Data processing within rows for sugarcane yield mapping

The mapping of sugarcane yield is still not as widely available as it is for grain crops. Sugarcane harvesters cut and process the cane in a single or maximum of two rows, facilitating the monitoring of cane yield and its behavior on a small scale. This study tested a method for sugarcane yield data cleaning, investigating if the data recording frequency influences the characterization of yield variations in mapping high-resolution spatial data within a single row. Four data sets from yield monitors of single row harvesting were used. A cleaning process with global and anisotropic filtering in a single sugarcane row was applied. The local outlier cleaner compares the yield value of a point with its nearest neighbors within the same row. Even after the elimination of outliers, there is great variation in yield between the rows, and this variation is much smaller in a single row. A frequency of 2 Hz was required for identifying and characterizing small yield variations within the sugarcane rows whilst other frequencies tried (0.2 and 0.1 Hz) resulted in loss of information on yield variability within the row. The difference between the root mean square error (RMSE) of ordinary kriging (OK) and inverse distance weighting (IDW) techniques is large enough to suggest the use of an individual yield line. Individual yield lines saved information in the data generated by the yield monitor unlike IDW and OK interpolation methods which omitted information over short distances within the rows and compromised the quality of high-resolution maps

High-resolution imagery data to assess the spatial variability of sugarcane fields/ Dados de imagens de alta resolução para avaliação da variabilidade espacial de talhões de cana-de-açúcar

The vegetation index (VI) generated from orbital images are essential tools to identify the spatial variability of the crops. The objective of this study was to evaluate the spatial variability of sugarcane fields using imagery data. Also, to explore the ideal period to correlate the Normalized Difference Vegetation Index (NDVI), Normalized Difference Red-Edge Index (NDRE) and Wide Dynamic Range Vegetation Index (WDRVI) with sugarcane yield. Four fields were selected in the state of São Paulo (56.37 ha) during the 2017/2018 and 2018/2019 growing seasons, as well as five fields in the state of Goiás (86.86 ha) during the 2019/2020 growing season. The VIs were calculated using orbital images from Sentinel-2 (spatial resolution of 10 m). The yield data were generated by a commercial sensor-system installed on the harvesters with a resolution of 0.20 Hz. Yield data were filtered and interpolated using the same resolution of the orbital images. Pearson's correlation was calculated between the yield and the VIs for each orbital image. The considered VIs were able to identify the spatial variability of sugarcane fields with coefficients of correlation of 0.95 and 0.96. The sugarcane stalks growth was the best period to correlate the VIs and the yield maps among the analyzed fields

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Processing of data from sugarcane yield monitors

Na cultura da cana-de-açúcar, a colheita é realizada por uma colhedora que efetua o corte e processamento do produto colhido ao longo de uma (ou duas) fileira (s) da cultura estabelecida. Neste processo, dados obtidos por monitor de produtividade, quando existentes, fornecem informações com diferentes utilidades. Métodos existentes para o processamento de dados de produtividade utlizados atualmente foram desenvolvidos para conjuntos de dados de produtividade de grãos e quando aplicados a um conjunto de dados de produtividade de cana-de-açúcar podem eliminar dados com variações reais de produtividade dentro da fileira. O objetivo deste trabalho é desenvolver métodos que busquem identificar e remover dados errôneos, em pós-processamento, do conjunto de dados gerados por monitor de produtividade para caracterização das pequenas variações de produtividade dentro de uma fileira de cana-de-açúcar. A identificação de dados discrepantes do conjunto de dados utilizando método estatístico por quartis e uma filtragem comparando valores de produtividade usando somente dados de uma única passada da colhedora foi proposto. Foram utlizados quatro conjunto de dados de produtividade gerados por dois monitores. O monitor de produtividade 1 registrou os dados a uma frequência de 0,5 Hz e o monitor de produtividade 2 a uma frequência de 1 Hz. Foram encontrados dados errôneos gerados devido ao tempo de sincronização entre a colhedora e o conjunto transbordo durante as manobras de cabeceira e durante a troca do conjunto de transbordo. Também foram encontrados dados durante a manobras da colhedora, onde o monitor registrou dados com produtividade zero e nulas. Foram simuladas diferentes frequência de registro de dados com objetivo de verificar se a densidade de dados fornecida pelo monitor influência na caracterização de pequenas variações nos valores de produtividade dentro da passada. Os conjuntos de dados de produtividade gerados por diferentes tipos de monitores demostraram a necessidade de pós-processamento para remoção devalores de produtividades discrepantes. A metodologia desenvolvida neste trabalho foi capaz de identificar e eliminar os dados errôneos dos conjuntos de dados analisados. A metodologia de filtragem de dados considerando somente dados dentro de uma única passada da colhedora de cana-de-açúcar proporcionou a caracterização da variação de valores de produtividade em pequenas distâncias.In the sugarcane crop, a harvest is performed by a harvester who cuts and processes the product harvested along one (or two) row (s) of the established crop. In this process, data from yield monitor, when applicable, provide information with different utilities. Existing methods for processing yield data currently used have been developed for datasets of yield grain and when applied to a sugarcane yield dataset can eliminate data with actual variations of yield within the row. The objective of this work is to develop methods that seek to identify and remove erroneous data, in post-processing, from the data set generated by yield monitor to characterize the small variations of yield within a row of sugarcane. The identification of outliers from the data set using statistical method for comparing quartiles and filtering yield values using only data from a single past the harvester has been proposed. Assay were utilized four yield dataset generated by two monitors. The yield monitor 1 recorded data at a frequency of 0.5 Hz and the yield monitor 2 at a frequency of 1 Hz. Erroneous data were found in the data set generated due to the time of synchronization between the sugarcane harvester and the transportation of chopped sugarcane during the headland turns and during the exchange of the transportation of chopped sugarcane during harvest. Were also found during the headland turns of the sugarcane harvester, where the yield monitor recorded data with values of yield zero and void. It was simulated different frequency of recording data with the objective of verifying if density of data provided by the monitor influences in the characterization of small variations in the yield values within the path. The yield data sets generated by different types of displays have demonstrated the need for post-processing to remove outliers in the yield dataset. The methodology developed in this study was able to identify and eliminate erroneous data sets analyzed data. Data filtering methodology considering only data within a single pass of the sugarcane harvester provided to characterize the variation in yield values over short distances

Detecção e mapeamento de plantas de cana-de-açúcar para gerenciamento específico do local

The sugarcane production sector is one of the most adept at adopting technology to manage equipment and sugarcane fields. Developing new technologies and optimizing the use of the technologies already used in other production systems is essential for successful field management. The optimized use of technologies will help in the localized management to increase viability, maximize profitability, and minimize the environmental impacts of sugarcane production. Technologies to detect, measure, and spatialize plants can be one of the solutions for the row level management. Moreover, this data can be used to temporally follow the development of sugarcane fields, being essential data for localized field management. The spatialization of plants and plant spacing can help in the investigation of factors that influence sugarcane yield. In this context, the overall objective of the thesis was to explore tools and methods for detecting plants at row level to improve and support localized management of sugarcane plantations. An approach to sugarcane plant detection using photoelectric and ultrasonic sensors was developed and evaluated. Aerial image and ground sensors have been tested to detect and measure sugarcane plant spacing. Temporal evaluation of sensors and aerial images during four different stages of sugarcane development was made to propose the best time to detect sugarcane plants and measure the plant spacing. High-resolution images were used to map plant population and plant spacing. These two data were used to check the relationship between slope, path angle, and the plant population, furthermore, map regions with higher susceptibility to plant reduction over the years. At last, a spatio-temporal analysis of yield and plant spacing was performed to verify the relationship between these two variables in regions with different yield potentials in commercial crops. Results show that ultrasonic and photoelectric sensor fusion associated with the machine learning model has accuracy above 95%. These two sensors and high-resolution images had the best accuracy and precision in detecting and measuring plant spacing at 31 and 47 days after harvest. Spatial and temporal analysis showed that regions with a terrain slope of 5-8% and greater than 8% with curved paths have an inferior number of plants compared to other regions. The local analysis identified that regions with steeper slopes and curved paths have high susceptibility of plant reduction over the years compared to other regions. Finally, yield loss within the sugarcane row occurs with increasing plant spacing. Regions with different yield potentials require different optimum populations to maximize yield. Low-yielding regions require a larger plant population and are more susceptible to lose in yield within the row with increasing plant spacing.O setor de produção de cana-de-açúcar é um dos mais aptos a adotar tecnologia para gerenciar equipamentos e as lavouras de cana-de-açúcar. Desenvolver novas tecnologias e otimizar o uso das tecnologias já utilizadas em outros sistemas de produção é essencial para o sucesso da gestão das lavouras. O uso otimizado de tecnologias ajudará na gestão localizada a aumentar a viabilidade, maximizar a rentabilidade e minimizar os impactos ambientais da produção de cana-de-açúcar. Tecnologias para detectar, medir e espacializar plantas podem ser uma das soluções para o gerenciamento a nível de fileira. Além disso, estes dados podem ser usados para acompanhar temporariamente o desenvolvimento das lavouras de cana-de-açúcar, sendo dados essenciais para o gerenciamento localizado da lavoura. A espacialização das plantas e o espaçamento entre plantas podem ajudar na investigação dos fatores que influenciam a produtividade da cana-de-açúcar. Neste contexto, o objetivo geral da tese foi explorar ferramentas e métodos de detecção de plantas em nível de fileira para melhorar e apoiar o gerenciamento localizado de plantações de cana-de-açúcar. Uma abordagem para a detecção de plantas de cana-de-açúcar usando sensores fotoelétricos e ultrassônicos foi desenvolvida e avaliada. A imagem aérea e os sensores terrestres foram testados para detectar e medir o espaçamento entre plantas de cana-de-açúcar. A avaliação temporal dos sensores e imagens aéreas durante quatro estágios diferentes de desenvolvimento da cana-de-açúcar foi feita para propor o melhor momento para detectar plantas de cana-de-açúcar e medir o espaçamento entre as plantas. Imagens de alta resolução foram usadas para mapear a população e o espaçamento das plantas. Estes dois dados foram utilizados para verificar a relação entre declividade, ângulo do percurso e a população de plantas, além disso, mapear regiões com maior suscetibilidade à redução de plantas ao longo dos anos. Finalmente, foi realizada a análise espacial-temporal da produtividade e espaçamento de plantas para verificar a relação entre estas duas variáveis em regiões com diferentes potenciais produtivos em lavouras comerciais. Os resultados mostram que a fusão de sensores ultrassônico e fotoelétrico associada ao modelo de aprendizagem da máquina tem precisão acima de 95%. Estes dois sensores e imagens de alta resolução tiveram a melhor precisão e acurácia para detectar e medir o espaçamento das plantas em 31 e 47 dias após a colheita. A análise espacial e temporal mostrou que regiões com uma declive do terreno de 5-8% e maior que 8% com percursos curvos têm um número inferior de plantas em comparação com outras regiões. A análise local identificou que regiões com declives mais acentuados e caminhos curvos têm alta suscetibilidade de redução da planta ao longo dos anos, em comparação com outras regiões. Finalmente, a perda de produtividade dentro da fileira da cana de açúcar ocorre com o aumento do espaçamento entre as plantas. Regiões com diferentes potenciais produtivos requerem diferentes populações ótimas para maximizar a produtividade. Regiões de baixa produtividade requerem uma população de plantas maior e são mais suscetíveis a perder produtividade dentro da fileira com o aumento do espaçamento entre plantas

Sistema de monitoramento meteorológico através da Plataforma Arduino

Automatic weather stations are instruments of high operational cost. As a result, there are few places where real-time atmospheric measurements are taken Such measures can be employed to forecast a region's weather and climate, enabling decision-making in a variety of areas.Given the high importance of meteorological measurements and due to the reduced number of automatic stations, this work proposes the development of a low cost atmospheric data collection system using the Arduino Platform. In the present study, we elaborated a system composed by a set of sensors connected to an Arduino microcontroller. Wind speed, temperature, humidity and atmospheric pressure are the variables measured and controlled by the developed system.As estações meteorológicas automáticas são instrumentos de alto custo operacional. Em função disso, poucos são os locais onde são realizadas medidas atmosféricas em tempo real. Tais medidas podem ser empregadas para  a previsão do tempo e clima de uma região, permitindo a tomada de decisões em diversas áreas. Diante da elevada importância das medições meteorológicas e devido ao número reduzido de estações automáticas, neste trabalho propõe-se desenvolver um sistema de coleta de dados atmosféricos de baixo custo utilizando a Plataforma Arduino. No presente estudo elaborou-se um sistema composto por um conjunto de  sensores conectados a um microcontrolador Arduino. Velocidade do vento,  temperatura, umidade e pressão atmosférica são as variáveis medidas e controlados pelo sistema desenvolvido

Sensor Fusion with NARX Neural Network to Predict the Mass Flow in a Sugarcane Harvester

Measuring the mass flow of sugarcane in real-time is essential for harvester automation and crop monitoring. Data integration from multiple sensors should be an alternative to receive more reliable, accurate, and valuable predictions than data delivered by a single sensor. In this sense, the objective was to evaluate if the fusion of different sensors installed in a sugarcane harvester improves the mass flow prediction accuracy. A harvester was experimentally instrumented, and neural network models integrated sensor data along the harvester to perform the self-calibration of these sensors and estimate the mass flow. Nonlinear autoregressive networks with exogenous input (NARX) and multiple linear regression (MLR) models were compared to predict the mass flow. The prediction with the NARX showed a significant superiority over MLR. MLR decreases the estimated mass flow variability in the harvester. NARX with multi-sensor data has an RMSE of 0.3 kg s−1, representing a MAPE of 0.7%. The fusion of sensor signals improves prediction accuracy, with higher performance than studies with approaches that used a single sensor. The mass flow approach with multiple sensors is a potential approach to replace conventional yield monitors. The system generates accurate data with high sample density within sugarcane rows

Carrot Yield Mapping: A Precision Agriculture Approach Based on Machine Learning

Carrot yield maps are an essential tool in supporting decision makers in improving their agricultural practices, but they are unconventional and not easy to obtain. The objective was to develop a method to generate a carrot yield map applying a random forest (RF) regression algorithm on a database composed of satellite spectral data and carrot ground-truth yield sampling. Georeferenced carrot yield sampling was carried out and satellite imagery was obtained during crop development. The entire dataset was split into training and test sets. The Gini index was used to find the five most important predictor variables of the model. Statistical parameters used to evaluate model performance were the root mean squared error (RMSE), coefficient of determination (R2) and mean absolute error (MAE). The five most important predictor variables were the near-infrared spectral band at 92 and 79 days after sowing (DAS), green spectral band at 50 DAS and blue spectral band at 92 and 81 DAS. The RF algorithm applied to the entire dataset presented R2, RMSE and MAE values of 0.82, 2.64 Mg ha−1 and 1.74 Mg ha−1, respectively. The method based on RF regression applied to a database composed of spectral bands proved to be accurate and suitable to predict carrot yield