Blue-ZE: Artificial vision based smart solution for weed detection in Blueberries plantations

Spain, Basque Country


Experiment objective

In the context of fruit growing and harvesting, a clear demand for farming 4.0 solutions has emerged. In this case, the blueberry growing and harvesting process is mainly manual, to prevent damage and largely depends on its point of maturity and the status of the plant. Therefore, a solution that allows the farmer to evaluate the condition of the plant at different points in his fields would allow him to make the appropriate decision when planning the growing and harvesting precision tasks and also adopt the best preventive method in order to eliminate the weeds.

Blue-ZE experiment’s main objective is to use Artificial Vision and Deep Learning technologies to digitize the task of assessing the condition of Blueberries plantations. The proposed solution, first of its kind, enables the extraction of key knowledge, which is based so far on the experience of farmers, without generational change, and supports growing and harvest planning automation in a near future.  Moreover, AGRIA ZE‘s strategic objective is to include this new smart solution as part of its catalogue of kits and tools to be integrated in its electrical vehicles and machinery. Blue-ZE solution will also be compatible with AGRIA ZE’s new Myzetrack data platform, designed to enable autonomous vehicle management and precision agriculture tasks, thus improving their efficiency and sustainability


Implementation of the Solution

At the beginning of the experiment, and in order to be able to implement the solution, AgriaZE has to analyze the use case scenario and has to take into account all the functional and technical requirements specification for the correct development and performance of Blue-ZE smart solution.

On the one hand, Blue-ZE’s architecture is defined to ensure the detection, classification and determination of the state of the plants, as well as data preparation process methodology, which enables an optimal training of proposed Deep Learning algorithms with the correct generated images dataset and ensure correct treatment and management of big data in agriculture.

On the other hand, a set of components is designed for the visualization and monitoring applications, the management of the smart solution and its integration with electric vehicles systems and AGRIA ZE Myzetrack data platform.

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