Mechanical Pollination – From Concept to Reality

Jul 24, 2023 | Pollination

By Meital Shaked


The importance of supplemental mechanical pollination has been gaining significant interest lately.

Overcoming challenges related to both, wind and insect pollination, is a topic keeping growers up at night these days. Thankfully, at Edete, we have committed ourselves to developing solutions to address these challenges. Our solutions are designed to optimize pollen dispersal towards trees, ensuring comprehensive coverage.

This article focuses on a crucial aspect of Edete’s pollinator (trademark 2Be) mechanical design process, specifically the utilization of flow and mechanical simulations and analysis to achieve an optimal design.

Analysis Based Design

At the core of any successful mechanical design process lies analysis. Analysis serves as the foundation for making informed decisions, enabling us to create efficient, reliable and optimized designs. By employing various analytical techniques, we can evaluate and predict the behavior of our designs under different conditions. This method also saves time and financial resources for the company.

Let’s Begin

Mechanical engineers are familiar with the initial steps of a new project, involving receiving a set of requirements and brainstorming concepts based on previous knowledge and experience. However, mechanical pollination is a unique challenge, especially considering the limited development in the agricultural sector. Therefore, our solution should be groundbreaking and innovative.

One of the key parameters in the design of Edete’s pollinator is the efficient dispersal of pollen to the tree.

Flow Simulations and Analysis

The first step in the design process was to understand the “shape” of the airflow that would carry pollen from our machine to the tree. This was crucial for designing the blowers and determining how to dispense the pollen.

Initially, we examined the unrestricted airflow patterns originating from the blower in an open environment (free flow field). Subsequently, we explored the airflow behavior from multiple blowers along the tree, taking into account external wind and environmental conditions.

The first simulation involved the free flow of air from a single blower. This allowed us to explore the “flow field”, in terms of velocity and vectors (streamlines) for unrestricted airflow. We adjusted the analysis parameters and studied their impact on the flow field and wind path.

The following figures illustrate the results of the analysis: Figure 1 simulation results of a single blower – wind velocity and flow field, Figure 2 simulation results of a single blower – airflow vectors (streamlines).

Figure one analysis results wind velocity simulation results of a single blower
Figure 1: Analysis results – wind velocity
Figure 2 Analysis results vectors streamlines
Figure 2: Analysis results- vectors (streamlines)

To ensure the reliability of analysis results despite the inherent mathematical limitations of computational modeling programs, it is crucial to verify and validate the analysis through comparison with actual measurements. To achieve this, a series of physical tests were conducted.

The test setup included the following components: a single blower, a smoke machine, a high-speed camera (to observe wind dispersal), and measurement instrumentation such as sensors for wind velocity. These elements were carefully assembled to capture and evaluate the performance of the system.

The test results were then compared to the analysis results. By aligning the physical measurements with the analytical predictions, an iterative process of calibration was performed, resulting in an improved and validated analytical model.

Figure 3 herein demonstrates the test setup during smoke dispersal

Figure 3 Test setup demonstrating  wind behavior using smoke
Figure 3: Test setup – demonstrating  wind behavior using smoke

The subsequent step involved scaling up the analysis to explore the airflow of the system at a pollinator level. The analysis was configured to simulate a set of blowers dispersing air along a tree. This simulation is used to examine the tree’s resistance to airflow and the interaction between the streamlines generated by several blowers. Various parameters, such as the distance between barrels and their length, were considered during the learning process. The optimal parameter set obtained from this analysis was then utilized in the pollinator design. It is worth mentioning that this analysis, like the previous ones, underwent verification and calibration as part of our analysis-based design approach.

Figure 4 demonstrates the analysis of the airflow from several blowers directed toward a tree.

Figure 4 air flow analysis from several blowers
Figure 4: Air Flow analysis from several blowers

For the verification and validation of the model, physical tests were conducted. A set of sensors was mounted on a single tree, measuring the airspeed during pollinator operation.

The sensor setup is demonstrated in Figure 5 herein.

Figure 5 Air Flow measurements test setup
Figure 5: Air Flow measurements – test setup 

Mechanical Simulations 

The pollinator, being an agricultural machine, needs to endure harsh environmental conditions and to sustain dynamic loads during operation. The design-based-analysis methodology incorporated an iterative process: the mechanical design is analyzed, whether as assemblies, sub-assemblies or single parts, to assess the stress distribution and identify potential weaknesses. Based on the results, the design is being updated accordingly, until the desired outcome is achieved before manufacturing or construction begins. This approach ensures an optimized design, prevents failures and reduces costs.

For complex design, a dynamic analysis was utilized, combining multi-body and structural analysis. 

The example provided below demonstrates an analysis of the folding mechanism of one of our advanced pollinator designs. The purpose of the analysis was to explore the structure’s dynamic behavior under mechanism movements: deployment and folding.  The analysis results reveal a buckling of the structure under its self-weight load while folding.

Based on the stress level results and the shape of the buckling mode, the mechanical design was updated. The process involved several iterations: reinforcements were added, and the simulation was recalculated. The final mechanical solution was implemented only after the analysis results aligned with the desired design criteria.

The figures below exemplify the multi-body analysis results – on the right are the results of the original design, while on the left are the results of the updated design.

Figure 5 Multi body analysis results left figure results after reinforcements and right figure results before reinforcements
Figure 5: Multi-body analysis results: (a) left figure results after reinforcements (b) right figure results before reinforcements
Figure 6 structural analysis results side view left figure results after reinforcements and right figure results before reinforcements
Figure 6: Structural analysis results – side view : (a) left figure results after reinforcements (b) right figure results before reinforcements
Figure 7 Structural analysis results front view left figure results after reinforcements and right figure results before reinforcements
Figure 7: Structural analysis results – front view: (a) left figure results after reinforcements (b) right figure results before reinforcements


For the journey from ‘concept to reality’ to be successful, it needs to be well-calculated. At Edete Precision Technologies for Agriculture, a progressive and innovative AgTech company, the design process is well-informed.

At Edete, we leverage the technology of mechanical and flow simulation tools as an integral and crucial part of our design process. By doing so, we can guarantee that by the time our pollinators meet the orchards, they will be able to handle the challenge of mechanical pollination in the best and most adequate way possible.


The analyses were performed using LS-DYNA multi-physics Simulation tool.

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