Abstract: Aiming at the problem of removing impurities such as rocks and clods from stored potatoes, a classification method of potato and impurities based on active thermal infrared imaging was proposed. Firstly, the heat transfer model of potato and impurity was established based on lumped parameter method, and the factors affecting the heating effect and leading to temperature difference were obtained as material thermal properties, wind speed, wind direction angle and wind temperature. Then, using the finite element method (FEM) to analyze the thermal image of potatoes and impurities under the hot air excitation, the potato surface temperature was significantly higher than that of rocks and clods, and four level single factor simulation experiment was carried out for the wind speed, wind direction angle and wind temperature respectively, and it was concluded that the difference in surface temperature of potatoes and impurities was nonlinear and positively related to the wind speed, wind direction Angle and wind temperature in a certain period of time. Finally, potatoes and impurities were classified on the test platform. The experimental results showed that the optimal image recognition conditions were wind speed of 4m/s, wind temperature of 40℃ and wind direction angle of 90°, and the recognition success rate was 97%. It can provide technical reference for potato removal in harvesting-storage process.

Key words: potato, thermal excitation, thermal infrared imaging, impurity removal

摘要： 为解决入库马铃薯中石块、土块等杂质剔除的问题,提出一种基于主动热红外成像的马铃薯与杂质分类方法。建立基于集总参数法的马铃薯和杂质的传热模型,获得影响受热效果与导致温度差异的因素为材料热物性、风速、风向角度、风温;利用有限元法分析马铃薯和杂质在外部热风激励下的热像图,马铃薯表面温度显著高于石块与土块,并对风速、风向角度、风温分别进行四水平单因素仿真试验,得出马铃薯与杂质的表面温度差异大小在一定时间内与风速、风向角度和风温呈非线性正相关。在试验平台上对马铃薯和杂质进行分类试验,结果表明:最佳图像识别处理条件为风速4m/s、风温为40℃、风向角度为90°,识别成功率为97%。为马铃薯收获—仓储过程中除杂提供技术参考。

关键词: 马铃薯, 热激励, 热红外成像, 杂质剔除