Using fraction images derived from modis data for coffee crop mapping

Bispo, Rafael Carlos; Lamparelli, Rubens Augusto Camargo; Rocha, Jansle Vieira

doi:10.1590/s0100-69162014000100012

Cited by 3 publications

(1 citation statement)

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“…This study employs a mapping spatial analysis to explain the total variability. Reference [56], using MODIS sensor, evaluated the effectiveness of coffee mapping in Brazil, through the images of the sensor. Reference [57] considered a Mandani-Fuzzy logic model as a tool for monitoring and selecting, at the industrial level, an appropriated dry mill process in coffee production in Veracruz.…”

Section: Introductionmentioning

confidence: 99%

Technology Roadmapping Architecture Based on Knowledge Management: Case Study for Improved Indigenous Coffee Production from Guerrero, Mexico

et al. 2019

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Having demonstrated its importance for economic and human developments, technological change is considered one of the biggest supports worldwide. Today, with the population increase, the inclusion of technology is considered the most appropriate way to reduce the impact of this challenge. Considering 171 indigenous coffee producers from Paraje Montero and Tierra Colorada from Guerrero, Mexico, the present study proposes technological routes for the indigenous coffee production chain and the inclusion of emerging technologies such as sensors, actuators, and processing devices basing on the requirements of the chain. During face-to-face sessions, questionaries, field visits, and literature review for knowledge management, the results expose the need of effective actions against diseases and the reduction of climate change, lack of infrastructure, old plantations, and lack of commercialization channels; for these and considering the requirement of greenhouse technology by indigenous coffee producers and following the labor value driver of the digital compass, the necessity of several kinds of sensors, technologies, and methodologies has been identified with high possibilities to be implemented by similar producers analyzed in this manuscript to help in solving the problems identified in this work. The combination of remote sensing, signal processing, and spectroscopy could be employed to explore mineralogical features of soil and help problems with fertilization; sensor modules to collect temperature, humidity, and light intensity data are a priority for greenhouse monitoring; electrochemical sensors and optical technologies could be of great help to detect the presence of diseases in coffee plants; and the installation of a greenhouse solar dryer is necessary to reduce the time of the sun drying process and protect the coffee cherry. These emerging technologies will help to improve the production of coffee. The study contributes to identify a technology roadmap proposing technological implements according to indigenous coffee production chain requirements and serves as support for future studies in indigenous regions.

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Section: Introductionmentioning

confidence: 99%

Technology Roadmapping Architecture Based on Knowledge Management: Case Study for Improved Indigenous Coffee Production from Guerrero, Mexico

et al. 2019

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The use of machine learning in digital processing of satellite images applied to coffee crop

Miranda

Alves

2020

CABI Reviews

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Remote sensing can be used to monitor and estimate, with reasonable correct answers, the yield, plant health, and coffee nutrition. Satellite-coupled sensors can obtain information about the spectral signature of the crop, on a time scale, in order to monitor and detect phenological changes. However, the accumulation of data obtained by orbital sensors makes it difficult to understand the relationship between the aspects of coffee. Thus, machine learning can perform data mining and meet the spectral signature patterns that constitute coffee behavior. This literature review sought the survey of research that used machine learning tools applied in digital image processing from satellites for coffee crop monitoring.

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Review of Remote Sensing Methods to Map Coffee Production Systems

et al. 2020

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The coffee sector is working towards sector-wide commitments for sustainable production. Yet, knowledge of where coffee is cultivated and its environmental impact remains limited, in part due to the challenges of mapping coffee using satellite remote sensing. We recognize the urgency to capitalize on recent technological advances to improve remote sensing methods and generate more accurate, reliable, and scalable approaches to coffee mapping. In this study, we provide a systematic review of satellite-based approaches to mapping coffee extent, which produced 43 articles in the peer-reviewed and gray literature. We outline key considerations for employing effective approaches, focused on the need to balance data affordability and quality, classification complexity and accuracy, and generalizability and site-specificity. We discuss research opportunities for improved approaches by leveraging the recent expansion of diverse satellite sensors and constellations, optical/Synthetic Aperture Radar data fusion approaches, and advances in cloud computing and deep learning algorithms. We highlight the need for differentiating between production systems and the need for research in important coffee-growing geographies. By reviewing the range of techniques successfully used to map coffee extent, we provide technical recommendations and future directions to enable accurate and scalable coffee maps.

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Using fraction images derived from modis data for coffee crop mapping

Cited by 3 publications

References 9 publications

Technology Roadmapping Architecture Based on Knowledge Management: Case Study for Improved Indigenous Coffee Production from Guerrero, Mexico

Technology Roadmapping Architecture Based on Knowledge Management: Case Study for Improved Indigenous Coffee Production from Guerrero, Mexico

The use of machine learning in digital processing of satellite images applied to coffee crop

Review of Remote Sensing Methods to Map Coffee Production Systems

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