Because of its good performance, crawler-type running gear plays a very important role in the fields of modern agriculture. This article aims to study the construction of the drive system of the crawler self-propelled rotary tiller with the deep learning network and carry out the system simulation experiment. In this article, deep learning-related algorithms, auto-encoding networks, convolutional neural networks, and structural design of crawler self-propelled rotary tillers are proposed. It then used the self-developed crawler-type rotary tiller and straw paddle machine to compare the field operation performance with the combination of ordinary wheeled tractors and rotary tillers. The experimental results show that the tillage performance indicators such as the working depth, tillage depth stability, ground flatness, stubble pressing depth, and vegetation coverage qualification rate of the “crawler self-propelled tractor + straw stubble pulper” are better than those of “wheel tractor + ordinary rotary tiller” and “crawler tractor + ordinary rotary tiller,” increased by 9.92% and 4.88%, 4.31% and 4.13%, 42.59% and 19.12%, 40.15% and 34.57%, and 13.04% and 7.16%, respectively. The mechanical transplanting index was significantly better than other treatments. The yield increase effect of the field test is remarkable, with the average yield increase rate of 9.63% and 4.57%, which is suitable for popularization and application in the southern double-cropping rice area.
