Abstract: Mature stage monitoring can provide significant scientific instruction for the management of litchi orchard. However, nowadays, any research based on mature stage monitoring in orchard has not been reported yet. Given that this paper proposed a monitoring method of litchi orchard mature stage based on electronic nose. We used electronic nose (PEN3) to sample litchis which were in 6 different mature stages (s1, s2, s3, s4, s5 and s6) from about 25 days after it fruited to maturity, and measured 3 physical characteristics of litchi fruits (fruits' size, fruits' weight and fruits' soluble solid content). According to the changes of litchi's physical characteristics in different mature stages, the 2 physical indices (fruit size and weight) of litchi from the 30th to the 39th day and from the 53th to the 60th day after it fruited were increasing comparatively faster than other stages. That was to say, the litchi fruit normally grew fast in the 2 periods. In addition, the soluble solid content of litchi grew slowly from the 53th to 60th day after it fruited and could not be tested before the 32th day after it fruited. After extracting each sensor's response value in stable time (75 s), we used loading analysis (Loadings) for sensors optimization, and kept sensors (R2, R4, R6, R7, R8, R9 and R10) for the next analysis. Loadings results also showed that R7, R4 and R6 were comparatively more sensitive than other sensors when identifying the volatile of litchi, which provided a reference for the next research when exploring especial instrument for litchi quality detection based on bionic olfaction mechanism. Then, unitary processing was used for the noise reduction of the sensor's response value. At last, we used linear discriminant analysis (LDA) for further extraction of feature information to decrease the redundant information. In addition, LDA could not detect the mature stage of litchi in orchard effectively. LDA classification results showed that the sample points in s2 and s3 were overlapped by each other, which had poor classification effect. The sample points in s5 and s6 were not overlapped by each other, but the distance between them was close, which may easily cause the confusion in practical monitoring of fruit mature stage. For further research the feasibility of electronic nose application for litchi mature stage monitoring in orchard, fuzzy c means clustering (FCM) method, k-nearest neighbor (KNN) method and probabilistic neural network (PNN) method were used for pattern recognition. The experimental results showed that the accuracy of FCM for litchi mature stage monitoring in orchard was 89.17%. The classification effects of s2 and s3 were undesirable, and the mature stages s5 could not be absolutely distinguished from s6. After building up KNN and PNN detection model, their accuracies of training set were all 100%, and their accuracies of test set were both 96.67%, which had good effect for litchi mature stage monitoring in orchard. By comparing electronic nose analysis results with physical characteristics changes, we could infer that the accumulation speed of litchi's inner compositions had inverse correlation with the size growing speed of litchi fruit. That meant when the size of litchi fruit grew faster, the accumulation speed of litchi's inner compositions was slower. Otherwise, the accumulation speed of litchi's inner compositions was faster, and the classification effect was better. This research proves the feasibility of using electronic nose for litchi mature stage monitoring in orchard, and provides the reference for fruit quality and situation monitoring in orchard in the future.