Traceable self-feedback learning urban plant factory

Abstract

The invention relates to a traceable self-feedback learning urban plant factory which comprises an application layer, a contract layer and a service layer. The application layer comprises a plurality of independent soilless culture systems and a traceability platform; the contract layer receives the data collected by the application layer through the Internet of Things interface and executes a contract; the service layer comprises an artificial intelligence platform, a big data platform and a block chain platform, the artificial intelligence platform comprises a model training unit and a model storage unit, and the model storage unit is used for storing a plant growth model and a production parameter adjustment and optimization model. The invention has the advantages that real-time statistics and analysis can be carried out on plants from the initial seedling planting stage to the flowering result, and plant growth research is automatically carried out in a self-feedback mode. Meanwhile, by means of the block chain technology, the agricultural product quality supervision and service level is improved through an informatization means, and the purposes that the production process can be recorded, the product flow can be tracked, the storage and transport information can be inquired, and the quality problem can be traced in agricultural product quality management are achieved.

Description

technical field [0001] The invention relates to the technical fields of artificial intelligence and plant cultivation, in particular to a traceable self-feedback learning urban plant factory. 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 in the field of plant cultivation te...

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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 traceable self-feedback learning urban plant factory and system, using the image structure of the leaves, plants, flowers, and fruits of the plant in a controlled environment. The growth status is judged, 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 convolutional neural network, the growth of plants can be accurately recognized state, and based on this, more accurate data adjustments can be made to the set production parameter data, and the best different growth environment parameters can be given.

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