Coffee Bean Inspection Machine with Deep Learning Classification

Micaraseth, Terisara; Pornpipatsakul, Khemwutta; Chancharoen, Ratchatin; Phanomchoeng, Gridsada

doi:10.1109/iceccme55909.2022.9987835

“…Methodology Results [25] The study created an inspection machine to classify defects in coffee beans.…”

Section: Reference Objectives and Scenario Of Applicationmentioning

confidence: 99%

“…The authors of [34] created a high-speed near-infrared (NIR) camera to capture images of coffee beans moving on a conveyor belt. [25] also proposed a conveyor for capturing and uploading images.…”

Section: [] [] [] [] []mentioning

confidence: 99%

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

de C. Motta,

Vuillerme,

Pham

et al. 2024

Preprint

0

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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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“…CNN and its derived methods can directly use images as inputs to train the deep learning model for classification applications, and it has become very popular in coffee-related applications. For example, the CNN-based or derived models have been successfully applied in coffee bean inspection machines [38], coffee bean quality screening [13], coffee bean defects [39,40], coffee leaf disease classification using ResNet50 [41] and MobileNetV3 [42], coffee maturity classification [43], and roasting coffee bean quality assessment using Mo-bileNetV2 [44]. A good comparison of CNN-derived models is available in [44].…”

Section: Introductionmentioning

confidence: 99%

A Remote Access Server with Chatbot User Interface for Coffee Grinder Burr Wear Level Assessment Based on Imaging Granule Analysis and Deep Learning Techniques

Chen,

Lin,

Tseng

et al. 2024

Applied Sciences

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Coffee chains are very popular around the world. Because overly worn coffee grinder burrs can downgrade the taste of coffee, coffee experts and professional cuppers in an anonymous coffee chain have developed a manual method to classify coffee grinder burr wear so that worn burrs can be replaced in time to maintain the good taste of coffee. In this paper, a remote access server system that can mimic the ability of those recognized coffee experts and professional cuppers to classify coffee grinder burr wear has been developed. Users only need to first upload a photo of coffee granules ground by a grinder to the system through a chatbot interface; then, they can receive the burr wear classification result from the remote server in a minute. The system first uses image processing to obtain the coffee granules’ size distribution. Based on the size distributions, unified length data inputs are then obtained to train and test the deep learning model so that it can classify the burr wear level into initial wear, normal wear, and severe wear with more than 96% accuracy. As only a mobile phone is needed to use this service, the proposed system is very suitable for both coffee chains and coffee lovers.

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Machine learning techniques for coffee classification: a comprehensive review of scientific research

Motta,

Vuillerme,

Pham

et al. 2024

Artif Intell Rev

1

0

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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. The motivation for the literature review is to address the increasing global demands and evolving scales of grain production, particularly in coffee farming, by critically reevaluating existing paradigms and integrating AI techniques. This review aims to synthesize and summarize how ML has been utilized to detect and classify various characteristics of coffee beans and leaves, thereby highlighting the potential of AI to revolutionize coffee farming by enhancing production efficiency, minimizing costs, and improving decisionmaking accuracy. This article presents the latest studies in ML in the coffee area, observes the methodology used, and allows researchers to develop new solutions that cover gaps in the literature, open problems, challenges, and future trends, bringing a real contribution to the scientific field. Finally, this article gathers and presents the databases used in many studies, which may be useful for future ML projects.

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Coffee Bean Inspection Machine with Deep Learning Classification

Cited by 7 publications

References 14 publications

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

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

A Remote Access Server with Chatbot User Interface for Coffee Grinder Burr Wear Level Assessment Based on Imaging Granule Analysis and Deep Learning Techniques

Machine learning techniques for coffee classification: a comprehensive review of scientific research

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