In the United States, the apple (Malus ×domestica) industry contributes ≈$2.75 billion to the economy (U.S. Department of Agriculture, 2020). Pruning is an important cultivation technique that impacts the fruit quality and usefulness of disease control practices (Glenn and Campostrini, 2011). Manual pruning of apple trees is one of the most labor-intensive operations, requiring ≈80 to 120 h⋅ha−1 of labor (Mika et al., 2016) and accounting for 20% of the total labor costs (Crassweller et al., 2020). To deal with decreasing labor availability and higher associated costs, alternate solutions are needed. A few studies have reported mechanical pruning or hedging of tree fruits (Krueger et al., 2013), but these operations are less useful for apple trees (He and Schupp, 2018; Zahid et al., 2021a) because they can result in unwanted vegetative growth.
Robotic pruning is a selective branch operation that can produce accurate cuts using an end-effector tool attached to the robotic arm (Lehnert, 2012). A few studies have reported the development of pruning robots for different crops such as apple (Zahid et al., 2020a, b), grape [Vitis vinifera (Botterill et al., 2017)], and cherry [Prunus avium (You et al., 2020)]. Robotic pruning of apple trees is a challenging task because the crowded and overlapped branches result in narrow spaces for maneuvering the robot inside the canopy (He and Schupp, 2018; Zahid et al., 2021b). Therefore, the design considerations for a pruning robot should include the maneuverability and spatial requirements during manipulation. The end-effector is an integral component of a robotic pruning system equipped with a cutting tool operated by an appropriate mechanism to perform the cutting action (Zahid et al., 2021a). Researchers have developed pruning end-effectors using different cutting mechanisms, such as a saw disc (Botterill et al., 2017) and shear blades (Zahid et al., 2020b). For robotic pruning, the shear blades were more successful than a saw disc and delivered smooth cuts (Zahid et al., 2020b) essential for tree pruning to avoid negatively affecting the healing process. A compact robotic cutter is essential for successful operation (Zahid et al., 2021b), which requires the selection of appropriate cutter system components. However, a prerequisite for component selection is to determine the force (torque) required to cut the branches.
The dynamic analysis of branch cutting is the first step in developing an effective robotic pruning system. In recent years, there have been dynamic analyses of robotic operations for various specialty crops, such as harvesting apples (Davidson et al., 2016), olives [Olea europaea (Ruiz et al., 2018)], tomatoes [Solanum lycopersicum (Li et al., 2019)], and button mushrooms [Agaricus bisporus (Huang et al., 2021)]. A few studies also reported torque requirements for branch cutting. Branch diameter is one of the most important factors affecting the cutting torque. Pezzi et al. (2009) evaluated the branch cutting force requirements for pruning grapevines and reported that the forces required for pruning vary with vine diameter, cultivar, and pruning time of year. Zahid et al. (2020a) estimated the torque required to prune ‘Fuji’ apple tree branches, but the tests were conducted in the laboratory; therefore, the results may not truly represent the force required to cut the branches in field conditions. The tests were conducted on ‘Fuji’ apple trees, but the cutting torque requirements for different cultivars may vary because of variations in specific wood densities.
The cutting angle of the end-effector is critical for robotic pruning operation. Ideally, the cutter should reach the target branches perpendicular to limb orientation (straight cut); however, the straight cut may not always be possible because of the narrow workspace. Criss-crossed branches limit the robot’s ability to attain a desired approach angle, which may require different approach angles of the cutter to target specific branches, possibly resulting in bevel (inclined) cuts (Zahid et al., 2021b). The effective cut size (surface area) of the inclined (bevel) cut is greater than the straight cut when cutting a branch at the same point; thus, the required cutting torque could be different and needs to be investigated.
The positioning of a branch on the blade before cutting (branch–blade contact point) is also crucial for determining the robot kinematics to accurately reach the target cut points. The robot kinematics are calculated based on three-dimensional (3D) coordinate frames. Selecting the origin of the cutter coordinate frame is a key factor that could affect the positioning of the robot. This is particularly important when the branches are pruned near the tree trunk: the cutter may collide with the trunk or fail to attain the desired cutting angle at a defined reference point such as at the cutter pivot or cutter center. Thus, the cutter may need to use a different reference point based on the canopy requirements and to reduce potential collisions with the tree trunk. However, the variations in the branch–blade contact points could also affect the torque required to cut the branches. Therefore, this should be investigated to understand the accurate robot kinematics during pruning.
Considering the knowledge gaps, the primary goal of this study was to determine the branch-cutting torque requirements for ‘Fuji’, ‘Gala’, ‘Honeycrisp’, and ‘Golden Delicious’ trees under different cutting settings to assist the development of an automated pruning system. The variations in the cutting settings could alter the cutting torque requirements, thus affecting the performance of a robotic pruning system. Thus, our study was performed with objectives: 1) to integrate force measurement and inertial measurement unit (IMU) sensors with a manual shear pruner to perform pruning dynamic tests; 2) to investigate pruning torque requirements for different apple cultivars; and 3) to determine the effects of branch placement on the cutter (contact point) and cutting angle on torque requirements.
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Crassweller, R., Peter, K., Krawczyk, G., Schupp, J., Ford, T., Brittingham, M., Johnson, J., LaBorde, L., Harper, J., Kephart, K., Pifer, R., Kelley, K., He, L., Heinemann, P., Biddinger, D., Lopez-Uribe, M., Marini, R., Baugher, T., Weber, D., Kime, L., Crow, E., Weaver, E. & Lehman, B. 2020 2020-21 Penn State tree fruit production guide Penn State Ext. University Park, PA
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Crassweller, R. Peter, K. Krawczyk, G. Schupp, J. Ford, T. Brittingham, M. Johnson, J. LaBorde, L. Harper, J. Kephart, K. Pifer, R. Kelley, K. He, L. Heinemann, P. Biddinger, D. Lopez-Uribe, M. Marini, R. Baugher, T. Weber, D. Kime, L. Crow, E. Weaver, E. Lehman, B. 2020 2020-21 Penn State tree fruit production guide Penn State Ext. University Park, PA
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