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Performance Test and Parameter Optimization of Trichogramma Delivery System.

Shengzheng Ji¹, Jinliang Gong¹, Kai Cui²

¹School of Mechanical Engineering, Shandong University of Technology, Zibo 255000, China.

|November 24, 2022

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Unmanned aerial vehicles (UAVs) offer a sustainable solution for delivering Trichogramma, natural pest enemies, to agricultural fields. This study optimized UAV parameters for accurate Trichogramma pill delivery, achieving 93.29% accuracy.

Area of Science:

Agricultural Entomology
Precision Agriculture
Robotics in Agriculture

Background:

Trichogramma wasps are vital biological control agents for agricultural pests.
Current Trichogramma delivery methods are costly and environmentally concerning.
Unmanned Aerial Vehicles (UAVs) present a potential for efficient and eco-friendly delivery.

Purpose of the Study:

To investigate the feasibility of using UAVs for Trichogramma delivery.
To optimize the parameters of a UAV-based Trichogramma delivery system.
To determine the factors influencing the accuracy of Trichogramma pill deployment.

Main Methods:

A six-rotor plant protection UAV equipped with a specialized Trichogramma delivery device was utilized.
A Box-Behnken experimental design was employed to test Trichogramma pills.

Keywords:

UAV feasibility parameter optimization trichogramma

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Analysis of Variance (ANOVA) and response surface methodology were used to analyze data and optimize parameters.

Main Results:

Launch height significantly impacted Trichogramma pill landing accuracy, followed by launch interval and launch speed.
Optimal parameters were determined as: launch height 147.95 cm, launch speed 3.7745 m/s, and launch interval 2.98 s.
The optimized system achieved a maximum landing accuracy of 93.29% with a 1.71% average relative error.

Conclusions:

UAVs are a feasible and effective method for delivering Trichogramma for pest control.
The optimized delivery system meets practical requirements for agricultural applications.
This research provides a foundation for developing advanced UAV-based biological control systems.