Data Mining Techniques for Rainfall Regionalization in Parana State

The prevalence of agro-meteorological data for specific regions serve as parameters for agricultural and related climate studies. This study aims to regionalize the rainfall in the State of Paraná (Southern Brazil) through data mining techniques with ECMWF (European Centre for Medium Range Weather Forecasts) data from 1989 to 2013. The algorithms k-means and Simple EM (Expectation Maximization) for clustering were applied in Weka software, version 3.6. The quality of the clustering was determined with the J48 classification algorithm applied using training set. The decision tree presents similarity indexes and errors measures to determine the best number of cluster for this case. As results 6 regions of homogeneous rainfall in the state of Paraná were presented

ALGORITMOS DE APRENDIZADO DE MÁQUINA PARA CLASSIFICAÇÃO DE SOJA EM IMAGENS LANDSAT-8

O estado do Paraná ocupa uma posição importante no cenário nacional na produção de soja, sendo responsável por mais de 18% da produção brasileira, produzindo mais do que o quarto maior produtor mundial, a China. Para monitorar a produção agrícola, a informação de área é fundamental neste processo. Diversas técnicas e métodos podem ser empregados, incluindo algoritmos de Aprendizado de Máquina (Machine Learning). Logo, este trabalho tem como objetivo comparar quatro técnicas de aprendizado de máquina para mapear a área de soja a partir de imagens do sensor Landsat-8 no estado do Paraná, durante a safra de verão 2013/2014. Os algoritmos empregados no processo foram o Random Forest (RF), Model Averaged Neural Network (MANet), Classification and Regression Trees (CART) e o Extreme Learning Machine (ELM). Todos receberam os mesmos dados de treinamento (38 áreas de soja e 28 áreas não-soja) e as bandas de 1 a 7 do sensor Landsat-8 no tile 223/77 do dia 18/12/2013. A avaliação da acurácia (Exatidão Global e índice Kappa) foi realizada para cada classificação, sendo que técnica MANet apresentou os melhores resultados (Kappa = 0,9993 e EG = 0,9505) e o ELM o pior (Kappa = 0,9980 e EG = 0,8855)

Potential for machine vision of grain crop features for nitrogen assessment

Existing approaches for determining nitrogen (N) requirements typically involve measuring biomass and sensing near-infrared-based crop reflectance indices. There is potential for automated assessments of tiller counts, plant size and colour using machine vision to help indicate plant N status. Existing demonstrations of machine vision systems are typically for a single field rather than multiple fields. A barley and wheat field study has been conducted to identify robustness of machine vision across multiple sites for assessing biomass, and plant N status and concentration. Three N trial sites were established in Western Australia and South Australia during the 2020 season with low and rich-N strips. Each strip and the paddock were sampled in five to seven locations for plant N uptake, plant N concentration, and plant response using crop dry biomass and machine vision cameras. Machine vision algorithms were implemented on oblique images to extract indicators of vigour (colour) and physical size (line length and density that represent tillers and branches). Linear regression analysis identified that a normalised green red difference index from the colour machine vision system was strongly correlated with biomass and could add value to biomass and plant N assessment. Further work is to incorporate machine vision parameters into a data-driven N decision making method

Three Decades after: Landscape Dynamics in Different Colonisation Models Implemented in the Brazilian Legal Amazon

Several colonisation projects were implemented in the Brazilian Legal Amazon in the 1970s and 1980s. Among these colonisation projects, the most prominent were those with the “fishbone” and “topographic” models. Within this scope, the settlements known as Anari and Machadinho stand out because they are contiguous areas with different models and structures of occupation and colonisation. The main objective of this work was to evaluate the dynamics of Land-Use and Land-Cover (LULC) in two different colonisation models, implanted in the State of Rondônia in the 1980s. The fishbone and topographic or Disorganised Multidirectional models were implemented in the Anari and Machadinho settlements, respectively. A 36-year time series of Landsat images (1984–2020) was used to evaluate the rates and trends in the LULC process in the different colonisation models. In the analysed models, a rapid loss of primary and secondary forests (anthropized areas) was observed, mainly due to the dynamics of its use, established by the Agriculture/Pasture relation with a heavy dependence on road construction. Understanding these two forms of occupation can help the future programs and guidelines of the Brazilian Legal Amazon and any tropical rainforest across the globe

Mapping summer soybean and corn with remote sensing on Google Earth Engine cloud computing in Parana state – Brazil

Brazilian farming influences directly the worldwide economy. Thus, fast and reliable information on areas sown with the main crops is essential for planning logistics and public or private commodity market policies. Recent farming practices have embraced remote sensing to provide fast and reliable information on commodity dynamics. Medium-to-low resolution free orbital images, such as those from Landsat 8 and Sentinel 2, have been used for crop mapping; however, satellite image processing requires high computing power, especially when monitoring vast areas. Therefore, cloud data processing has been the only feasible option to deal with a large amount of orbital data and its processing and analysis. Thus, our goal was to develop a method to map the two main crops (soybeans and corn) in Paraná, one of the major Brazilian state producers. Landsat-8, Sentinel-2, SRTM+, and field data from 2016 to 2018 were used with the Simple Non-Iterative Clustering segmentation method and the Continuous Naive Bayes classifier, to identify cropped areas. A minimum global accuracy of 90% was found for both crops. Comparison with field data showed correlations of 0.96 and agreement coefficients no lower than 0.86. This ensures mapping quality when using Sentinel and/or Landsat imagery on the GEE platform

Estimating Crop Sowing and Harvesting Dates Using Satellite Vegetation Index: A Comparative Analysis

In the last decades, several methodologies for estimating crop phenology based on remote sensing data have been developed and used to create different algorithms. Although many studies have been conducted to evaluate the different methodologies, a comprehensive understanding of the potential of the different current algorithms to detect changes in the growing season is still lacking, especially in large regions and with more than one crop per season. Therefore, this work aimed to evaluate different phenological metrics extraction methodologies. Using data from over 1500 fields distributed across Brazil’s central area, six algorithms, including CropPhenology, Digital Earth Australia tools package (DEA), greenbrown, phenex, phenofit, and TIMESAT, to extract soybean crop phenology were applied. To understand how robust the algorithms are to different input sources, the NDVI and EVI2 time series derived from MODIS products (MOD13Q1 and MOD09Q1) and from Sentinel-2 satellites were used to estimate the sowing date (SD) and harvest date (HD) in each field. The algorithms produced significantly different phenological date estimates, with Spearman’s R ranging between 0.26 and 0.82 when comparing sowing and harvesting dates. The best estimates were obtained using TIMESAT and phenex for SD and HD, respectively, with R greater than 0.7 and RMSE of 16–17 days. The DEA tools and greenbrown packages showed higher sensitivity when using different data sources. Double cropping is an added challenge, with no method adequately identifying it

Köppen, Thornthwaite and Camargo climate classifications for climatic zoning in the State of Paraná, Brazil

ABSTRACT Climate is the set of average atmospheric conditions that characterizes a region. It directly influences the majority of human activities, especially agriculture. Climate classification systems (CCSs) are important tools in the study of agriculture, enabling knowledge of the climatic characteristics of a region. Thus, we aimed to perform the climatic characterization of the State of Paraná using the methods proposed by Köppen and Geiger (1928), modified by Trewartha (1954) (KT), Thornthwaite (1948) (TH) and Camargo (1991) and modified by Maluf (2000) (CM), using data from the European Center for Medium-Range Weather Forecast (ECMWF) model. The results of spatial interpolation (virtual stations) were performed using the Kriging method in spherical shape with one neighbour and resolution of 0.25°. The CCSs displayed the ability to separate the warm and dry from cold and wet regions. The most predominant climates were Cfa (temperate humid with hot summers), C1rA'a' (sub-humid with little water deficiency, megathermal) and ST-UMi (humid subtropical with dry winter), according to KT, TH and CM, respectively. CM is an intermediate CCS between KT and TH