Pineapple harvesting is a key step in pineapple field production. At present, pineapple fruits are usually picked manually. With decreasing labor resources and increasing production costs, machines have been used instead of manual picking approaches in the modern pineapple industry. This paper briefly describes the basic situation of pineapple planting worldwide. Based on the degree of automation of mechanized pineapple harvesting equipment, the main structural forms, core technologies, and operation modes of semi-automatic, automatic, and intelligent pineapple harvesting equipment are summarized. The research status and existing problems of key pineapple fruit picking robots, such as fruit recognition, maturity classification, positioning, and separation of pineapple fruits, are analyzed. Considering the problems of pineapple harvesting equipment, such as difficulty entering the ground, low harvesting efficiency, low picking success rate, and fruit damage, innovative future research directions for mechanized pineapple harvesting technology are proposed, such as combining agricultural machinery and agronomical principles, integrating mechanized, automated, and intelligent technology, and developing modular designs and generalized approaches.

In view of the lack of research on the extrusion of pineapple caused by the overall stress response of pineapple at the present stage of pineapple automatic harvesting, the finite element model of pineapples can be studied by constructing such a model. At present, there is still a lack of research on the mechanical properties of the pineapple stem. In this research, the mechanical properties of a pineapple stem were determined by a three-point bending test, a compression test, and a theoretical calculation. Based on the cohesive zone model (CZM), the relevant parameters of the pineapple fruit–stem junction were determined by the fracture test. The overall finite element model of pineapple was established, and then the verification test of the finite element model was carried out. In the validation test, the correlation between sample size parameters and results was analyzed, and the validity of the test sample selection was demonstrated. By substituting the simulation results into the derivation formula, the maximum traction strength and maximum displacement error values were calculated to be 4.3% and 2.9%, respectively, which verified the accuracy of the cohesive zone model parameters. By comparing the maximum load force and load displacement of the load point in the test and simulation, the error of the load force at the fracture point relative to the average value was 3.6%, and the error of the load displacement relative to the average value was 1.4%. The numerical results showed that the model reflected the accuracy of the process of pineapple plant fracture. This study provides a reliable finite element model for future research on pineapple automatic harvesting.

To address the challenges of inadequate adaptability, insufficient power, high ground clearance, and limited functionality in existing pineapple field machinery, this study proposes a height-adjustable pineapple field management platform based on previously established cultivation patterns and agronomic requirements. The structural configuration and operational principles of the platform’s power chassis are elucidated, with specific emphasis on the development of the traction power system and modular operational systems. Theoretical and experimental analyses of steering parameters, stability, and field performance were conducted. Finite element simulation analysis of the frame revealed that under full-load conditions, the equivalent elastic strains during descent and ascent phases were 0.000317 and 0.00125, respectively. Maximum equivalent stresses (48.27 MPa and 231.6 MPa for descent and ascent, respectively) were localized at the beam–plate junctions, while peak deformations of 1.14 mm (descent) and 4.31 mm (ascent) occurred at mid-frame and posterior–mid regions, respectively. Field validation demonstrated operational velocities of 0.16–1.77 m/s (forward) and 0.11–0.28 m/s (reverse), with a maximum gradability of 20°. The platform exhibited multifunctional capabilities including weeding, spraying, fertilization, flower induction, harvesting, and transportation, demonstrating its potential to fulfill the operational requirements for pineapple field management. Simultaneously, the overall work efficiency is increased by more than 50%, compared to manual labor.