Abstract: In order to further improve the allocation performance of kiwifruit（Actinidia chinensis Planch.） estimated yield in planting areas, local search algorithms were improved through sparrow search algorithm and variable helix factor,and the learning strategy of progressive lenses was used to accelerate the convergence speed of the improved local search algorithm. The results showed that when the estimated yield of kiwifruit remained unchanged, the allocation time gradually decreased as the value of the variable helix factor increased;when the value of the variable helix factor remained unchanged, the estimated yield of kiwifruit increased and the allocation time also increased. The estimated yields of kiwifruit were 10, 20, 30, 40, 50, and 60 tons respectively, with four planting areas of 500, 650, 700, and 850 m². It was recommended to set the variable helix factor value to 4 when the estimated yield of kiwifruit was 10~60 tons;when the estimated yield was 10~40 tons, planting area 3 could meet the optimal planting interval. When the estimated yield was 50~60 tons, planting area 4 could meet the optimal planting interval. Kiwifruit planting interval was reasonably allocated based on different estimated yields and planting areas to ensure that kiwifruit received sufficient nutrients. The improved local search algorithm had a faster convergence speed, and by 500 iterations, the algorithm had tended to converge. The convergence speed of deep learning, particle swarm optimization algorithm, and grey wolf algorithm was lower than that of the improved local search algorithm.

Key words: improved local search algorithm, kiwifruit（Actinidia chinensis Planch.）, estimated yield, planting areas, planting interval, variable helix factor, allocation performance