Use of data mining and spectral profiles to differentiate condition after harvest of coffee plants

Lamparelli, Rubens Augusto Camargo; Johann, Jerry Adriani; Santos, Éder Ribeiro; Esquerdo, J. C. D. M.; Rocha, Jansle Vieira

doi:10.1590/s0100-69162012000100019

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…Additionally, these data have been used to monitor mine waste [41], estimate foliar nutrition [42] and photosynthetic activity [43], and assess fire danger and post-fire effects [44,45]. Agricultural studies using Hyperion data include classification of crop residue [46,47], crops and crop varieties [48,49], crop planting area [50], and crop conditions after harvest [51]. These data have also been used to assess tillage intensity [47], and crop biophysical and biochemical characteristics [8,[52][53][54][55].…”

Section: Introductionmentioning

confidence: 99%

Accuracies Achieved in Classifying Five Leading World Crop Types and their Growth Stages Using Optimal Earth Observing-1 Hyperion Hyperspectral Narrowbands on Google Earth Engine

Aneece

Thenkabail

2018

Remote Sensing

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As the global population increases, we face increasing demand for food and nutrition. Remote sensing can help monitor food availability to assess global food security rapidly and accurately enough to inform decision-making. However, advances in remote sensing technology are still often limited to multispectral broadband sensors. Although these sensors have many applications, they can be limited in studying agricultural crop characteristics such as differentiating crop types and their growth stages with a high degree of accuracy and detail. In contrast, hyperspectral data contain continuous narrowbands that provide data in terms of spectral signatures rather than a few data points along the spectrum, and hence can help advance the study of crop characteristics. To better understand and advance this idea, we conducted a detailed study of five leading world crops (corn, soybean, winter wheat, rice, and cotton) that occupy 75% and 54% of principal crop areas in the United States and the world respectively. The study was conducted in seven agroecological zones of the United States using 99 Earth Observing-1 (EO-1) Hyperion hyperspectral images from 2008–2015 at 30 m resolution. The authors first developed a first-of-its-kind comprehensive Hyperion-derived Hyperspectral Imaging Spectral Library of Agricultural crops (HISA) of these crops in the US based on USDA Cropland Data Layer (CDL) reference data. Principal Component Analysis was used to eliminate redundant bands by using factor loadings to determine which bands most influenced the first few principal components. This resulted in the establishment of 30 optimal hyperspectral narrowbands (OHNBs) for the study of agricultural crops. The rest of the 242 Hyperion HNBs were redundant, uncalibrated, or noisy. Crop types and crop growth stages were classified using linear discriminant analysis (LDA) and support vector machines (SVM) in the Google Earth Engine cloud computing platform using the 30 optimal HNBs (OHNBs). The best overall accuracies were between 75% to 95% in classifying crop types and their growth stages, which were achieved using 15–20 HNBs in the majority of cases. However, in complex cases (e.g., 4 or more crops in a Hyperion image) 25–30 HNBs were required to achieve optimal accuracies. Beyond 25–30 bands, accuracies asymptote. This research makes a significant contribution towards understanding modeling, mapping, and monitoring agricultural crops using data from upcoming hyperspectral satellites, such as NASA’s Surface Biology and Geology mission (formerly HyspIRI mission) and the recently launched HysIS (Indian Hyperspectral Imaging Satellite, 55 bands over 400–950 nm in VNIR and 165 bands over 900–2500 nm in SWIR), and contributions in advancing the building of a novel, first-of-its-kind global hyperspectral imaging spectral-library of agricultural crops (GHISA: www.usgs.gov/WGSC/GHISA).

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Section: Introductionmentioning

confidence: 99%

Accuracies Achieved in Classifying Five Leading World Crop Types and their Growth Stages Using Optimal Earth Observing-1 Hyperion Hyperspectral Narrowbands on Google Earth Engine

Aneece

Thenkabail

2018

Remote Sensing

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“…Use of data mining and spectral profiles to differentiate condition after harvest of coffee plants Lamparelli et al(2012) O Quadro 7 apresenta uma matriz que relaciona as instituições às quais os autores são afiliados e suas áreas de atuação, que serve para consolidar, em outra visão, a distribuição dos trabalhos selecionados. corte.…”

Section: Composição Do Núcleo De Partidaunclassified

Mapeamento de um núcleo de partida de referências em Data Mining a partir de periódicos publicados no Brasil

Silva

Costa

2015

Gest. Prod.

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The main objective of this paper is to establish a mapping of the scientific production published in (2006), as etapas iniciais do KDD, referentes à análise exploratória dos dados que fazem uso de ferramentas estatísticas, não têm recebido a mesma atenção. Além disso, é uma percepção dos autores, a existência de poucos trabalhos na área de Engenharia de Produção, constituindo uma pergunta básica a pesquisar.O presente trabalho tem por objetivo principal estabelecer um mapeamento da produção científica publicada no Brasil e definir um núcleo inicial de referências bibliográficas para desenvolvimento de pesquisas em Data Mining por meio da aplicação do método de Webibliomining. Para tal, está organizado da seguinte maneira: conceitos e definições são apresentados na seção 2; na seção 3, é descrito o método Webibliomining; na seção 4, -metodologia, o método é a aplicado para geração do núcleo de referências em Data Mining e são apresentados os resultados; a seção 5 apresenta as conclusões e considerações acerca do trabalho. Conceitos e definiçõesNesta seção, são descritos os fundamentos teóricos e conceituais, além de citar trabalhos prévios que utilizaram o modelo Webibliomining, que serviram de base para a pesquisa.

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“…LAMPARELLI et al (2012) SANTOS et al (2012) mention that another difficulty in identifying coffee areas lies in the fact that this culture usually grows in mountainous regions, what causes shading and distortions in the spectral response. Classification and interpretation of shaded targets in the image is, thus, hampered, due to reduction or total loss of the spectral response.…”

Section: Introductionmentioning

confidence: 99%

Using fraction images derived from modis data for coffee crop mapping

2014

Self Cite

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Coffee production was closely linked to the economic development of Brazil and, even today, coffee is an important product of the national agriculture. The State of Minas Gerais currently accounts for 52% of the whole coffee area in Brazil. Remote sensing data can provide information for monitoring and mapping of coffee crops, faster and cheaper than conventional methods. In this context, the objective of this study was to assess the effectiveness of coffee crop mapping in Monte Santo de Minas municipality, Minas Gerais State, Brazil, from fraction images derived from MODIS data, in both dry and rainy seasons. The Spectral Linear Mixing Model was used to derive fraction images of soil, coffee, and water/shade. These fraction images served as input data for the supervised automatic classification using the SVM -Support Vector Machine approach. The best results concerning Overall Accuracy and Kappa Index were obtained in the classification of the dry season, with 67% and 0.41, respectively. KEYWORDS: spectral linear mixing model; supervised classification; Overall Accuracy; Kappa Index. UTILIZAÇÃO DE IMAGENS-FRAÇÃO DERIVADAS DO SENSOR MODIS PARA O MAPEAMENTO DE LAVOURAS DE CAFÉ

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Use of data mining and spectral profiles to differentiate condition after harvest of coffee plants

Cited by 5 publications

References 8 publications

Accuracies Achieved in Classifying Five Leading World Crop Types and their Growth Stages Using Optimal Earth Observing-1 Hyperion Hyperspectral Narrowbands on Google Earth Engine

Accuracies Achieved in Classifying Five Leading World Crop Types and their Growth Stages Using Optimal Earth Observing-1 Hyperion Hyperspectral Narrowbands on Google Earth Engine

Mapeamento de um núcleo de partida de referências em Data Mining a partir de periódicos publicados no Brasil

Using fraction images derived from modis data for coffee crop mapping

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