Data Mining Techniques for Separation of Summer Crop Based on Satellite Images

Becker, Willyan Ronaldo; Johann, Jerry Adriani; Richetti, Jonathan; Silva, Laíza C. De A.

doi:10.1590/1809-4430-eng.agric.v37n4p750-759/2017

Cited by 8 publications

(8 citation statements)

References 15 publications

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“…Thus, agro-climatic zoning, especially for the second summer crop, recommends early planting in order to avoid risky periods. These results are similar to those obtained by Johann et al (2016) and Becker, Johann, Richetti and Silva (2017). However, they retrieved dates from soybean areas in a municipality level, while this study shows by pixel scale.…”

Section: Introductionsupporting

confidence: 90%

“…Corn harvest varied across the state, like SD and DMVD. Becker et al (2017) also observed similar pattern. This is due to the possibility of maintaining corn in the field after it is ready for harvest, which does not apply to other agricultural crops as it could cause grain loss.…”

Section: Introductionsupporting

confidence: 68%

“…The west region planting first than the other regions and most corn is planted in October. Corroborating with Becker et al (2017) that found that corn SD was concentrated in October, which suggests a temporal preference for corn sowing in this month. The Western and Northwestern mesoregions harvest far ahead of the other regions, followed by the Western Center.…”

Section: Introductionmentioning

confidence: 67%

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Agricultural soybean and corn calendar based on moderate resolution satellite images for southern Brazil

Becker

Richetti

Mercante

et al. 2020

Semina: Ciênc. Agrár.

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Knowledge of the agricultural calendar of crops is essential to better estimate and forecast the cultivation of large-scale crops. The aim of this study was to estimate sowing date (SD), date of maximum vegetative development (DMVD), and harvest date (HD) of soybean and corn in the state of Paraná, Brazil. Dates from 120 farms and the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) from 2011 to 2014 were used into a seasonal trend analysis to obtain soybean and corn seasonal patterns. The results indicate that the majority soybean is sown during October and the DMVD occurs between the second ten-day period of December and the first ten-day period of January. Owing to the spatial variability of the SD, the difference in the maturation cycles of the cultivars, and regional climatic variation, the HD of soybean varied greatly during the studied crop years, ranging from mid-February to late March. The SD of corn is before that of soybean, and mainly occurs in late September to mid-October. The DMVD mainly occurs during December, and the HD is distributed throughout January to March in Paraná. When comparing the estimated dates with observed dates the mean error (ME) varied from 0.2 days earlier to 3.3 days after the observed date for soybean with root mean square error (RMSE) from 1.93 to 14.73 days. For corn, the ME varied from 10.3 days to 18.5 days after the observed date with RMSE from 18.02 to 27.82 days.

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Section: Introductionsupporting

confidence: 90%

Section: Introductionsupporting

confidence: 68%

Section: Introductionmentioning

confidence: 67%

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Agricultural soybean and corn calendar based on moderate resolution satellite images for southern Brazil

Becker

Richetti

Mercante

et al. 2020

Semina: Ciênc. Agrár.

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“…Agrometeorological variables, in the most critical phenological phases of the soybean cycle, were analyzed, that is, in the intervals close to the maximum vegetative development date (MVDD), defined by Becker et al (2017) as the crop's reproductive stages (flowering, pod formation and filling). For the two harvest years, the MVDD was obtained from Becker et al (2020).…”

Section: Determination Of the Study Area And Data Acquisitionmentioning

confidence: 99%

Soybean Productivity and Agrometeorological Variables Assessed from the perspective of Spatial and Circular Statistics

Silva

Gasparin

Paludo

et al. 2021

JAS

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Climate change can affect the development of soybean cultivation, impacting your productivity. Thus, agrometeorological information is essential in order to improve productivity strategies. The objective of the paper was to analyze the influence and occurrence of seasonality of the following agrometeorological variables on soybean productivity: mean air temperature [TMean] (ºC), accumulated rainfall value [Rain] (mm), global solar radiation [Sr] (MJ m-2 day-1), and potential evapotranspiration [ETp] (mm), in ten-day variations of the the maximum vegetative development date (MVDD), in the 2011/2012 and 2013/2014 harvest years in the state of Paraná. The study was based on spatial distribution of variables, using univariate and bivariate Global Moran’s Indexes, and multivariate clustering analysis. To verify seasonality in the time distribution of the agrometeorological variables in the ten-day variations close to soybean MVDD, we used the circular statistics, through the mean vector length (R). Result it was identified regions of the state that have higher and lower rainfall and seasonality, also have higher and lowest productivity, respectively. That the variation in soybean productivity between harvest years was correlated with the agrometeorological variables, and rainfall volume is an important factor in productivity. The other agrometeorological variables occurred uniformly, especially in 2011/2012 harvest year, in the Northwest, Central-northern and West mesoregions. Furthermore, there was clustering of regions with similar spatial distribution of the evapotranspiration and rainfall variables in 2aDMDV2d in the 2011/2012 and 2013/2014 harvest year, showed the same spatial distribution of the agrometeorological variables and the productivity variable.

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“…Thus, new Deep Learning architecture should be tested in order to facilitate and create a streamline for this process in agricultural mapping. One of the options which is gaining recognition in remote sensing is the use of the KDD (Knowledge Discovery in Databases) process that aims to transform a large amount of data into useful information, as reported in several studies (Becker et al 2017;Han and Kamber 2006;Hansen et al 2000;Johann et al 2013;Zhou et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Networks and Data Mining Techniques for Summer Crop Discrimination: A New Approach

Júnior

Shemmer

Johann

et al. 2019

Canadian Journal of Remote Sensing

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Data Mining Techniques for Separation of Summer Crop Based on Satellite Images

Cited by 8 publications

References 15 publications

Agricultural soybean and corn calendar based on moderate resolution satellite images for southern Brazil

Agricultural soybean and corn calendar based on moderate resolution satellite images for southern Brazil

Soybean Productivity and Agrometeorological Variables Assessed from the perspective of Spatial and Circular Statistics

Artificial Neural Networks and Data Mining Techniques for Summer Crop Discrimination: A New Approach

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