Detection of leafy spurge using hyper-spectral-spatial-temporal imagery

Jay, Steven C.; Lawrence, Rick L.; Repasky, Kevin S.; Rew, Lisa J.

doi:10.1109/igarss.2010.5652580

Cited by 10 publications

(7 citation statements)

References 6 publications

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“…Literature shows that EC a has the potential to become a widely adopted means for characterizing the spatial variability of soil properties at field and landscape scales [7,8]. Spatial variability of soil properties can also be characterized by other means such as ground penetrating radar (GPR) [9,10], time domain reflectometry (TDR) [11,12], cosmic-ray neutrons [13,14], aerial photography [5,15], multi-and hyper-spectral imagery [16,17], or by a combination of several approaches as shown by Rudolph et al [18].…”

Section: Introductionmentioning

confidence: 99%

Soil Moisture Mapping Using Multi-Frequency and Multi-Coil Electromagnetic Induction Sensors on Managed Podzols

et al. 2018

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Precision agriculture (PA) involves the management of agricultural fields including spatial information of soil properties derived from apparent electrical conductivity (ECa) measurements. While this approach is gaining much attention in agricultural management, farmed podzolic soils are under-represented in the relevant literature. This study: (i) established the relationship between ECa and soil moisture content (SMC) measured using time domain reflectometry (TDR); and (ii) evaluated the estimated SMC with ECa measurements obtained with two electromagnetic induction (EMI) sensors, i.e., multi-coil and multi-frequency, using TDR measured SMC. Measurements were taken on several plots at Pynn’s Brook Research Station, Pasadena, Newfoundland, Canada. The means of ECa measurements were calculated for the same sampling location in each plot. The linear regression models generated for SMC using the CMD-MINIEXPLORER were statistically significant with the highest R2 of 0.79 and the lowest RMSE (root mean square error) of 0.015 m3 m−3 but were not significant for GEM-2 with the lowest R2 of 0.17 and RMSE of 0.045 m3 m−3; this was due to the difference in the depth of investigation between the two EMI sensors. The validation of the SMC regression models for the two EMI sensors produced the highest R2 = 0.54 with the lowest RMSE prediction = 0.031 m3 m−3 given by CMD-MINIEXPLORER. The result demonstrated that the CMD-MINIEXPLORER based measurements better predicted shallow SMC, while deeper SMC was better predicted by GEM-2 measurements. In addition, the ECa measurements obtained through either multi-coil or multi-frequency sensors have the potential to be successfully employed for SMC mapping at the field scale.

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Section: Introductionmentioning

confidence: 99%

Soil Moisture Mapping Using Multi-Frequency and Multi-Coil Electromagnetic Induction Sensors on Managed Podzols

et al. 2018

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“…Hyperspectral imaging was originally developed for remote sensing of the earth from satellites [ 58 ]. Hyperspectral imaging has been used in numerous fields including agriculture [ 59 , 60 ], medicine [ 61 ], and pharmaceuticals [ 62 ]. Applications for food quality and safety include the detection of contaminants, identification of defects, quantification of constituents, and sensory analysis [ 63 ].…”

Section: Detection Of Foreign Materials In Food Productsmentioning

confidence: 99%

Detection and prevention of foreign material in food: A review

Payne,

O'Bryan,

Marcy

et al. 2023

Heliyon

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“…Various machine learning-based methods such as random forest, artificial neural networks, and support vector machines have been used in recent years to obtain reliable and most accurate information from satellite images efficiently by image classification. There are many studies in which the RF classifier stands out in terms of classification accuracy when it comes to the use of machine learning methods in agriculture [24][25][26][27][28][29]. In the realm of precision agriculture applications, deep learning algorithms, which are a subset of machine learning techniques, have gained significant popularity in recent years due to their ability to provide more precise and reliable detection of agricultural products on images [30].…”

Section: Introductionmentioning

confidence: 99%

Semantic segmentation of very-high spatial resolution satellite images: A comparative analysis of 3D-CNN and traditional machine learning algorithms for automatic vineyard detection

AKAR,

SARALIOĞLU,

GÜNGÖR

et al. 2024

International Journal of Engineering and Geosciences

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Erzincan (Cimin) grape, which is an endemic product, makes a significant contribution to the economy of both the region where it is grown and the country's economy. For this reason, the follow-up and dissemination of this product have become increasingly important. The study aims to determine the spatial distribution of vineyards by classifying very-high spatial resolution satellite images using machine learning and deep learning algorithms. A 3D Convolutional Neural Network (CNN) based deep learning model was created for classification vineyards. Then, this model has compared machine learning algorithms, namely support vector machine (SVM), Random Forest (RF), and Rotation Forest (RTF). Post-classification accuracy analyses were performed using error matrices, kappa analysis and McNemar tests, and the best overall classification accuracies and kappa values were obtained for 3D CNN and RF as 86.47% (0.8308) and 70.53% (0.6279), respectively. It was seen that the accuracy of RF increased to 75.94% (0.6364) when Gabor texture features were used, yet the highest classification accuracy was obtained with the 3D CNN classifier with an 11% higher score (86.47%). The χ^2 values calculated with the McNemar's statistical hypothesis test for all classification results are higher than 3.84 at the 95% confidence interval, indicating that the 3D CNN classifier has improved the classification accuracy, significantly.

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Detection of leafy spurge using hyper-spectral-spatial-temporal imagery

Cited by 10 publications

References 6 publications

Soil Moisture Mapping Using Multi-Frequency and Multi-Coil Electromagnetic Induction Sensors on Managed Podzols

Soil Moisture Mapping Using Multi-Frequency and Multi-Coil Electromagnetic Induction Sensors on Managed Podzols

Detection and prevention of foreign material in food: A review

Semantic segmentation of very-high spatial resolution satellite images: A comparative analysis of 3D-CNN and traditional machine learning algorithms for automatic vineyard detection

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