scholarly journals Leaf Nitrogen Concentration and Plant Height Prediction for Maize Using UAV-Based Multispectral Imagery and Machine Learning Techniques

2020 ◽  
Vol 12 (19) ◽  
pp. 3237
Author(s):  
Lucas Prado Osco ◽  
José Marcato Junior ◽  
Ana Paula Marques Ramos ◽  
Danielle Elis Garcia Furuya ◽  
Dthenifer Cordeiro Santana ◽  
...  
Keyword(s):  
Machine Learning ◽  
Plant Height ◽  
Vegetation Index ◽  
Nitrogen Concentration ◽  
Leaf Nitrogen ◽  
Machine Learning Techniques ◽  
Multispectral Imagery ◽  
Leaf Nitrogen Concentration ◽  
Spectral Bands ◽  
Learning Techniques

Under ideal conditions of nitrogen (N), maize (Zea mays L.) can grow to its full potential, reaching maximum plant height (PH). As a rapid and nondestructive approach, the analysis of unmanned aerial vehicles (UAV)-based imagery may be of assistance to estimate N and height. The main objective of this study is to present an approach to predict leaf nitrogen concentration (LNC, g kg−1) and PH (m) with machine learning techniques and UAV-based multispectral imagery in maize plants. An experiment with 11 maize cultivars under two rates of N fertilization was carried during the 2017/2018 and 2018/2019 crop seasons. The spectral vegetation indices (VI) normalized difference vegetation index (NDVI), normalized difference red-edge index (NDRE), green normalized difference vegetation (GNDVI), and the soil adjusted vegetation index (SAVI) were extracted from the images and, in a computational system, used alongside the spectral bands as input parameters for different machine learning models. A randomized 10-fold cross-validation strategy, with a total of 100 replicates, was used to evaluate the performance of 9 supervised machine learning (ML) models using the Pearson’s correlation coefficient (r), mean absolute error (MAE), coefficient of regression (R²), and root mean square error (RMSE) metrics. The results indicated that the random forest (RF) algorithm performed better, with r and RMSE, respectively, of 0.91 and 1.9 g.kg−¹ for LNC, and 0.86 and 0.17 m for PH. It was also demonstrated that VIs contributed more to the algorithm’s performances than individual spectral bands. This study concludes that the RF model is appropriate to predict both agronomic variables in maize and may help farmers to monitor their plants based upon their LNC and PH diagnosis and use this knowledge to improve their production rates in the subsequent seasons.

scholarly journals APPLICATION OF MACHINE LEARNING TO THE PREDICTION OF VEGETATION HEALTH

2016 ◽  
Vol XLI-B2 ◽  
pp. 465-469 ◽  
Author(s):  
Emily Burchfield ◽  
John J. Nay ◽  
Jonathan Gilligan
Keyword(s):  
Machine Learning ◽  
Predictive Power ◽  
Vegetation Index ◽  
Region Of Interest ◽  
Environmental Data ◽  
Decision Makers ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Health Dynamics ◽  
Vegetation Health

This project applies machine learning techniques to remotely sensed imagery to train and validate predictive models of vegetation health in Bangladesh and Sri Lanka. For both locations, we downloaded and processed eleven years of imagery from multiple MODIS datasets which were combined and transformed into two-dimensional matrices. We applied a gradient boosted machines model to the lagged dataset values to forecast future values of the Enhanced Vegetation Index (EVI). The predictive power of raw spectral data MODIS products were compared across time periods and land use categories. Our models have significantly more predictive power on held-out datasets than a baseline. Though the tool was built to increase capacity to monitor vegetation health in data scarce regions like South Asia, users may include ancillary spatiotemporal datasets relevant to their region of interest to increase predictive power and to facilitate interpretation of model results. The tool can automatically update predictions as new MODIS data is made available by NASA. The tool is particularly well-suited for decision makers interested in understanding and predicting vegetation health dynamics in countries in which environmental data is scarce and cloud cover is a significant concern.

scholarly journals APPLICATION OF MACHINE LEARNING TO THE PREDICTION OF VEGETATION HEALTH

2016 ◽  
Vol XLI-B2 ◽  
pp. 465-469
Author(s):  
Emily Burchfield ◽  
John J. Nay ◽  
Jonathan Gilligan
Keyword(s):  
Machine Learning ◽  
Predictive Power ◽  
Vegetation Index ◽  
Region Of Interest ◽  
Environmental Data ◽  
Decision Makers ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Health Dynamics ◽  
Vegetation Health

This project applies machine learning techniques to remotely sensed imagery to train and validate predictive models of vegetation health in Bangladesh and Sri Lanka. For both locations, we downloaded and processed eleven years of imagery from multiple MODIS datasets which were combined and transformed into two-dimensional matrices. We applied a gradient boosted machines model to the lagged dataset values to forecast future values of the Enhanced Vegetation Index (EVI). The predictive power of raw spectral data MODIS products were compared across time periods and land use categories. Our models have significantly more predictive power on held-out datasets than a baseline. Though the tool was built to increase capacity to monitor vegetation health in data scarce regions like South Asia, users may include ancillary spatiotemporal datasets relevant to their region of interest to increase predictive power and to facilitate interpretation of model results. The tool can automatically update predictions as new MODIS data is made available by NASA. The tool is particularly well-suited for decision makers interested in understanding and predicting vegetation health dynamics in countries in which environmental data is scarce and cloud cover is a significant concern.

scholarly journals mosquitoes.pdf

2019 ◽  
Author(s):  
Oladimeji Mudele ◽  
Fabio M. Bayer ◽  
Lucas Zanandrez ◽  
Alvaro Eiras ◽  
Paolo Gamba
Keyword(s):  
Machine Learning ◽  
Statistical Models ◽  
Land Surface ◽  
Vegetation Index ◽  
Machine Learning Techniques ◽  
Distribution Model ◽  
Support Vector ◽  
World Population ◽  
Vector Population ◽  
Learning Techniques

<div>Over 50% of the world population is at risk of mosquito-borne diseases. Female Ae. aegypti mosquito species transmit Zika, Dengue, and Chikungunya. The spread of these diseases correlate positively with the vector population, and this population depends on biotic and abiotic environmental factors including temperature, vegetation condition, humidity and precipitation. To combat virus outbreaks, information about vector population is required. To this aim, Earth observation (EO) data provide fast, efficient and economically viable means to estimate environmental features of interest. In this work, we present a temporal distribution model for adult female Ae. aegypti mosquitoes based on the joint use of the Normalized Difference Vegetation Index, the Normalized Difference Water Index, the Land Surface Temperature (both at day and night time), along with the precipitation information, extracted from EO data. The model was applied separately to data obtained during three different vector control and field data collection condition regimes, and used to explain the differences in environmental variable contributions across these regimes. To this aim, a random forest (RF) regression technique and its nonlinear features importance ranking based on mean decrease impurity (MDI) were implemented. To prove the robustness of the proposed model, other machine learning techniques, including support vector regression, decision trees and k-nearest neighbor regression, as well as artificial neural networks, and statistical models such as the linear regression model and generalized linear model were also considered. Our results show that machine learning techniques perform better than linear statistical models for the task at hand, and RF performs best. By ranking the importance of all features based on MDI in RF and selecting the subset comprising the most</div>

scholarly journals Adaboost-Based Machine Learning Improved the Modeling Robust and Estimation Accuracy of Pear Leaf Nitrogen Concentration by In-Field VIS-NIR Spectroscopy

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6260
Author(s):  
Jie Wang ◽  
Wei Xue ◽  
Xiaojun Shi ◽  
Yangchun Xu ◽  
Caixia Dong
Keyword(s):  
Machine Learning ◽  
Nitrogen Concentration ◽  
Nir Spectroscopy ◽  
Leaf Nitrogen ◽  
Estimation Accuracy ◽  
Support Vector ◽  
Leaf Nitrogen Concentration ◽  
Adaboost Algorithm ◽  
N Concentration ◽  
Pear Trees

Different cultivars of pear trees are often planted in one orchard to enhance yield for its gametophytic self-incompatibility. Therefore, an accurate and robust modelling method is needed for the non-destructive determination of leaf nitrogen (N) concentration in pear orchards with mixed cultivars. This study proposes a new technique based on in-field visible-near infrared (VIS-NIR) spectroscopy and the Adaboost algorithm initiated with machine learning methods. The performance was evaluated by estimating leaf N concentration for a total of 1285 samples from different cultivars, growth regions, and tree ages and compared with traditional techniques, including vegetation indices, partial least squares regression, singular support vector regression (SVR) and neural networks (NN). The results demonstrated that the leaf reflectance responded to the leaf nitrogen concentration were more sensitive to the types of cultivars than to the different growing regions and tree ages. Moreover, the AdaBoost.RT-BP had the best accuracy in both the training (R2 = 0.96, root mean relative error (RMSE) = 1.03 g kg−1) and the test datasets (R2 = 0.91, RMSE = 1.29 g kg−1), and was the most robust in repeated experiments. This study provides a new insight for monitoring the status of pear trees by the in-field VIS-NIR spectroscopy for better N managements in heterogeneous pear orchards.

Classification of an Intertidal Reef by Machine Learning Techniques Using UAV Based RGB and Multispectral Imagery

2021 ◽  
Author(s):  
Debora Borges ◽  
Luas Padua ◽  
Isabel Costa Azevedo ◽  
Joelen Silva ◽  
Joaquim J. Sousa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Multispectral Imagery ◽  
Learning Techniques
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