In the handling or processing process, fruits are easily crushed by external loads. This type of damage in fruit often leads to the internal pulp browning and rotting, with the severity largely dependent on the fruit tissue’s geometric and mechanical properties. In kiwifruits, with their thin skin and dark-colored flesh, it is particularly challenging to observe and analyze the damage caused by extrusion through traditional experimental methods. The objective of this research is to construct a multi-scale finite element model encompassing the skin, flesh, and core by measuring the geometric and mechanical properties of kiwifruit, to assess and predict the damage characteristics under compression, and to verify the accuracy of the finite element model through experiments. The results indicated that kiwifruits demonstrated different compressive strengths in different directions during compression. The compressive strength in the axial direction was higher than that in the radial direction, and there was little difference between the long and short radial directions. The flesh tissue is the most vulnerable to mechanical damage under external compression, followed by the core. At strain levels below 5%, there was no noticeable damage in the axial or radial directions of the kiwifruit. However, when strain exceeded 5%, damage began to manifest in some of the flesh tissue. To maintain fruit quality during storage and transportation, the stacking height should not exceed 77 fruits in the axial direction, 48 in the long direction, and 53 in the short direction. The finite element analysis showed that the established model can effectively simulate and predict the internal damage behavior of kiwifruits under compression loads, which is helpful for a deeper understanding of the mechanical properties of fruits and provides a theoretical basis and technical guidance for minimizing mechanical damage during fruit handling.

The non-destructive harvesting of fresh tomatoes with agricultural robots requires the robotic arm to approach the fruit with the correct posture to ensure successful harvesting. However, this process faces significant challenges due to the small size of fruit pedicels, cluttered environments, and varied poses of the tomatoes and pedicels. Accurately identifying, localizing, and estimating the 6D spatial pose of the cutting points is critical for efficient and non-destructive harvesting. To address these challenges, we propose a keypoint-based pose estimation model, TomatoPoseNet, tailored to meet the agronomic requirements of tomato harvesting. The model integrates an efficient fusion block (EFBlock) based on the CSPLayer, referred to as the CSEFLayer, as the backbone network, designed to fuse multiscale features while maintaining efficient computational resource usage. Next, a parallel deep fusion network (PDFN) is utilized as the neck network to integrate features from multiple parallel branches. Furthermore, simple coordinate classification (SimCC) is employed as the head network for keypoint detection, and a StripPooling block is introduced to enhance the model’s ability to capture features of different scales and shapes by applying strip pooling in horizontal and vertical directions. Finally, a geometric model is constructed based on the information about the predicted 3D keypoints to estimate the 6D pose of the cutting points. The results show the following: (1) The average precision for keypoint detection (PCK@0.05) reached 82.51%, surpassing those of ViTPose, HRNet, Lite-HRNet, Hourglass, and RTMPose by 3.78%, 9.46%, 11%, 9.14%, and 10.07%, respectively. (2) The mean absolute errors (MAEs) of the yaw and pitch angles for 6D pose estimation of the cutting points were 2.98° and 3.54°, respectively, with maximum errors within 6.5°, meeting the requirements for harvesting. The experimental results demonstrate that the proposed method can accurately locate the 6D pose of cutting points in an unstructured tomato harvesting environment, enabling non-destructive harvesting.

Falling damage is the most common form of damage sustained by kiwifruit during the process of picking and post-processing, and it is difficult to conduct a quantitative analysis of this phenomenon through traditional experimental methods. In order to deeply understand the sensitivity of kiwifruit to falling collision damage, the finite element numerical simulation method was used to evaluate and predict the sensitivity of kiwifruit to falling collision damage during harvesting. First, we obtained the appearance characteristics of kiwifruit through reverse engineering technology and determined the geometric and mechanical property parameters of kiwifruit through physical mechanics experiments. Then, according to the characteristics of fruit tissue structure, a multiscale finite element model, including the skin, pulp, and core, was constructed to simulate the effects of different falling heights, collision angles, and contact surface materials on fruit damage, and the accuracy of the model was verified through falling experiments. Finally, based on the simulation results, the Box–Behnken design was employed within the response surface methodology to establish a sensitivity prediction model for the drop damage sensitivity of kiwifruit across different contact materials. The results showed that the maximum relative error between the speed change obtained using finite element simulation and the speed obtained by the high-speed camera was 5.19%. The model showed high rationality in energy distribution, with the maximum value of hourglass energy not exceeding 0.08% of the internal energy. On the contact surface material with a large elastic modulus, a higher falling height and larger collision angle will significantly increase the risk of fruit bruise. When the contact surface material was a steel plate, the falling height was 1 m, and the collision angle was 90°; the maximum bruise sensitivity of kiwifruit reached 6716.07 mm³ J⁻¹. However, when the contact surface material was neoprene, the falling height was 0.25 m, and the collision angle was 0°, the damage sensitivity was the lowest, at 1570.59 mm³ J⁻¹. The multiscale finite element model of kiwifruit falling collision constructed in this study can accurately predict the damage of kiwifruit during falling collision and provide an effective tool for the quantitative analysis of kiwifruit falling collision damage. At the same time, this study can also provide guidance for the design and optimization of the loss reduction method of the harvesting mechanism, which has important theoretical significance and practical value.

Navigating unstructured roads in vineyards with weak satellite signals presents significant challenges for robotic systems. This research introduces DoubleNet, an innovative deep-learning model designed to generate navigation lines for such conditions. To improve the model’s ability to extract image features, DoubleNet incorporates several key innovations, such as a unique multi-head self-attention mechanism (Fused-MHSA), a modified activation function (SA-GELU), and a specialized operation block (DNBLK). Based on them, DoubleNet is structured as an encoder–decoder network that includes two parallel subnetworks: one dedicated to processing 2D feature maps and the other focused on 1D tensors. These subnetworks interact through two feature fusion networks, which operate in both the encoder and decoder stages, facilitating a more integrated feature extraction process. Additionally, we utilized a specially annotated dataset comprising images fused with RGB and mask, with five navigation points marked to enhance the accuracy of point localization. As a result of these innovations, DoubleNet achieves a remarkable 95.75% percentage of correct key points (PCK) and operates at 71.16 FPS on our dataset, with a combined performance that outperformed several well-known key point detection algorithms. DoubleNet demonstrates strong potential as a competitive solution for generating effective navigation routes for robots operating in vineyards with unstructured roads.

In agricultural operation scenarios, the diversity of farmland terrain, crops and other forms, as well as uncertain factors such as weather changes and crop growth during agricultural operation, can have an impact on the construction of high-precision maps. To address these challenges and analyze operational scenarios based on the characteristics of agricultural scenarios, this paper proposes a point cloud map construction algorithm for plant point removal and locatability estimation. Based on the existing Simultaneous Localization and Mapping (SLAM) framework, plant point removal and locatability estimation are improved. Firstly, Red, Green, Blue (RGB) images and Near Infrared (NIR) images are fused to identify and remove plant point clouds, preserving effective inter frame matching information, reducing the impact of dynamic points on inter frame matching, and achieving high front-end motion estimation accuracy. Then, the localization estimation method based on learning is used to determine the motion estimation status and determine whether to execute the backend optimization algorithm. Finally, the back-end optimization algorithm based on Factor graph is designed, and the Factor graph, constraint relationship and optimization function are constructed to optimize the pose of all frames. The optimized map construction algorithm reduces the re projection errors between field roads, paths, and crop rows by 10.27%, 20.76%, and 14.36% compared to before optimization. To verify the actual operational effectiveness of the point cloud map construction algorithm, the hardware part of the multi-sensor information collection system was designed, and sensor internal and external parameter calibration were also carried out. A map information collection vehicle was built and field experiments were conducted. The results showed that the positioning error of the point cloud map construction method proposed in this paper is less than 0.5°, and the cumulative error of 30 m translation is less than 12 cm, which meets the actual operational requirements.