Intelligent Recommendation and Matching Method for Agricultural Knowledge Based on Context-Aware Models

The personalized recommendation of the cloud platform for agricultural knowledge and agricultural intelligent service is one of the core technologies for the development of smart agriculture.Revealing the implicit laws and dynamic characteristics of agricultural knowledge demand is a key problem to be solved urgently.In order to enhance the matching ability of knowledge recommendation and service in human-computer interaction of cloud platform,the mechanism of agricultural knowledge intelligent recommendation service integrated with context-aware model was analyzed.By combining context data acquisition,data analysis and matching,and personalized knowledge recommendation,a framework for agricultural knowledge recommendation service is constructed to improve the ability to extract multidimensional information features and predict sequence data.Using the cloud platform for agricultural knowledge and agricultural intelligent service,this research aims to deliver interesting video service content to users in order to solve key problems faced by farmers,including planting technology,disease control,expert advice,etc.Then the knowledge needs of different users can be met and user satisfaction can be improved.

A Weakly-Supervised Method for Named Entity Recognition of Agricultural Knowledge Graph

It is significant for agricultural intelligent knowledge services using knowledge graph technology to integrate multi-source heterogeneous crop and pest data and fully mine the knowledge hidden in the text.However,only some labeled data for agricultural knowledge graph domain training are available.Furthermore,labeling is costly due to the need for more data openness and standardization.This paper proposes a novel model using knowledge distillation for a weakly supervised entity recognition in ontology construction.Knowledge distillation between the target and source data domain is performed,where Bi-LSTM and CRF models are constructed for entity recognition.The experimental result is shown that we only need to label less than one-tenth of the data for model training.Furthermore,the agricultural domain ontology is constructed by BILSTM-CRF named entity recognition model and relationship extraction model.Moreover,there are a total of 13,983 entities and 26,498 relationships built in the neo4j graph database.

Biocultural Diversity of Sarangani Province, Philippines: An Ethno-Ecological Analysis

This paper discussed effects of lowland-associated influences on upland ecology, food security and biocultural diversity in the Sarangani farming communities of the Philippines. In the uplands of Sarangani Province, the conservation of traditional rice varieties, the centrality of rice in tribal life, and the continued observance of planting rituals attest to its cultural significance and convey a common desire for cultural preservation and community solidarity. Economic and socio-political pressures had transformed tribal communities, although vestiges of traditional farming systems are still being practiced in remote sitios（villages）. Changing land use patterns had also resulted in shrinking farm sizes and consequently in food insecurity in the Sarangani uplands. Extractive industries（i.e. logging, mining and charcoal making） and swidden farming were observed to cause widespread environmental degradation, while modern agriculture had undermined the capacity of indigenous peoples to survive because of their complete dependence on lands and resources. With the reality that cultural and biological diversities are inextricably linked, trans-disciplinary strategies coupling indigenous knowledge systems with scientific knowledge should, therefore, be instituted to save the Sarangani upland ecosystem, the indigenous peoples and their tribal resources.