Crop Selection Using Fuzzy Logic-Based Expert System

Kapoor, Aveksha; Verma, Anil Kumar

doi:10.1007/978-981-10-7098-3_8

Cited by 6 publications

(3 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…No performance metric was used. [41] In this study, a FL-based expert system is proposed to automate the CS for farmers based on parameters, such as, the climatic and soil conditions.…”

Section: Ann and Fuzzy Logic (Fl)mentioning

confidence: 99%

Machine Learning Based Recommender Systems for Crop Selection: A Systematic Literature Review

Ommane,

Rhanbouri,

Chouikh

et al. 2023

Studies in Computational Intelligence

View full text Add to dashboard Cite

This paper draws a systematic literature review about the use of Machine learning based recommender systems for crop selection, with respect to the PRISMA protocol for systematic reviews. The second section, describes an overview of existing recommender systems in literature. The outline of this study is explained, as well as the method of content analysis used in this article to sort out the papers is introduced in the third section. In the fourth section, the selection process and the literature review matrix are provided. Additionally, the evolvement of research on crop recommendation over the years is considered, a detailed study of the main input features is done. Further, the current challenges found in crop recommendation are listed. The fth section discusses the outcomes of the study. The last section recapitulates the outcomes of the study, shedding the light of research perspectives worthwhile pursuing.

show abstract

“…No performance metric was used. [41] In this study, a FL-based expert system is proposed to automate the CS for farmers based on parameters, such as, the climatic and soil conditions.…”

Section: Ann and Fuzzy Logic (Fl)mentioning

confidence: 99%

Machine Learning Based Recommender Systems for Crop Selection: A Systematic Literature Review

Ommane,

Rhanbouri,

Chouikh

et al. 2023

Studies in Computational Intelligence

View full text Add to dashboard Cite

show abstract

“…Reference Year Contribution Dataset [41] In this study, a FL-based expert system is proposed to auto-mate the CS for farmers based on parameters, such as, the climatic and soil conditions.…”

Section: Features: Cropmentioning

confidence: 99%

“…WCs 20 [32,33,35,36,37,40,41,42,39,50,54,57,58,64,71,43,60,67,44,45] SP 19 [32,35,36,37,38,40,41,42,39,50,57,69,68,71,43,65,66,60,45] Geography 17 [35,33,36,37,38,40,41,42,39,50,54,69,71,65,70,67,…”

Section: Feature Class Number Of Studies Referencesmentioning

confidence: 99%

Machine Learning based Recommender Systems for Crop Selection: A Systematic Literature Review

Ommane

RHANBOURI

CHOUIKH

et al. 2022

Preprint

View full text Add to dashboard Cite

Crop selection (CS) is one of the most critical elements that affects the final yield directly. As a result, selecting an appropriate crop is always a critical decision that a farmer must make, considering environmental factors. Choosing an appropriate crop for a given farm is a difficult decision including a plethora of variables that influence the final yield. Experts are frequently consulted to assist farmers with CS; but, as this alternative is time consuming and expensive, it is not available to many farms. The use of recommender systems (RSs) in agricultural management has recently brought some captivating and promising results. We propose a systematic literature review (SLR) in this article to find and provide the most relevant and high-quality publications ad- dressing the crop recommendation (CR) question. The core concept of this SLR is inspired from the guidelines of PRISMA 2020.The different CR approaches are discussed, as well as all the most important input features for recommendation, which are determined and classified. We also identified some of the biggest hurdles to using CR in agriculture. Besides, we made an inventory of the most used techniques for CR. Further, we made an inventory of evaluation criteria and evaluation approaches.

show abstract

Agricultural land suitability classification and crop suggestion using machine learning and spatial multicriteria decision analysis in semi-arid ecosystem

Agrawal,

Govil,

Kumar

2024

Environ Dev Sustain

View full text Add to dashboard Cite

Crop Selection Using Fuzzy Logic-Based Expert System

Cited by 6 publications

References 9 publications

Machine Learning Based Recommender Systems for Crop Selection: A Systematic Literature Review

Machine Learning Based Recommender Systems for Crop Selection: A Systematic Literature Review

Machine Learning based Recommender Systems for Crop Selection: A Systematic Literature Review

Agricultural land suitability classification and crop suggestion using machine learning and spatial multicriteria decision analysis in semi-arid ecosystem

Contact Info

Product

Resources

About