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In order to solve the problems of traditional ant colony algorithm in agricultural machinery path planning, such as initial blind searches, deadlock, slow convergence rate, and low-quality converged path, a path planning method based on jump point optimized ant colony algorithm (JPOACO) was proposed. Initially, the jump point search optimization algorithm was employed to preprocess the map, thereby obtaining simplified jump points. These simplified jump points were utilized for pheromone initializing on the grid map, to enhance the guiding capability of simplified jump points and reduce blind search in the early stages. Secondly, a punish mechanism for dead ant was designed to lower the pheromone levels on paths traversed by ants which fell into deadlock, and to decrease the occurrence of deadlocks. Furthermore, the heuristic information function was redesigned and a hierarchical pheromone factor was introduced to enhance convergence speed and shorten the converged path length. Finally, a path optimization strategy was applied to eliminate unnecessary path nodes, further reduce converged path length and improve smoothness, ultimately improve the converged path quality. Simulation results showed that in simple environments, the JPOACO algorithm reduced path length by about 22.6% and 2% in comparison with traditional ant colony algorithm and other optimized ant colony algorithms, respectively. It also decreased convergence number and convergence time by about 77.0%, 77.5% and 49.3%, 87.8%, respectively. The zero-death number and zero-death time were reduced by about 19.5% and 80.5% in comparison with the latter. In complex pineapple planting environments, JPOACO achieved a path length reduction of 16.6% and 4.7%, decreased convergence number and convergence time by about 77.1%, 17.4% and 73.7%, 47.4%, respectively. The zerodeath number and zerodeath time were reduced by about 34.3% and 58.2% in comparison with the latter. These results indicated that the JPOACO algorithm was highly feasible and applicable.