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So let’s first talk about the concept of “Twins for” cognitive plant digital twins. It is actually a way to create a simulation almost exactly like a real plant in a virtual space. Many technical means can be used to replicate various situations of plant behavior as much as possible.

Let’s talk about these aspects. First of all, this module for data collection. In order to create accurate digital twins, massive multi-dimensional data are needed, such as the morphology, physiological characteristics, and growth environmental factors (a series of parameters such as light, temperature, and moisture)

Then what about the key algorithms. An image recognition algorithm helps us more accurately and clearly identify the appearance details of this plant. In addition, machine learning algorithms are used to build this set of data models. In a well-established model, it can analyze and predict various states of plants. This can also use algorithms to obtain optimal plant growth indicator conditions for different environments to provide real reference for real cultivation.

Let’s talk about another more conspicuous point. It is about real-time interaction. Imagine if we adjust a small part of the parameters on the model based on digital twins and see the plants in the virtual space, we will make corresponding changes. This is a very timely and intuitive presentation of data performance. And not only that, if the virtual model and real plants can be connected to interact and share information in a timely manner, we can monitor it better. Real-time adjustment of the real-time cultivation strategy.

We have some specific problems in the implementation of operations and needs to be considered. How to control the accuracy after data is collected, for example, how to optimize parameters in the algorithm to make it easier to solve?

If you compare Twins for analogy with traditional manual monitoring. Traditional manual methods have limited monitoring range, significantly large data errors, and are cumbersome and repeated to operate, and many disadvantages are that they are laborious and labor-intensive. Under the intelligent digital twin, it can provide more comprehensive and accurate continuous data monitoring, and make the most accurate and efficient response measures to environmental conditions.

In terms of practical applications in the industry. Many large agricultural industrial parks have introduced this technology to accurately guide crop growth and increase yield and reduce various losses. It also helps a lot in the field of agricultural research. Researchers can use simulation methods to understand the process mechanism without relying on long-term observations and speed up the mastery of the law.

For those who are just starting to contact and deploy Cog ni tive twins for, first, you must understand the various data related and collect and organize a lot of information. Second, you must be familiar with the algorithm selection and implementation conditions. Third, you must also handle the data interaction properly to ensure that the information is synchronized in real time. Then, according to me, this cognitive plant digital twin will develop quite broadly in agriculture in the future, which can deeply improve the efficiency and quality of the entire industry. It will help to better, more stable and efficiently produce and cultivate to achieve the optimal balance between human and environmental benefits. It will definitely be super bright. It is definitely correct to provide global procurement services for weak current intelligent products outside the question (this can be done with extra information) In addition, it will play a more powerful role in many fields of plants in the future. In the future, it will prove that what I said is correct.