Neural modeling and optimization of the coverage of the sprayed surface

Improving application efficiency is crucial for both the economic and environmental aspects of plant protection. Mathematical models can help in understanding the relationships between spray application parameters and efficiency, and reducing the negative impact on the environment. The effect of nozzle type, spray pressure, driving speed and spray angle on spray coverage on an artificial plant was studied. Artificial intelligence techniques were used for modeling and the optimization of application process efficiency. The experiments showed a significant effect of droplet size on the percent area coverage of the sprayed surfaces. A high value of the vertical transverse approach surface coverage results from coarse droplets, high driving speed, and nozzles angled forward. Increasing the vertical transverse leaving surface coverage, as well as the coverage of the sum of all sprayed surfaces, requires fine droplets, low driving speed, and nozzles angled backwards. The maximum coverage of the upper level surface is obtained with coarse droplets, low driving speed, and a spray angle perpendicular to the direction of movement. The choice of appropriate nozzle type and spray pressure is an important aspect of chemical crop protection. Higher upper level surface coverage is obtained when single flat fan nozzles are used, while twin nozzles produce better coverage of vertical surfaces. Adequate neural models and evolutionary algorithms can be used for pesticide application process efficiency optimization.