Methodology to filter out outliers in high spatial density data to improve maps reliability

Maldaner, Leonardo Felipe; Molin, José Paulo; Spekken, Mark

doi:10.1590/1678-992x-2020-0178

Cited by 10 publications

(3 citation statements)

References 15 publications

(37 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After collection, the data were processed by removing the outliers identified in the processing according to the methodology described by Maldane et al. (2021a), which takes into account the local and global variability in data processing. After processing, grain yield maps of the two crops were generated, allowing such data to be used for training and testing the models.…”

Section: Methodsmentioning

confidence: 99%

Corn grain yield forecasting by satellite remote sensing and machine‐learning models

et al. 2022

View full text Add to dashboard Cite

This study aimed to evaluate the performance of six machine-learning models in forecasting corn (Zea mays L.) grain yield before harvest using, as input, variables in the models, some of the most-used vegetation indices (VIs) and spectral bands in the literature, as well as using data at 770 and 980 sum of degree days (SDD).The field study was carried out in a commercial area in the 2017-2018 and 2018-2019 harvests. Spectral data were obtained from Sentinel-2 satellite images and were used as input variables in the proposed models: artificial neural networks (ANN), k-nearest neighbors (KNN), random forest (RF), and support vector machine (SVM). The maximum R 2 and minimum values of mean absolute error (MAE) and RMSE were 0.89, 0.33, and 0.42 t ha −1 , respectively, for the RF algorithm using all input variables. The results obtained in the present study show that it is possible to predict corn grain yield 80 d before harvest using only VIs for the crop. Testing the various combinations of spectral bands and VIs resulted in obtaining the GREEN band and the VI global environment monitoring index (GEMI) as the best predictor variables in the present study. The use of more than one SDD did not improve the performance of the models tested. The models developed using data at 980 SDD obtained the best precision and accuracy performance both in the scenario with all model input variables and with the two best predictors. The KNN algorithm obtained the best performance in the precision and accuracy metrics for most of the scenarios studied in the present work.

show abstract

Section: Methodsmentioning

confidence: 99%

Corn grain yield forecasting by satellite remote sensing and machine‐learning models

et al. 2022

View full text Add to dashboard Cite

show abstract

“…According to the author, the technology is low-cost and necessary for the widespread use of target-oriented selective spraying. Maldaner et al (2021), present the MapFilter 2.0 software (Table III, item 16), developed to analyze and remove inconsistent data in high-density agricultural datasets. The authors claim that the software is easy to install and has a friendly interface (Fig.…”

Section: A -Crop and Climate Protection And Diagnosismentioning

confidence: 99%

A Review on the Use of Open Source Technologies and Soft-Ware Applied to Precision Agriculture Practices

Santos¹,

Fachada²,

Beko³

et al. 2023

Preprint

View full text Add to dashboard Cite

Agricultural production needs technologies that assist the management of natural resources, for example, the collection of real-time data on soil, water, weather, crops, and biodiversity conditions. Sensor technology solutions and open-source software are appropriate for promoting more sustainable agricultural production. Among the advantages of using open-source technologies and software is its potential for extension, collaboration, customization, flexibility, maintenance cost, transparency, speed, and better security. Given the above, the objective of this research was to find, in different electronic databases, exclusively open-source software for precision agriculture, offering a systematic review, and addressing considerations and challenges. This survey considers up-to-date open-source software available in repositories such as GitHub and GitLab, to understand its characteristics and application formats.

show abstract

“…. , k n ), where n represents the number of data regions, and then the data flow density of the data region [11] can be expressed by formula (1):…”

Section: Behavioral Data Miningmentioning

confidence: 99%

Analysis of Persuasive Design Mechanism Based on Unconscious Calculation

Zheng

2022

Scientific Programming

View full text Add to dashboard Cite

In order to make users’ behavior more standardized, a persuasive design mechanism analysis method based on unconscious calculation is proposed. Taking the concept of persuasion and the goal of persuading function as the theoretical basis, the user behavior data is obtained in the form of correlation calculation, and the ant colony algorithm is used to classify the behavior data. According to the results of data processing, analyze unconscious behavior and its characteristics, and obtain the persuasion mechanism through unconscious calculation, persuasion model, and persuasion model design. The experimental results show that the method in this paper has a higher acceptance of behavioral persuasion and higher satisfaction of the persuaded, indicating that the method has strong practical applicability.

show abstract

Methodology to filter out outliers in high spatial density data to improve maps reliability

Cited by 10 publications

References 15 publications

Corn grain yield forecasting by satellite remote sensing and machine‐learning models

Corn grain yield forecasting by satellite remote sensing and machine‐learning models

A Review on the Use of Open Source Technologies and Soft-Ware Applied to Precision Agriculture Practices

Analysis of Persuasive Design Mechanism Based on Unconscious Calculation

Contact Info

Product

Resources

About