Comparison of Clustering Algorithms Using Quality Metrics with Invariant Features Extracted from Plant Leaves

Majumdar, Jharna; Ankalaki, Shilpa

doi:10.1166/asl.2017.10253

Cited by 5 publications

(4 citation statements)

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“…To ensure fairer conditions in evaluation, this work finds the optimal clustering method for agriculture data analysis. Proposed work adopts the external quality metrics [3] like Purity, Homogeneity, Completeness, V Measure, Rand Index, Precision, Recall and F measure to compare the PAM, CLARA and DBSCAN clustering methods.…”

Section: Evaluation Methodsmentioning

confidence: 99%

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Analysis of agriculture data using data mining techniques: application of big data

Majumdar¹,

Naraseeyappa²,

Ankalaki³

2017

J Big Data

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190

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Section: Evaluation Methodsmentioning

confidence: 99%

“…Cluster analysis divides data into well-formed groups. Well-formed clusters should capture the "natural" structure of the data [3]. This paper focuses on PAM, CLARA and DBSCAN clustering methods.…”

mentioning

confidence: 99%

Analysis of agriculture data using data mining techniques: application of big data

Majumdar¹,

Naraseeyappa²,

Ankalaki³

2017

J Big Data

Self Cite

190

View full text Add to dashboard Cite

“…This is helpful to government in framing policies related to crops such as crop insurance policies, supply chain operation policies. Knowing what crops has been grown, and how much area of it had been shown historically, combined with the prices at which it could have been sold at the nearest market-place provides the income-growth profile of the farmer [3].…”

mentioning

confidence: 99%

“…The analysis of the cluster divides data into well organized groups. The natural structure of the data is captured by these well-formed groups [3,4].…”

mentioning

confidence: 99%

Agriculture Data Analytics in Crop Yield Estimation: A Critical Review

Sagar¹,

Cauvery²

2018

IJEECS

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<p>Agriculture is important for human survival because it serves the basic need. A well-known fact that the majority of population (≥55%) in India is into agriculture. Due to variations in climatic conditions, there exist bottlenecks for increasing the crop production in India. It has become challenging task to achieve desired targets in Agri based crop yield. Factors like climate, geographical conditions, economic and political conditions are to be considered which have direct impact on the production, productivity of the crops. Crop yield prediction is one of the important factors in agriculture practices. Farmers need information regarding crop yield before sowing seeds in their fields to achieve enhanced crop yield. The use of technology in agriculture has increased in recent year and data analytics is one such trend that has penetrated into the agriculture field being used for management of crop yield and monitoring crop health. The recent trends in the domain of agriculture have made the people to understand the significance of Big data. The main challenge using big data in agriculture is identification of impact and effectiveness of big data analytics. Efforts are going on to understand how big data analytics can be used to improve the productivity in agricultural practices. The analysis of data related to agriculture helps in crop yield prediction, crop health monitoring and other such related activities. In literature, there exist several studies related to the use of data analytics in the agriculture domain. The present study gives insights on various data analytics methods applied to crop yield prediction. The work also signifies the important lacunae points’ in the proposed area of research.</p>

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