstructured objects and the avoidance of target damage are critical challenges for flexible grippers in fruit-picking robots. Most existing flexible grippers have many problems in terms of control complexity, stability and cost. This paper proposes a flexible finger design method that considers contact behavior. The new approach incorporates topological design of contact targets and introduces contact stress constraints to directly obtain a flexible finger structure with low contact stress and good adaptability. The study explores the effects of design parameters, including virtual spring stiffness, volume fraction, design domain size, and discretization, on the outcomes of the flexible finger topology optimization. Two flexible finger structures were selected for comparative analysis. The experimental results verified the effectiveness of the design method and the maximum contact stress was reduced by about 70%. An adaptive two-finger gripper was developed. This design allows the gripper to achieve damage-free grasping without additional sensors and control systems. The adaptive and contact performances of the grippers with different driving modes were analyzed. Practical grasping tests were also performed, including evaluation of adaptive performance, stability, and maximum grasping weight. The results indicate that gripper 2 with flexible finger 2 excelled in contact stress and adaptive wrapping, making it well-suited for grasping unstructured and fragile objects. This paper provides valuable insights for the design and application of flexible grippers for picking robots, offering a promising solution to enhance adaptability while minimizing target damage. [ABSTRACT FROM AUTHOR]

Precise detection and localization are prerequisites for intelligent harvesting, while fruit size and weight estimation are key to intelligent orchard management. In commercial banana orchards, it is necessary to manage the growth and weight of banana bunches so that they can be harvested in time and prepared for transportation according to their different maturity levels. In this study, in order to reduce management costs and labor dependence, and obtain non-destructive weight estimation, we propose a method for localizing and estimating banana bunches using RGB-D images. First, the color image is detected through the YOLO-Banana neural network to obtain two-dimensional information about the banana bunches and stalks. Then, the three-dimensional coordinates of the central point of the banana stalk are calculated according to the depth information, and the banana bunch size is obtained based on the depth information of the central point. Finally, the effective pixel ratio of the banana bunch is presented, and the banana bunch weight estimation model is statistically analyzed. Thus, the weight estimation of the banana bunch is obtained through the bunch size and the effective pixel ratio. The R2 value between the estimated weight and the actual measured value is 0.8947, the RMSE is 1.4102 kg, and the average localization error of the central point of the banana stalk is 22.875 mm. The results show that the proposed method can provide bunch size and weight estimation for the intelligent management of banana orchards, along with localization information for banana-harvesting robots. [ABSTRACT FROM AUTHOR]

BACKGROUND: The effective deposition of pesticide droplets on a target leaf surface is critical for decreasing pesticide application rates. The wettability between the target leaf surface and the pesticide spray liquid should be investigated in depth, with the aim of enhancing the adhesion of pesticide solutions. The wetting and deposition behavior of pesticides on target leaves depends on the properties of the liquid and the physical and chemical properties of the leaves. The physical and chemical properties of leaves vary with growth stage. This study aims to investigate the wetting behavior of banana leaf surfaces at different stages. RESULTS: The microstructures and chemical compositions of banana leaf surfaces at different stages were studied using modern methods. The surface structure of banana leaves exhibited a wide variety of characteristics at different growth stages, and the chemical composition changed marginally. The surface free energy (SFE) and polar and non‐polar components of banana leaves at different growth stages were measured by examining the contact angles (CA) of different test solutions on the surface of banana leaves. Previous research has suggested that changes in the CA and SFE correlate with changes in leaf surface wettability. In general, the new upper leaves of banana trees are composed of polar components and exhibit hydrophobicity. Non‐polar components become dominant as the leaf grows. The back surface of banana leaves was non‐polar at all growth stages, with a trend that was opposite to that of the front surface. The critical surface tension of the banana leaf surface at different growth stages ranged from 7.83 to 24.22 mN m−1, thus falling into the category of a low‐energy surface. CONCLUSION: The surface roughness and chemical characteristics of banana leaves affected the wettability of the leaf surface. Differences in the free energy and the polar and non‐polar components of the leaf surface at were seen at different growth stages. This study provides a favorable reference for the rational control of pesticide spraying parameters and the enhancement of wetting and adhesion of the solution on banana leaf surfaces. © 2023 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Potted plant canopy extraction requires a fast, accurate, stable, and affordable detection system for precise pesticide application. In this study, we propose a new method for extracting three-dimensional canopy information of potted plants using millimeter-wave radar and evaluate the system on plants in static, rotating, and rotating-while-spraying states. The position and rotation speed of the rotating platform are used to compute the rotation–translation matrix between point clouds, enabling the multi-view point clouds to be overlaid on the world coordinate system. Point cloud extraction is performed by applying the Density-Based Spatial Clustering of Applications with Noise algorithm (DBSCAN), while an Alpha-shape algorithm is used for three-dimensional reconstruction of the canopy. Our measurement results for the 3D reconstruction of plants at different growth stages showed that the reconstruction model has higher accuracy under the rotation condition than that under the static condition, with average relative errors of 41.61% and 10.21%, respectively. The significant correlation between the sampling data with and without spray reached 0.03, indicating that the effect of the droplets on radar detection during the spray process can be neglected. This study provides guidance for plant canopy detection using millimeter-wave radar for advanced agricultural informatization and automation. [ABSTRACT FROM AUTHOR]

As the banana industry develops, the demand for intelligent banana crown cutting is increasing. To achieve efficient crown cutting of bananas, accurate segmentation of the banana crown is crucial for the operation of a banana crown cutting device. In order to address the existing challenges, this paper proposed a method for segmentation of banana crown based on improved DeepLabv3+. This method replaces the backbone network of the classical DeepLabv3+ model with MobilenetV2, reducing the number of parameters and training time, thereby achieving model lightweightness and enhancing model speed. Additionally, the Atrous Spatial Pyramid Pooling (ASPP) module is enhanced by incorporating the Shuffle Attention Mechanism and replacing the activation function with Meta-ACONC. This enhancement results in the creation of a new feature extraction module, called Banana-ASPP, which effectively handles high-level features. Furthermore, Multi-scale Channel Attention Module (MS-CAM) is introduced to the Decoder to improve the integration of features from multiple semantics and scales. According to experimental data, the proposed method has a Mean Intersection over Union (MIoU) of 85.75%, a Mean Pixel Accuracy (MPA) of 91.41%, parameters of 5.881 M and model speed of 61.05 f/s. Compared to the classical DeepLabv3+ network, the proposed model exhibits an improvement of 1.94% in MIoU and 1.21% in MPA, while reducing the number of parameters by 89.25% and increasing the model speed by 47.07 f/s. The proposed method enhanced banana crown segmentation accuracy while maintaining model lightweightness and speed. It also provided robust technical support for relevant parameters calculation of banana crown and control of banana crown cutting equipment. [ABSTRACT FROM AUTHOR]

Detecting banana pseudostems is an indispensable part of the intelligent management of banana cultivation, which can be used in settings such as counting banana pseudostems and smart fertilization. In complex environments, dense and occlusion banana pseudostems pose a significant challenge for detection. This paper proposes an improved YOLOV7 deep learning object detection algorithm, YOLOV7-FM, for detecting banana pseudostems with different growth conditions. In the loss optimization part of the YOLOV7 model, Focal loss is introduced, to optimize the problematic training for banana pseudostems that are dense and sheltered, so as to improve the recognition rate of challenging samples. In the data augmentation part of the YOLOV7 model, the Mixup data augmentation is used, to improve the model's generalization ability for banana pseudostems with similar features to complex environments. This paper compares the AP (average precision) and inference speed of the YOLOV7-FM algorithm with YOLOX, YOLOV5, YOLOV3, and Faster R-CNN algorithms. The results show that the AP and inference speed of the YOLOV7-FM algorithm is higher than those models that are compared, with an average inference time of 8.0 ms per image containing banana pseudostems and AP of 81.45%. This improved YOLOV7-FM model can achieve fast and accurate detection of banana pseudostems. [ABSTRACT FROM AUTHOR]

The aerial atomizer is the most essential component of the plant protection UAV (unmanned aerial vehicle). However, the structural optimization of existing aerial atomizers lacks comprehensive consideration of spray parameters and structural parameters, and there is a shortage of available atomizer spray models, resulting in the unstable effect of UAV application. In our previous work, an aerial dual-atomization centrifugal atomizer was developed. In order to obtain an aerial atomizer with good atomization effect and its atomization model, structural optimization at different rotation speeds and flow rates of the atomizer, and its atomization performance, are studied in this paper. Firstly, with the droplet volume median diameter (VMD) and spectral width (SRW) as the evaluation index, through the single-factor, Plackett–Burman and Box–Behnken tests, the influence of rotation speed, flow rate, tooth number and tooth shape were studied. The regression models of the droplet VMD and SRW were established using multiple quadratic regression fitting of the test data. Secondly, in order to achieve the lowest droplet VMD and SRW, the response surface method and post-hoc multiple comparison method were used to obtain the optimized structure of the atomizer's rotation ring at different rotation speeds (600–7000 r/min) and flow rates (500–1000 mL/min). Lastly, with the effective swath width (ESW) of the optimized atomizer as the evaluation index, through the Box–Behnken test, the influence of rotation speed, flow rate and spray height were studied. The multiple quadratic regression model of ESW was established with the test data. The test results indicated that rotation speed, flow rate and tooth number had a significant effect on droplet VMD and SRW; tooth shape had no significant effect on the droplet VMD and SRW, however, the square tooth shape had the best atomization effect; and rotation speed, flow rate and spray height had a significant effect on ESW. The optimized structural parameters were tooth shape: square, and tooth number: 20. The determination coefficient R 2 of the regression model of VMD, SRW and ESW were 0.9976, 0.9770 and 0.9974, respectively, which indicates that the model was accurate, and can evaluate and predict the spray effect. This paper provides an optimized dual-atomization centrifugal atomizer, and its regression models of VMD, SRW and ESW for UAV applications can provide a reference for efficient UAV spraying. [ABSTRACT FROM AUTHOR]

Many aspects of the agricultural industry such a field crop planting and harvesting and chemical application in fruit crops have been employing mechanization and automation solutions for decades. However, the de-handing operation in banana postharvest operations is usually performed manually. Mechanical or automated de-handing is a potential long-term solution to address labor shortages and the associated high costs. Bananas are mainly grown in developing countries located in tropical and subtropical regions, where the development of agricultural mechanization and automation solutions started only recently and is progressing relatively slowly. In addition, large-scale banana orchards are mainly distributed in hilly and mountainous areas, though there are also some small-scale banana plantations in plain areas. The complex environment of banana orchards and the aging farming population are other important factors that make it difficult to realize mechanized operation of banana de-handing. In recent years, researchers have proposed advanced techniques that may facilitate the development of mechanical de-handing systems. However, the successful adoption of mechanical de-handing technology still faces many challenges. This paper systematically reviews the existing research on de-handing technologies and component mechanisms. A comprehensive evaluation is carried out from the perspectives of feasibility of the mechanism design, stability of the model simulation and reliability of the prototype systems developed. The future challenges and opportunities for designing and practically adopting mechanical de-handing equipment are also summarized and discussed. [ABSTRACT FROM AUTHOR]

Deep learning is the subject of increasing research for fruit tree detection. Previously developed deep-learning-based models are either too large to perform real-time tasks or too small to extract good enough features. Moreover, there has been scarce research on the detection of pomelo trees. This paper proposes a pomelo tree-detection method that introduces the attention mechanism and a Ghost module into the lightweight model network, as well as a feature-fusion module to improve the feature-extraction ability and reduce computation. The proposed method was experimentally validated and showed better detection performance and fewer parameters than some state-of-the-art target-detection algorithms. The results indicate that our method is more suitable for pomelo tree detection. [ABSTRACT FROM AUTHOR]

Multi-target recognition and positioning using robots in orchards is a challenging task in modern precision agriculture owing to the presence of complex noise disturbance, including wind disturbance, changing illumination, and branch and leaf shading. To obtain the target information for a bud-cutting robotic operation, we employed a modified deep learning algorithm for the fast and precise recognition of banana fruits, inflorescence axes, and flower buds. Thus, the cutting point on the inflorescence axis was identified using an edge detection algorithm and geometric calculation. We proposed a modified YOLOv3 model based on clustering optimization and clarified the influence of front-lighting and backlighting on the model. Image segmentation and denoising were performed to obtain the edge images of the flower buds and inflorescence axes. The spatial geometry model was constructed on this basis. The center of symmetry and centroid were calculated for the edges of the flower buds. The equation for the position of the inflorescence axis was established, and the cutting point was determined. Experimental results showed that the modified YOLOv3 model based on clustering optimization showed excellent performance with good balance between speed and precision both under front-lighting and backlighting conditions. The total pixel positioning error between the calculated and manually determined optimal cutting point in the flower bud was 4 and 5 pixels under the front-lighting and backlighting conditions, respectively. The percentage of images that met the positioning requirements was 93 and 90%, respectively. The results indicate that the new method can satisfy the real-time operating requirements for the banana bud-cutting robot. [ABSTRACT FROM AUTHOR]

The selective laser melting technique is widely used in aerospace and biomedical industries, and the performance of formed 316L parts is significantly subject to the forming angle. As the selective laser melting 316L parts are constrained by multiple performance indexes, the study involves multivariate interaction influenced on the forming parameters such as the angle with the xz plane, the angle with the xy plane, laser power, scan speed, powder thickness, and hatching space on the indexes like tensile strength, density, and surface roughness with linear regression equations based on multiobjective optimization to obtain the best process parameters. The study results of microstructure performance of the formed 316L parts show that the angle with the xz plane has significant effect on the experiment indexes, while the layer thickness has the greatest effect on the indexes. After stretching, the molten pools are obviously elongated and the microstructure of the formed 316L parts is composed of equiaxed crystals and columnar crystals with a grain width of 0.28–0.4 nm. The secondary growth of the dendrites is not obvious, and the crystallinity of the selective laser melting 316L parts is not as good as the standard parts, with the microstructure showing directional solidification due to grain refinement and microscopic distortion of crystals. As the fracture has dimples, it is a ductile fracture and typical plastic fracture. The hardness near the fracture is higher than that of the substrate, whilst the indexes regarding the selective laser melting parts are higher than the ASTM-A182 and ASTM-F3184-16 standards. Since the theoretical model built in this study has less error, the findings have practical engineering application value. [ABSTRACT FROM AUTHOR]

Pseudo-stem diameter of banana is an important phenotype parameter reflecting the crop health and growth. Moreover, it indicates the lodging resistance as an important factor for peasants. For rapid measurement of pseudo-stems diameters, a 3D reconstruction algorithm is proposed. This algorithm consists of pseudo-stem reconstruction, point clouds data fitting, and parameter extraction of a depth image obtained by the red, green, blue-depth (RGB-D) camera by using a Particle Swarm Optimization algorithm. The experimental results show that the average measurement error of the pseudo-stem is 4.06 mm. The average relative measurement error is 2.34%. The mentioned results demonstrate the superior treatment effect of the proposed method. [ABSTRACT FROM AUTHOR]

Currently, banana harvest still relies on manual operation with high labor intensity. With an aging global population, it is important to develop a machine to replace the manual harvesting of bananas to increase sustainability. In the area of robotic fruit harvest, most of the existing studies have used one single manipulator to grip the fruit. However, unlike other fruits, the weight of a banana bunch (25–40 kg) would be too heavy for one single manipulator. To solve this problem, this paper proposes a flexible supporting device, which was introduced to cooperate with the manipulator to complete banana harvest. The supporting device was designed to hold the bottom and the weight of the banana bunch. It included two parts: the flexible contact part and the height difference self-adjusting part. The shape adaptability, size adaptability, and height difference adaptability of the proposed supporting device were studied in this paper. The process of supporting bananas was also simulated and analyzed. The stiffness and stress properties of the device during this process were studied. The results showed that the flexible supporting device had a good adaptive performance for supporting different shapes and sizes of objects. During the supporting process, the device worked stably and reliably and caused small stress on the banana skin. Finally, a prototype of the supporting device was used to further verify the performance of the device. This research can promote the mechanization and automation progress of the harvesting of such a complex crop as bananas. [ABSTRACT FROM AUTHOR]

The real-time detection of banana bunches and stalks in banana orchards is a key technology in the application of agricultural robots. The complex conditions of the orchard make accurate detection a difficult task, and the light weight of the deep learning network is an application trend. This study proposes and compares two improved YOLOv4 neural network detection models in a banana orchard. One is the YOLO-Banana detection model, which analyzes banana characteristics and network structure to prune the less important network layers; the other is the YOLO-Banana-l4 detection model, which, by adding a YOLO head layer to the pruned network structure, explores the impact of a four-scale prediction structure on the pruning network. The results show that YOLO-Banana and YOLO-Banana-l4 could reduce the network weight and shorten the detection time compared with YOLOv4. Furthermore, YOLO-Banana detection model has the best performance, with good detection accuracy for banana bunches and stalks in the natural environment. The average precision (AP) values of the YOLO-Banana detection model on banana bunches and stalks are 98.4% and 85.98%, and the mean average precision (mAP) of the detection model is 92.19%. The model weight is reduced from 244 to 137 MB, and the detection time is shortened from 44.96 to 35.33 ms. In short, the network is lightweight and has good real-time performance and application prospects in intelligent management and automatic harvesting in the banana orchard. [ABSTRACT FROM AUTHOR]

Janus wettability plays an important role in certain special occasions. In this study, field emission scanning electron microscopy (FESEM) was used to observe the surface microstructure of banana leaves, the static wettability of the banana leaf surface was tested, and the dynamic response of water droplets falling at different heights and hitting on the adaxial and abaxial sides was studied. The study found that the nanopillars on the adaxial and abaxial sides of the banana leaf were different in shape. The nanopillars on the adaxial side were cone-shaped with large gaps, showing hydrophilicity (Wenzel state), and the heads of the nanopillars on the abaxial side were smooth and spherical with small gaps, showing weak hydrophobicity (Cassie–Baxter state). Banana leaves show Janus wettability, and the banana leaf surface has high adhesion properties. During the dynamic impact test, the adaxial and abaxial sides of the banana leaves showed different dynamic responses, and the wettability of the adaxial side of the banana leaves was always stronger than the abaxial side. Based on the structural parameters of nanopillars on the surface of the banana leaf and the classical wetting theory model, an ideal geometric model around a single nanopillar on both sides of the banana leaf was established. The results show that the established model has high accuracy and can reflect the experimental results effectively. When the apparent contact angle was 76.17°, and the intrinsic contact angle was 81.17° on the adaxial side of the banana leaf, steady hydrophilicity was shown. The abaxial side was similar. The underlying mechanism of Janus wettability on the banana leaf surface was elucidated. This study provides an important reference for the preparation of Janus wettability bionic surfaces and the efficient and high-quality management of banana orchards. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

To obtain the appropriate mechanized cutting region for banana dehanding, the methods of X-ray Computed Tomography (CT), Paraffin-embedded tissue section, and scanning electron microscopy (SEM) were adopted to observe the morphological and anatomical characteristics of vascular bundles of the banana crown. The results indicated that the crown can be divided into three regions, viz., the central stalk–crown transition region (CSCTR), the crown expansion region (CER), and the crown–finger transition region (CFTR). According to the obtained characteristics, the cutting mechanical properties are found to be affected by the relative angle between the vascular bundle and cutter (RAVBC) and the vascular bundle density. In CSCTR, due to the opposite change of RAVBC and density, the cutting mechanical properties become unstable and the cutting energy decreases gradually from 4.30 J to 2.57 J. While in CER, the cutting mechanical properties tend to be stable, and the cutting energy varies in a small range (2.83–2.92 J), owing to the small changes of RAVBC and density. When the vascular bundles cross from the CER to CFTR, the cutting energy increases with the increase of RAVBC and density, which varies from 3.37 to 4.84 J. Accordingly, the appropriate cutting region for dehanding, which can reduce the energy consumption and improve the cutting efficiency, is ascertained to be between CSCTR and CER. [ABSTRACT FROM AUTHOR]

In all operations of banana post-harvesting, picking and transportation are the operations with the highest labor intensity and the highest production costs. The operation of banana de-handing is still in the traditional stage by manual cutting, which seriously impedes the development of the banana industry towards mechanization, automation and intelligence. This review summarizes and analyzes the mechanized technology research status and equipment application of the banana picking, transportation and de-handing operations in banana orchards around the world and proposes basic ideas and constructive suggestions for quickening the mechanized development of banana post-harvesting. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the banana de-handing device has poor radial deployment to the banana bunch stalk during the mechanized banana de-handing procedure, this paper presented a ring deployable mechanism based on a set of planar seven-bar linkages. It consists of multiple basic deployable units, which has great folding/deploying performance and is suitable for manufacturing a banana de-handing device, the diameter of which can be variably larger. The kinematics analysis of the mechanism was done, and the trajectory in space was obtained. When the mechanism is fully deployed, the diameter is 164 mm. The ratio of the folded height to the deployed diameter is 0.732, the ratio of the folded diameter to the deployed diameter is 0.262, and the ratio of the folded volume to the maximum of the deployed volume is 0.069. An experimental prototype of 500, 500, and 768 mm in length, width, and height was manufactured, and the deploying performance was analyzed to show the feasibility. Finally, experimental results show that 71.43% of the banana hands are de-handed successfully. The mechanism has a great deploying effect on banana bunch stalk, and the quality of the banana crown incision is good. [ABSTRACT FROM AUTHOR]