Spraying strategy optimization with genetic algorithm for autonomous air-assisted sprayer in Chinese heliogreenhouses

Abstract

Subject to narrow space and large planting density, many advanced crop protection machines are not suitable for Chinese heliogreenhouses. Under these conditions, a novel autonomous air-assisted sprayer is designed to realize automatic spraying and to improve droplet deposition uniformity. With this sprayer, this paper focuses on precise air-assisted spraying control, and a spraying optimization strategy is researched to obtain uniform deposition of droplets on crops. Firstly, the autonomous sprayer and its spraying sub-system are introduced, and the simplified airflow model is constructed and validated via spraying experiments. Furthermore, based on this model, the relationship between droplets deposition area and spraying mechanism posture is deduced, and an offline optimal spraying strategy based on Genetic Algorithm (GA) is proposed. The algorithm encodes 4 key parameters to control the spraying mechanism’s behavior, and the Coefficient of Variation (CV) of droplets deposition on various parts of crops is computed as the fitness value in algorithm iterations. Illustrative optimization processes are simulated, which show the characteristics of uniform spraying solution. With these optimized parameters pre-set in the autonomous sprayer, real spraying tests are carried out in a heliogreenhouse. The results show that the deposition volumes are all in the range of 1.2 ∼ 1.4 μL/cm2. The final CV values of three sets of data are 9.9%, 10.4% and 11.2%, which are small enough for spraying operation. Simulation results and real tests data validated the effectiveness and convenience of the proposed optimal spraying strategy. Finally, some conclusions and future work are discussed. With this spraying strategy optimization, optimized spraying behavior can be planed for different crops with different planting patterns in Chinese heliogreenhouses.