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In view of the difficulties in weeding and plant protection in the middle and late period of maize planting, this paper proposed a self-propelled thermal fogger chassis. According to the theoretical calculation and agronomic requirements for maize planting, the structure and working principles of the self-propelled thermal fogger chassis were introduced. On this basis, the multi-body dynamics model of chassis structure was established, and the chassis traction, steering and obstacle surmounting performances were also analyzed. Then the rationality and the feasibility of the design were verified through the furrow running test and test equipped with thermal fogger. Test results showed that, the traction performance improves with the decrease of soil deformation index and increase of cohesion, and when track pre-tensioning force was about 1000 N, the machine had a good traction performance; with the decrease of the soil deformation index and the increase of cohesive force, the stability of the single side brake turn of the chassis becomes better; on the contrary, with the increase of the tightness of the crawler, the steering radius turns smaller and the steering stability becomes worse. Under heavy clay, with the pre-tensioning of 1000 N, the machine has better steering stability and smaller turning radius. The obstacle-surmounting simulation result shows that on sandy soil road, the maximum climbing angle for the chassis is 42°, the height of vertical obstacle crossing is 170 mm and the trench width is 440 mm. The study provides a reference for the design of plant protection machinery in the middle and late stages of maize planting. . Performance analysis and test of a maize inter-row self-propelled thermal fogger chassis. Int J Agric & Biol Eng, 2018; 11(5): 100-107.

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Soil block distribution is one of the important indexes to evaluate the tillage performance of agricultural machinery. The traditional manual screening methods have the problems of low efficiency and damaging the original surface of the soil. This study proposes a statistical method of farmland soil block distribution based on deep learning. This method combines the adaptive learning rate and squeeze-and-excitation networks channel attention mechanism based on the original Mask-RCNN and uses the improved model to identify, segment and distribute statistics of the farmland soil blocks. Firstly, the influence of different learning rates and an improved Mask-RCNN algorithm model on training results were analyzed. Secondly, the effectiveness of the model in soil block identification and size measurement was analyzed. Finally, the identified soil blocks were classified accordingly, and the scale problem of soil block distribution after removing edge soil blocks was analyzed. The results show that with the decrease of learning rate, the loss value of model training decreases and the prediction accuracy of model is improved. The average precision value of the improved model increased by 25.29 %, and the recall value increased by 8.92%. The correlation coefficient of the maximum diameter measured by manual measurement and the maximum diameter measured by model algorithm was 0.99, which verifies the feasibility of the algorithm model. The prediction error of the model is the smallest when the camera height is 40 cm. Large-scale detection of soil block size in an experimental field in Hefei, Anhui, with an average confidence of over 97%. At the same time, the soil block is effectively classified according to the set classification standard. This study can provide an effective method for the accurate classification of soil block size and can provide a quantitative basis for the control of farmland cultivation intensity.

The forming process of spline cold rolling was analyzed. The unit average pressure, contact area and rolling force in the cold rolling precision forming process were analyzed and solved. The mechanical and mathematical model has been set up on the basis of the analysis. The numerical simulation of spline cold rolling process was carried out. The results obtained by comparison of theoretical analysis, numerical simulation and experiment provide a theoretical basis for the study and application of spline cold rolling process.