Plant growth self-feedback learning cultivation method based on artificial intelligence

Abstract

The invention relates to a plant growth self-feedback learning cultivation method based on artificial intelligence, and the method comprises the steps: continuously collecting two-dimensional and three-dimensional images of each stage of plant growth, and continuously collecting plant growth environment data; inputting the collected two-dimensional and three-dimensional images into a plant growth model, optimizing the plant growth model, and obtaining current plant growth condition data; meanwhile, according to the obtained current plant growth condition data, based on the set plant growth parameters, configuring the growth environment of the plants in all the plant cultivation boxes in all the growth stages; and training a classification algorithm based on the obtained plant growth condition data and plant growth environment data to obtain a production parameter tuning model and tuning growth parameters. And obtaining the optimal plant growth parameters after multiple iterations. According to the method, real-time statistics and analysis can be carried out on the plant from the initial seedling planting stage to the flowering result, and plant growth research is automatically carried out in a self-feedback mode.

Description

technical field [0001] The invention relates to the technical fields of artificial intelligence and plant cultivation, in particular to an artificial intelligence-based self-feedback learning cultivation method for plant growth. Background technique [0002] With the rapid development of artificial intelligence deep technology, deep neural network and convolutional neural network have made remarkable development and great progress in the field of image recognition. In recent years, deep learning technology has been applied in a variety of ways in image recognition. For example, image recognition competitions and datasets in the field of artificial intelligence such as ImageNet have given birth to the design of neural network structures that are very effective for image recognition. It has promoted the development and birth of many theories, technologies and methods related to machine learning. [0003] At present, although artificial intelligence control has been introduced...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the embodiments of the present invention is to address the structural shortcomings of the prior art, and propose a plant growth self-feedback learning and cultivation method and system based on artificial intelligence, using the leaves, plants, flowers, and fruits of plants in a controlled environment. The image structure judges the growth state of the plant, which overcomes the problems of low recognition efficiency and error caused by the artificial method of plant recognition in the existing plant organ recognition process. Through the algorithm modeling of the convolutional neural network, it can Accurately identify the growth status of plants, and use this to adjust the set production parameter data more accurately, and give the best different growth environment parameters

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