Automatic Rice Variety Identification System: state-of-the-art review, issues, challenges and future directions

Komal, .; Sethi, Ganesh Kumar; Bawa, Rajesh K.

doi:10.1007/s11042-023-14487-x

Cited by 4 publications

(2 citation statements)

References 47 publications

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“…The study of [9] analyzes technologies for detecting roast-level coffee beans, but not all studies use ML techniques. Many studies related to smart agriculture [10,11] address machine learning techniques for classifications of other types of crops, such as rice [12][13][14][15], corn [16,17] and soybean [18]. A Comprehensive Review was used to achieve the proposed objective of synthesizing and understanding how ML techniques for coffee classification are presented in scientific research.…”

Section: Of 34mentioning

confidence: 99%

Machine Learning Techniques for Coffee Classification: A Comprehensive Review of Scientific Research

de C. Motta,

Vuillerme,

Pham

et al. 2024

Preprint

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In the realm of agribusiness, transformative shifts are underway, propelled by the growing demands and expanding scales of grain production. This evolution calls for a critical reevaluation of the existing paradigms in coffee production and marketing paradigms, with a specific focus on integrating Artificial Intelligence (AI). This work aims to review, synthesize, and summarize the available data regarding how Machine Learning (ML) has been used to detect and classify characteristics in coffee beans and leaves. For this purpose, a comprehensive literature review of the most significant research contributions describing the application of AI for advanced classification techniques in coffee agriculture has been carried out. Our analysis suggests that implementing AI technologies allows the classification of coffee, encompassing various attributes such as maturity, roast intensity, disease identification, flavor profiles, and overall quality. More largely, this technological advancement holds the potential to revolutionize coffee farming by providing producers and agricultural specialists with sophisticated tools to enhance production efficiency, minimize costs, and improve the accuracy and confidence of their decision-making processes.

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Section: Of 34mentioning

confidence: 99%

Machine Learning Techniques for Coffee Classification: A Comprehensive Review of Scientific Research

de C. Motta,

Vuillerme,

Pham

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Identification of rice varieties primarily relies on their distinctive attributes such as shape, color, and texture. However, existing image datasets in this domain suffer from limitations [3] . Some datasets lack an adequate number of rice variants, while others may have abundant data but lack variation in terms of rice characteristics like shape, color, and texture.…”

Section: Introductionmentioning

confidence: 99%