Stomata are the important structure for plant leaves to exchange gas and water with environment. In order to solve the problem that traditional analysis methods of stomatal traits adopt manual observation and measurement, which causes tedious process, low efficiency and prone to human error, you only look once (YOLO) deep learning model was adopted to complete automatic identification and automatic measurement of stomata in maize (Zea mays L.) leaves. Combined with the characteristics of stomata data set, the YOLO deep learning model was improved to effectively improve the precision of stomata identification and measurement. The prediction end in YOLO deep learning model was optimized, which reduced the false detection rate. At the same time, the 16-fold and 32-fold down-sampling layers were simplified according to the characteristics of stomata, which improved the recognition efficiency. Experimental results showed that the identification precision of the improved YOLO deep learning model reached 95% on the maize leaves stomatal data set, and the average accuracy of parameter measurement was above 90%. The proposed method can automatically complete the identification, counting and measurement of stomata of maize, which solved the low efficiency of traditional stomatal analysis methods, and it can help agricultural scientists and botanists to conduct the analysis and research related to plant stomata.