Deep Learning Agricultural Information Classification Combined With Internet of Things Technology in Agricultural Production and Economic Management

Yang, He; Wen, Tiejun; Yang, Xi; Lin, Haoran

doi:10.1109/access.2022.3176643

Cited by 4 publications

(1 citation statement)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The applications for agricultural information classification analysis and agriculture production management have been developed [7], such as the classification model used to determine superior food crop clusters [8], The use of machine learning models for crop cultivation prediction [9], allows farmers to assess the cultivated types, monitor plant growth, and choose the correct harvest time [10]. System development using a classification model that combines qualitative and quantitative methods improves the relevance [11], completeness, and accuracy in finding information and increases the utilization of agricultural data information [12].…”

Section: Introductionmentioning

confidence: 99%

Classification of Agriculture Area based on Superior Commodities in Geographic Information System

Sumaryanti¹,

Widjastuti²,

Tempola³

et al. 2022

IJACSA

View full text Add to dashboard Cite

Research carries a classification model that combines LQ analysis and hierarchical classification using a single linkage. The classification results are a basis for mapping the potential of agriculture areas based on superior food commodities in Merauke Regency, Indonesia. LQ analysis is used to select food commodities. In contrast, the application of single linkage uses the production of three features, rice, corn, and peanuts, which have an LQ value>1, to group sub-districts based on agricultural potential. Intelligent mapping is represented by mapping the subdistricts agricultural areas according to the cluster. The classification results show that the first cluster has sixteen subdistrict members, the second consists of three sub-districts, and the third cluster consists of one sub-district. Each cluster member has similarities based on the distance measurement with the smallest value using the Euclidean distance. The proposed classification model is a creative idea to map agricultural areas, which can present information on regional potential based on superior food crop commodities.

show abstract