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The growth of field crops needs appropriate soil nutrients. As a basic fertilizer, liquid manure provides biological nutrients for crop growth and increases the content of organic matter in crops. However, improper spraying not only reduces soil fertility but also destroys soil structure. Therefore, the precise control of the amount of liquid manure is of great significance for agricultural production and weight loss. In this study, we first built the model of spraying control, then optimized the BP neural network algorithm through a genetic algorithm. The stability and efficiency of the optimized controller were compared with PID, fuzzy PID and BPNN-PID control. The simulation results show that the optimized algorithm has the shortest response time and lowest relative error. Finally, platform experiments were designed to verify the four control algorithms at four different vehicle speeds. The results show that, compared with other control algorithms, the control algorithm described here has good stability, short response time, small overshoot, and can achieve an accurate fertilizer application effect, providing an optimization scheme for research on the precise application of liquid manure.

Aiming to solve the problems of easy clogging and high energy consumption of multi-way fertilization devices for liquid manure, a crushing and impurity removal device for liquid manure was designed by combining the physical characteristics of liquid manure and impurities, and building the corresponding test bench. The proposed device could crush flexible impurities such as straw and filoplume and intercept hard impurities with high density. The main structural parameters of the device were determined according to the survey analysis and the theoretical design. The influences of cutter head shape, cutter edge angle, cutter shaft speed, and cutting clearance on the disqualification rate and energy consumption of straw crushing were obtained by a single-factor experiment. Furthermore, the Box–Behnken central composite design method of the response surface was employed to investigate the effects of the cutter shaft speed, cutting clearance, and cutter edge angle on the disqualification rate and energy consumption of straw crushing. In addition, the working parameters of the device were optimized by employing the response surface method. On this basis, the mathematical relationship model among the disqualification rate, energy consumption, and all influencing factors was established. The results show that the optimal combination of working parameters includes a cutter shaft speed of 312 r/min, a cutting clearance of 1.4 mm, and a cutter edge angle of 45°. From the prediction model, the predicted failure rate was 4.15%, and the predicted energy consumption was 47.53 J. The verification experiment was then performed under the optimal combination of working parameters. The obtained disqualification rate was 4.08% and the energy consumption was 47.56 J, which met the design and work requirements.