The Experts below are selected from a list of 30732 Experts worldwide ranked by ideXlab platform
José Paulo Molin - One of the best experts on this subject based on the ideXlab platform.
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Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique
Remote Sensing, 2021Co-Authors: Tatiana Fernanda Canata, Marcelo Chan Fu Wei, Leonardo Felipe Maldaner, José Paulo MolinAbstract:Yield maps provide essential information to guide precision agriculture (PA) practices. Yet, on-board Yield monitoring for sugarcane can be challenging. At the same time, orbital images have been widely used for indirect crop Yield estimation for many crops like wheat, corn, and rice, but not for sugarcane. Due to this, the objective of this study is to explore the potential of multi-temporal imagery data as an alternative for sugarcane Yield Mapping. The study was based on developing predictive sugarcane Yield models integrating time-series orbital imaging and a machine learning technique. A commercial sugarcane site was selected, and Sentinel-2 images were acquired from the beginning of the ratoon sprouting until harvesting of two consecutive cropping seasons. The predictive Yield models RF (Random forest) and MLR (Multiple Linear Regression) were developed using orbital images and Yield maps generated by a commercial sensor-system on harvesting. Original Yield data were filtered and interpolated with the same spatial resolution of the orbital images. The entire dataset was divided into training and testing datasets. Spectral bands, especially the near-infrared at tillering crop stage showed greater contribution to predicting sugarcane Yield than the use of derived spectral vegetation indices. The Root Mean Squared Error (RMSE) obtained for the RF regression based on multiple spectral bands was 4.63 Mg ha−1 with an R2 of 0.70 for the testing dataset. Overall, the RF regression had better performance than the MLR to predict sugarcane Yield.
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Yield Mapping methods for manually harvested crops
Computers and Electronics in Agriculture, 2020Co-Authors: André Freitas Colaço, Rodrigo Gonçalves Trevisan, F. H. S. Karp, José Paulo MolinAbstract:Abstract Lack of Yield Mapping solutions is currently a bottleneck for Precision Agriculture development and adoption in many manually harvested fruit and vegetable crops. In such systems, the handpicked produce is briefly stored in bags or boxes across the field before they are loaded and transported. This study tested a simple Yield Mapping method based on georeferencing the bags used during harvest with local Yield calculated based on the distribution of these points across the field. Virtual Yield maps and real field data were used to validate different data processing methods under different scenarios; scenarios included different levels of Yield spatial variability and bag positioning and mass errors. Method 1 calculated Yield at each bag point by estimating the area needed to fill it; such area was based on the bag distance to its neighbours. Method 2 calculated local Yield based on the distribution of bags across an area using a moving window approach. In normal field situations – with bag positioning and mass errors below 1 m and 5% – the approaches had similar performance with accuracy levels varying between 5 and 11 Mg ha−1, depending on the Yield spatial variability. With increasing bag positioning error, method 2 outperformed method 1. Both approaches were little affected by error in bag mass estimation. Overall, the Yield Mapping methods are useful in supporting most applications in Precision Agriculture and can be easily implemented in a software tool to promote user adoption and site-specific management.
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carrot Yield Mapping a precision agriculture approach based on machine learning
Artificial Intelligence, 2020Co-Authors: Marcelo Chan Fu Wei, Leonardo Felipe Maldaner, Pedro Medeiros Netto Ottoni, José Paulo MolinAbstract: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.
Alan C. Hansen - One of the best experts on this subject based on the ideXlab platform.
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Sugarcane Yield Mapping based on vehicle tracking
Precision Agriculture, 2019Co-Authors: Md Abdul Momin, Domingos S. Valente, Tony E. Grift, Alan C. HansenAbstract:The agricultural industry is increasingly reliant upon the development of technologies that employ real-time monitoring of machine performance to generate pertinent information for machine operators, owners, and managers. Yield Mapping in particular is an important component of implementing precision agricultural practices and assessing spatial variability. In an attempt to generate Yield maps in sugarcane, this research estimated Yield in the field based on GPS data from harvesters, tractors and semi-trucks. The method was based on identifying “fill events”, which represent a distance through which the tractor/wagon combination traveled in parallel with the harvester, indicating that the wagon was being filled. Each wagon was filled to approximately 10 Mg of sugarcane, which was divided by the fill event distance and row width to determine the Yield in Mg ha^−1. A total of 76 fill events were observed from a 7.1 ha harvested area. Based on the estimated Yield per fill event, a rudimentary Yield map was developed, which was expanded into a generalized Yield map for the 7.1 ha harvested area.
Marcelo Chan Fu Wei - One of the best experts on this subject based on the ideXlab platform.
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Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique
Remote Sensing, 2021Co-Authors: Tatiana Fernanda Canata, Marcelo Chan Fu Wei, Leonardo Felipe Maldaner, José Paulo MolinAbstract:Yield maps provide essential information to guide precision agriculture (PA) practices. Yet, on-board Yield monitoring for sugarcane can be challenging. At the same time, orbital images have been widely used for indirect crop Yield estimation for many crops like wheat, corn, and rice, but not for sugarcane. Due to this, the objective of this study is to explore the potential of multi-temporal imagery data as an alternative for sugarcane Yield Mapping. The study was based on developing predictive sugarcane Yield models integrating time-series orbital imaging and a machine learning technique. A commercial sugarcane site was selected, and Sentinel-2 images were acquired from the beginning of the ratoon sprouting until harvesting of two consecutive cropping seasons. The predictive Yield models RF (Random forest) and MLR (Multiple Linear Regression) were developed using orbital images and Yield maps generated by a commercial sensor-system on harvesting. Original Yield data were filtered and interpolated with the same spatial resolution of the orbital images. The entire dataset was divided into training and testing datasets. Spectral bands, especially the near-infrared at tillering crop stage showed greater contribution to predicting sugarcane Yield than the use of derived spectral vegetation indices. The Root Mean Squared Error (RMSE) obtained for the RF regression based on multiple spectral bands was 4.63 Mg ha−1 with an R2 of 0.70 for the testing dataset. Overall, the RF regression had better performance than the MLR to predict sugarcane Yield.
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carrot Yield Mapping a precision agriculture approach based on machine learning
Artificial Intelligence, 2020Co-Authors: Marcelo Chan Fu Wei, Leonardo Felipe Maldaner, Pedro Medeiros Netto Ottoni, José Paulo MolinAbstract: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.
Md Abdul Momin - One of the best experts on this subject based on the ideXlab platform.
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Sugarcane Yield Mapping based on vehicle tracking
Precision Agriculture, 2019Co-Authors: Md Abdul Momin, Domingos S. Valente, Tony E. Grift, Alan C. HansenAbstract:The agricultural industry is increasingly reliant upon the development of technologies that employ real-time monitoring of machine performance to generate pertinent information for machine operators, owners, and managers. Yield Mapping in particular is an important component of implementing precision agricultural practices and assessing spatial variability. In an attempt to generate Yield maps in sugarcane, this research estimated Yield in the field based on GPS data from harvesters, tractors and semi-trucks. The method was based on identifying “fill events”, which represent a distance through which the tractor/wagon combination traveled in parallel with the harvester, indicating that the wagon was being filled. Each wagon was filled to approximately 10 Mg of sugarcane, which was divided by the fill event distance and row width to determine the Yield in Mg ha^−1. A total of 76 fill events were observed from a 7.1 ha harvested area. Based on the estimated Yield per fill event, a rudimentary Yield map was developed, which was expanded into a generalized Yield map for the 7.1 ha harvested area.
R.k. Mazari - One of the best experts on this subject based on the ideXlab platform.
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Sediment Yield Mapping using small dam sedimentation surveys, Southern Tablelands, New South Wales
CATENA, 1993Co-Authors: D.t. Neil, R.k. MazariAbstract:Summary Sediment Yield estimates from sedimentation surveys of small farm dams are a cost effective method of obtaining long-term data suitable for use in catchment management. In this study, the results of a number of such surveys on the Southern Tablelands of New South Wales are used to derive empirical equations for sediment Yield in relation to catchment characteristics. The equations are used to estimate the distribution of sediment Yield in an adjacent catchment of similar morphology. In areas where numerous such dams have been built this approach can be used to derive sediment Yield data for a wide variety of terrain and land use types, which can then be extrapolated to larger areas for which no data exist.
