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Diameter and height are crucial morphological parameters of banana pseudo-stems, serving as indicators of the plant’s growth status. Currently, in densely cultivated banana plantations, there is a lack of applicable research methods for the scalable measurement of phenotypic parameters such as diameter and height of banana pseudo-stems. This paper introduces a handheld mobile LiDAR and Inertial Measurement Unit (IMU)-fused laser scanning system designed for measuring phenotypic parameters of banana pseudo-stems within banana orchards. To address the challenges posed by dense canopy cover in banana orchards, a distance-weighted feature extraction method is proposed. This method, coupled with Lidar-IMU integration, constructs a three-dimensional point cloud map of the banana plantation area. To overcome difficulties in segmenting individual banana plants in complex environments, a combined segmentation approach is proposed, involving Euclidean clustering, Kmeans clustering, and threshold segmentation. A sliding window recognition method is presented to determine the connection points between pseudo-stems and leaves, mitigating issues caused by crown closure and heavy leaf overlap. Experimental results in banana orchards demonstrate that, compared with manual measurements, the mean absolute errors and relative errors for banana pseudo-stem diameter and height are 0.2127 cm (4.06%) and 3.52 cm (1.91%), respectively. These findings indicate that the proposed method is suitable for scalable measurements of banana pseudo-stem diameter and height in complex, obscured environments, providing a rapid and accurate inter-orchard measurement approach for banana plantation managers.

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.

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.

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.

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.