An Ontological Knowledge Representation for Smart Agriculture

Bhuyan, Bikram Pratim; Tomar, Ravi; Gupta, Maanak; Ramdane-Chérif, Amar

doi:10.1109/bigdata52589.2021.9672020

Cited by 9 publications

(2 citation statements)

References 24 publications

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“…Many research areas have developed their own ontologies (e. g. ontologies for agriculture, disease, biomedical investigation, and genes). In agriculture, ontology can be applied for soil-and seed-selection processes; for disease control and treatment; or for smart farming (Visutsak 2021;Bonacin et al 2016;Li et al 2013;Bhuyan 2021;Goldstein 2021). The disease ontology semantically integrates disease and medical vocabularies through extensive cross mapping of terms (Kurbatova & Swiers 2021;Schriml 2022).…”

Section: Methodsmentioning

confidence: 99%

Exploitation of an ontology in a semantic web: A case study transferring Thai lichen data into domain ontologies

Khamweera,

Chaloyard,

Klaysood

et al. 2024

ecomont

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More than 100 lichens have been found and identified in Thailand over the last century. Lichens perform useful environmental functions. Normally, they grow on trees, rocks and soils, in various forms and colours. They provide shelter and food for animals and plants. Lichens can also be used as indicators of air pollution, and some are researched for drugs, dyes, deodorants and extracts. This paper presents an ontology for lichens in Thailand, using lichen data from Khao Pluang, in Lopburi, Thailand. The database developed makes major contributions in the amount of knowledge on lichens that it stores, including the latitude and longitude of lichens at Khao Pluang specifically, and images of lichens. The system was implemented via web and mobile applications. The ontology knowledge consists of 6 main classes: 1) scientific name, 2) lichen type, 3) date found, 4) uses of the lichen, 5) function as an air pollution indicator, and 6) specific features of the individual lichen. Evaluation of the system was carried out by lichen and ontology specialists using the Software Usability Measurement Inventory (SUMI).

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Section: Methodsmentioning

confidence: 99%

Exploitation of an ontology in a semantic web: A case study transferring Thai lichen data into domain ontologies

Khamweera,

Chaloyard,

Klaysood

et al. 2024

ecomont

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“…Artificial Intelligence has played a major role in agriculture [14,[20][21][22][23][24][25]. Crop yield is one of the major domains where the tools of AI can be molded for estimation of the same [15,[26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Crop Type Prediction: A Statistical and Machine Learning Approach

et al. 2022

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Farmers’ ability to accurately anticipate crop type is critical to global food production and sustainable smart cities since timely decisions on imports and exports, based on precise forecasts, are crucial to the country’s food security. In India, agriculture and allied sectors constitute the country’s primary source of revenue. Seventy percent of the country’s rural residents are small or marginal agriculture producers. Cereal crops such as rice, wheat, and other pulses make up the bulk of India’s food supply. Regarding cultivation, climate and soil conditions play a vital role. Information is of utmost need in predicting which crop is best suited given the soil and climate. This paper provides a statistical look at the features and indicates the best crop type on the given features in an Indian smart city context. Machine learning algorithms like k-NN, SVM, RF, and GB trees are examined for crop-type prediction. Building an accurate crop forecast system required high accuracy, and the GB tree technique provided that. It outperforms all the classification algorithms with an accuracy of 99.11% and an F1-score of 99.20%.

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