Digital mapping of coffee ripeness using UAV-based multispectral imagery

Martins, Rodrigo Nogueira; Pinto, Francisco de Assis de Carvalho; Queiroz, Daniel Marçal de; Valente, Domingos Sárvio Magalhães; Rosas, Jorge Tadeu Fim; Portes, Marcelo Fagundes; Cerqueira, Elder Sânzio Aguiar

doi:10.1016/j.compag.2022.107499

Cited by 9 publications

(4 citation statements)

References 29 publications

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“…The ResNet50 model performed best and achieved an accuracy of 99.01%. [63] The study presents a method to monitor the ripeness of coffee fruits using multispectral images obtained by Unmanned Aerial Vehicles (UAV).…”

Section: Reference Objectives and Scenario Of Applicationmentioning

confidence: 99%

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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: Reference Objectives and Scenario Of Applicationmentioning

confidence: 99%

“…[61] classified coffee cherries based on skin color and shape characteristics. [63] classified coffee cherries based on spectral and textural characteristics.…”

Section: Reference Objectives and Scenario Of Applicationmentioning

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

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“…Las crecientes resoluciones temporales y espaciales de la teledetección basadas en drones volando a baja altitud, proporcionan una forma factible de monitorear la variabilidad de la madurez de las bayas en plantas de café. Debido a eso Martins et al (2023) realizaron un estudio que tuvo como dos objetivos: (1) predecir la madurez de la fruta usando variables espectrales y texturales; y (2) determinar las mejores variables para desarrollar mapas de variabilidad espacio-temporal de la madurez de la fruta. Este trabajo demostró la viabilidad de utilizar variables espectrales y texturales derivadas de imágenes de drones para mapear y monitorear los cambios espacio-temporales en la madurez de la fruta.…”

Section: Mejoran La Resolución Temporal Y Espacialunclassified

Uso de drones en la agricultura de precisión

Lira-Saldivar,

Lara-Viveros

2023

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Editora Direitos para esta edição cedidos à Atena Editora pelos autores. Open access publication by Atena Editora Todo o conteúdo deste livro está licenciado sob uma Licença de Atribuição Creative Commons. Atribuição-Não-Comercial-NãoDerivativos 4.0 Internacional (CC BY-NC-ND 4.0). O conteúdo do texto e seus dados em sua forma, correção e confiabilidade são de responsabilidade exclusiva dos autores, inclusive não representam necessariamente a posição oficial da Atena Editora. Permitido o download da obra e o compartilhamento desde que sejam atribuídos créditos aos autores, mas sem a possibilidade de alterála de nenhuma forma ou utilizá-la para fins comerciais.Todos os manuscritos foram previamente submetidos à avaliação cega pelos pares, membros do Conselho Editorial desta Editora, tendo sido aprovados para a publicação com base em critérios de neutralidade e imparcialidade acadêmica.A Atena Editora é comprometida em garantir a integridade editorial em todas as etapas do processo de publicação, evitando plágio, dados ou resultados fraudulentos e impedindo que interesses financeiros comprometam os padrões éticos da publicação. Situações suspeitas de má conduta científica serão investigadas sob o mais alto padrão de rigor acadêmico e ético. Conselho Editorial Ciências Agrárias e Multidisciplinar

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“…Consequently, coffee production in these areas is adversely affected, resulting in lower yield levels. Despite these challenges, the demand for coffee continues to increase each year [8,9].…”

Section: Introductionmentioning

confidence: 99%

Robust Coffee Rust Detection Using UAV-Based Aerial RGB Imagery

Rodriguez-Gallo,

Escobar-Benitez,

Rodriguez-Lainez

2023

AgriEngineering

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Timely detection of pests and diseases in crops is essential to mitigate severe damage and economic losses, especially in the context of climate change. This paper describes a method for detecting the presence of coffee leaf rust (CLR) using two databases: RoCoLe and a database obtained from an unmanned aerial vehicle (UAV) equipped with an RGB camera. The developed method follows a two-stage approach. In the first stage, images are processed using ImageJ software, while, in the second phase, Python is used to implement morphological filters and the Hough transform for rust identification. The algorithm’s performance is evaluated using the chi-square test, and its discriminatory capacity is assessed through the generation of a Receiver Operating Characteristic (ROC) curve. Additionally, Cohen’s kappa method is used to assess the agreement among observers, while Kendall’s rank correlation coefficient (KRCC) measures the correlation between the criteria of the observers and the classifications generated by the method. The results demonstrate that the developed method achieved an efficiency of 97% in detecting coffee rust in the RoCoLe dataset and over 93.5% in UAV images. These findings suggest that the developed method has the potential to be implemented in the future on a UAV for rust detection.

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Digital mapping of coffee ripeness using UAV-based multispectral imagery

Cited by 9 publications

References 29 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

Uso de drones en la agricultura de precisión

Robust Coffee Rust Detection Using UAV-Based Aerial RGB Imagery

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