Smart Farming

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Farming

Smart agriculture requires the use of modern technology to change the traditional agricultural production methods so that agricultural production activities have the automation “smart” while also achieving the highest input-output ratio.

Through the application of Internet of Things technology, deep learning technology, and automatic control system to the automation transformation of traditional agriculture, the production method of smart agriculture has become within reach of the actual production process.

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Rationalized irrigation: At present, the methods of agricultural irrigation are artificial drip irrigation or flood irrigation. This method lacks rational calculation and control of irrigation amount, so it will inevitably lead to over-irrigation and insufficient irrigation of crops. Therefore, smart agriculture requires an intelligent automatic irrigation system to ensure that the best irrigation amount is given to crops without wasting water resources so that crops continue to be in the best growing environment.

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Picking at the right time: The maturity time of crops is affected by weather, growth environment, and other aspects. The reasonable picking time of crops is only a relative time in traditional agriculture. However, for smart agriculture, it is required to accurately predict the optimal picking time of agricultural products and to provide accurate positioning of the pickable agricultural products to inform the planters when and where the crops have reached the picking time, to avoid the early picking time or Economic losses caused by too late.

Our Solution

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To solve the current bottlenecks of smart agriculture, COEUS applies the Internet of Things, sensor technology, and deep learning technology to all aspects of agricultural production to reduce waste and increase yield, to achieve the purpose of maximizing the return on investment ratio; one provided by COSEUS Series solutions include:

  • Combining large-scale soil temperature and humidity sensors and camera sensor data across all production areas, and then using deep learning models for inference calculations to obtain real-time agricultural irrigation schemes. Then the irrigation scheme calculated by the model is issued to the automatic irrigation control system, automatic irrigation drones, and other irrigation equipment to complete the rationalized automatic irrigation requirements.

  • For the timely picking of agricultural products, COEUS uses a variety of sensors deployed throughout the production cycle to automatically recover indicators such as temperature, humidity, and carbon dioxide concentration. At the same time, it uses cameras to recover crop growth and then pushes all the data into the timely picking deep learning model provided by COEUS to automatically calculates and analyzes the most reasonable crop picking time and the location of the crops that can be picked, to realize accurate picking activities at the right time.

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Phone: + 61 431938981
Coeustech Sydney, NSW 2000, Australia
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