scholarly journals Estimation of soil moisture at different soil levels using machine learning techniques and unmanned aerial vehicle (UAV) multispectral imagery

2019 ◽  
Author(s):  
Mahyar Aboutalebi ◽  
Niel Allen ◽  
Alfonso F. Torres-Rua ◽  
Mac McKee ◽  
Calvin Coopmans
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Unmanned Aerial Vehicle ◽  
Machine Learning Techniques ◽  
Multispectral Imagery ◽  
Learning Techniques ◽  
Aerial Vehicle

scholarly journals Comparison of Outdoor Compost Pile Detection Using Unmanned Aerial Vehicle Images and Various Machine Learning Techniques

Drones ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 31
Author(s):  
Bonggeun Song ◽  
Kyunghun Park
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
South Korea ◽  
Unmanned Aerial Vehicle ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Efficient Detection ◽  
Learning Techniques ◽  
Aerial Vehicle ◽  
Uav Images

Since outdoor compost piles (OCPs) contain large amounts of nitrogen and phosphorus, they act as a major pollutant that deteriorates water quality, such as eutrophication and green algae, when the OCPs enter the river during rainfall. In South Korea, OCPs are frequently used, but there is a limitation that a lot of manpower and budget are consumed to investigate the current situation, so it is necessary to efficiently investigate the OCPs. This study compared the accuracy of various machine learning techniques for the efficient detection and management of outdoor compost piles (OCPs), a non-point pollution source in agricultural areas in South Korea, using unmanned aerial vehicle (UAV) images. RGB, multispectral, and thermal infrared UAV images were taken in August and October 2019. Additionally, vegetation indices (NDVI, NDRE, ENDVI, and GNDVI) and surface temperature were also considered. Four machine learning techniques, including support vector machine (SVM), decision tree (DT), random forest (RF), and k-NN, were implemented, and the machine learning technique with the highest accuracy was identified by adjusting several variables. The accuracy of all machine learning techniques was very high, reaching values of up to 0.96. Particularly, the accuracy of the RF method with the number of estimators set to 10 was highest, reaching 0.989 in August and 0.987 in October. The proposed method allows for the prediction of OCP location and area over large regions, thereby foregoing the need for OCP field measurements. Therefore, our findings provide highly useful data for the improvement of OCP management strategies and water quality.

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.

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