4 results about "Floriculture" patented technology

The application discloses an artificial intelligence-based flower art green plant maintenance monitoring and early warning method and system, which collects monitoring data of flower art green plants in real time, the monitoring data including plant image data, environmental parameters and soil parameters, removes the background of the plant image data, corrects the illumination and normalizes the scale to obtain standard plant image data; the environmental parameters and the soil parameters are input into a growth environment feature encoder to extract an environmental collaborative feature vector, and the environmental collaborative feature vector is subjected to multi-modal feature fusion with a plant phenotype feature vector to obtain a fusion feature vector; a multi-dimensional health index is calculated based on a plant growth state prediction sequence, the multi-dimensional health index is input into a hierarchical early warning decision module for comparison, and an early warning level is determined according to a comparison result. The application can improve the survival rate of green plants and the maintenance efficiency.

The invention discloses a Chinese rose leaf gloss whole genome SNP molecular marker combination and application thereof, the SNP molecular marker combination comprises 4746 SNP sites distributed on 14 chromosomes of a Chinese rose, and 4746 SNP markers significantly related to leaf gloss are screened by analyzing 358 parts of Chinese rose re-sequencing data and distributed on the 14 chromosomes. A liquid-phase gene chip is designed on the basis of the SNP loci, gene detection can be carried out in the seedling stage of the Chinese rose, and the leaf gloss character can be predicted in advance. Compared with a traditional breeding method, the method has the advantages of high throughput detection, low cost, shortened breeding period and the like, the breeding efficiency can be remarkably improved, the cultivation of a new variety of Chinese rose with good glossiness is accelerated, the requirement of the market for high-quality Chinese rose is met, and the development of the flower industry is promoted.

A system (10) for managing transport packagings, in particular horticulture / floriculture carts, the system comprising a plurality of user processing devices (16), operated by respective users (14), and at least one central processing device (20), wherein the central device (20) comprises: - an inventory module (43) configured to create and maintain an inventory of the transport packagings in the possession of at least one user (14), and therefore available to the user (14), - a tracking module (44) configured to track every transport of the transport packagings of the user (14), automatically collecting data from a transport document relating to the transport of the transport packagings of the user (14), wherein at least one user device (16) of the plurality of user devices (16) comprises: - a document reader (32) configured to detect the data contained in the transport document relating to the transport of the transport packagings of the user (14).

The invention relates to the technical field of intelligent order scheduling, in particular to a cross-store and franchise collaborative dynamic order scheduling method and system. The method comprises the following steps: acquiring real-time order data and a store information set, and based on the real-time order data and in combination with the store information set, analyzing matching information of order floriculture requirements and different store floriculture specialists to obtain a skill matching information set; based on the skill matching information set, the influence of the multi-physics field aging evolution effect on the flower material under the predicted distribution path is analyzed, and a path evaluation information set is obtained; based on the path assessment information set, analyzing path risk factors which cause order completion quality reduction and distribution loss increase, and obtaining order performance risk information; and according to the order performance risk information, dynamically adjusting order assignment and path planning in real time to minimize skill mismatching and transportation loss, and outputting a dynamic order scheduling log. Resource configuration is optimized, and order completion quality and user satisfaction are improved.