Abstract: Abstract: Accurate detection of historical status of granary is helpful for its management. Based on statistical analysis of grain temperature in granary, this paper presents a method to detect the storage status of the granary in attempts to resolve the problem of time-consuming and tediousness faced by inventory inspection. Warehouse with state of being empty, filled with fresh grain and ventilation was used as an example. Analysis of the distribution of statistical characteristics of historical grain temperature showed that the empty warehouse could be detected using the difference in temperature between the upper and lower adjacent layers of the grain bulk as well as the variation of the grain temperature; the fresh grain could be detected by the difference in temperature between adjacent layers and the difference in standard deviation of the grain temperature; the ventilation could be detected by the change in the grain temperature and the standard deviation difference of the grain temperature. The threshold intervals of the characteristic parameters of the three states were set preliminarily from analysis of the statistical characteristics of the grain temperature. Based on the threshold intervals, three optimal levels were selected and the orthogonal experiments of two-factors and three-levels were designed. In the same grain depot with 11 granaries, new and ventilated granaries were chosen for the orthogonal experiments. The optimal threshold range of the statistical characteristic parameters were determined by analyzing the statistical characteristics of the historical grain temperature changes in the 11 granaries. Multi-variable decision tree for inventory status detection was designed and the optimal threshold of the characteristic parameters of the multi-variable decision tree were as follows. The temperature difference between adjacent layers for empty granary status detection was -0.12, 0.12 ℃. The temperature difference between adjacent layers was more than 0.02 (or 0.03) ℃, and the standard deviation was less than 2.0 (or 2.1) ℃. The average temperature change rate in the parameters for detecting the ventilation characteristics was -0.5, 0.5 ℃/d and the standard deviation change rate was -0.21, 0.21 ℃/d . Finally, seven granaries in different provinces were selected to test the detection method. The results showed that the accuracy and recall rates were 78% and 82% respectively for the empty granary state, and 74% and 70% respectively for new grain state, and 91% and 88% respectively for ventilated state. The test results showed that the proposed method detected granary at empty and ventilated state more accurate than at new grain state.