Data Mining Analysis for Precision Agriculture: A Comprehensive Survey

Aishwarya, K; Ma, Jie

doi:10.1149/10701.17769ecst

Cited by 3 publications

(2 citation statements)

References 29 publications

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“…Crop management, precision agriculture, etc. [174] Crop water requirement prediction, uncertainty analysis [175] Nutrient requirement prediction, uncertainty analysis [176] Crop rotation modeling, decision support [177] Farm risk assessment, decision analysis [178,179] Boosting…”

Section: Algorithm Functionality (Descriptive Analytics) Application ...mentioning

confidence: 99%

Global Application of Regenerative Agriculture: A Review of Definitions and Assessment Approaches

Jayasinghe,

Thomas,

Anderson

et al. 2023

Sustainability

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Regenerative agriculture (RA) is an approach to farming pursued globally for sustaining agricultural production and improving ecosystem services and environmental benefits. However, the lack of a standardized definition and limited bioeconomic assessments hinder the understanding and application of RA more broadly. An initial systematic review revealed a wide range of definitions for regenerative agriculture, although it is generally understood as a framework consisting of principles, practices, or outcomes aimed at improving soil health, biodiversity, climate resilience, and ecosystem function. To address existing gaps, we propose a working definition that integrates socioeconomic outcomes and acknowledges the significance of local knowledge and context to complement established scientific knowledge. A second systematic review identified indicators, tools, and models for assessing biophysical and economic aspects of RA. Additionally, a third literature review aimed to identify the potential integration of advanced analytical methods into future assessments, including artificial intelligence and machine learning. Finally, as a case study, we developed a conceptual framework for the evaluation of the bioeconomic outcomes of RA in the mixed farming setting in Australia. This framework advocates a transdisciplinary approach, promoting a comprehensive assessment of RA outcomes through collaboration, integrated data, holistic frameworks, and stakeholder engagement. By defining, evaluating assessment methods, and proposing a pragmatic framework, this review advances the understanding of RA and guides future research to assess the fit of RA practices to defined contexts.

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Section: Algorithm Functionality (Descriptive Analytics) Application ...mentioning

confidence: 99%

Global Application of Regenerative Agriculture: A Review of Definitions and Assessment Approaches

Jayasinghe,

Thomas,

Anderson

et al. 2023

Sustainability

View full text Add to dashboard Cite

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“…The application of data mining techniques in agriculture has gained increasing attention in recent years [11], [12]. Data mining refers to the process of extracting useful information and patterns from large datasets [13].…”

Section: Data Mining and Open Data Malaysia In Agriculturementioning

confidence: 99%

Modelling of Ladyfinger Plantations Using Open Data Malaysia: A Comparative Study of Machine Learning Techniques

Mohamad Mohsin,

Hassan,

Mohd Sharif

et al. 2023

EmAIT

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This study investigates the potential of using open data from Open Data Malaysia to develop classification models for agricultural practices specifically focusing on ladyfinger plantations. The integration of climate data with agricultural data is performed to build predictive models for crop yield prediction for sustainable agriculture. Four machine learning models, namely Naïve Bayes, SVM, KNN, and decision tree, are evaluated based on various performance metrics. The ladyfinger dataset was obtained from Open Data Malaysia containing both climate and agricultural data. was preprocessed and mined. The results indicate that the Naïve Bayes model achieves the highest performance making it the most suitable model for predicting ladyfinger yield. The decision tree model performed poorly and may not be suitable for this type of classification task. This study highlights two important findings. Firtstly, the inclusion of climate data significantly improved the classification performance of the models. Secondly the limited size of the ladyfinger dataset emphasizes the need for larger and more diverse datasets to enhance the accuracy and generalizability of predictive models in agriculture. Open data initiatives are important for providing researchers with data however larger and more diverse datasets are needed to improve model accuracy. Future research could investigate machine learning models for predicting crop yields in different crops with various climate and agricultural data combinations.

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A survey of data mining methodologies in the environment of IoT and its variants

Marshoodulla,

Saha

2024

Journal of Network and Computer Applications

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Data Mining Analysis for Precision Agriculture: A Comprehensive Survey

Cited by 3 publications

References 29 publications

Global Application of Regenerative Agriculture: A Review of Definitions and Assessment Approaches

Global Application of Regenerative Agriculture: A Review of Definitions and Assessment Approaches

Modelling of Ladyfinger Plantations Using Open Data Malaysia: A Comparative Study of Machine Learning Techniques

A survey of data mining methodologies in the environment of IoT and its variants

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