Towards Optimizing Garlic Combine Harvester Design with Logistic Regression

Abstract

In this paper, a garlic combine harvester machine was designed and some influential parameters of the machine were optimized. The working parts of the machine mainly consisted of a reel, a reciprocating cutter, a seedling conveyor, a profiling depth-stop device, a digging shovel and a lifting chain. Each part had unique structural parameters and motion parameters, as different parameters would deeply affect the performance of the machine. A logistical regression algorithm was utilized to analyze the working speed of the reel, the digging depth of the reciprocating cutter and the lifting speed of the lifting chain. This paper also discussed the influence of these three functions on the damage rate based on the collected data when harvesting garlic. Specifically, each function was tested 60 times for collecting data. The experimental results showed that the order of influence of the three functions on the damage rate was the digging depth, working speed and lifting speed. Moreover, the lowest damage rate was 0.18% when the digging depth was 100 mm, the working speed was 1.05 km·h−1 and the lifting speed was 0.69 m·s−1. A validation test was taken out based on the three functions of the analysis results, and the damage rate was 0.83%, which was close to the analysis results, and proved that the analysis results were accurate and meaningful. The research results are beneficial to the development and application of the garlic combine harvester.