Retrieval of spinach crop parameters by microwave remote sensing with back propagation artificial neural networks: A comparison of different transfer functions

Prasad, Rajendra; Pandey, Abhishek; Singh, Kumar Hemant; Singh, Veer; Mishra, Rakesh; Singh, Dharmendra

doi:10.1016/j.asr.2012.04.010

Cited by 55 publications

(22 citation statements)

References 17 publications

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“…Machine-learning algorithms, including classification and regression trees (CART), artificial neural networks (ANN), support vector machines (SVM) and random forests (RF), can often reach a superior prediction accuracy due to fewer assumptions about the data and processes. Several studies have demonstrated that machine-learning algorithms are effective for modeling vegetation LAI using remotely sensed data and field measurements [45,46]. However, a single model has a certain randomness and instability partially due to the possible change in input variables performance during the calibration process [47].…”

mentioning

confidence: 99%

Exploring the Potential of WorldView-2 Red-Edge Band-Based Vegetation Indices for Estimation of Mangrove Leaf Area Index with Machine Learning Algorithms

Zhu

Liu

et al. 2017

Remote Sensing

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Abstract:To accurately estimate leaf area index (LAI) in mangrove areas, the selection of appropriate models and predictor variables is critical. However, there is a major challenge in quantifying and mapping LAI using multi-spectral sensors due to the saturation effects of traditional vegetation indices (VIs) for mangrove forests. WorldView-2 (WV2) imagery has proven to be effective to estimate LAI of grasslands and forests, but the sensitivity of its vegetation indices (VIs) has been uncertain for mangrove forests. Furthermore, the single model may exhibit certain randomness and instability in model calibration and estimation accuracy. Therefore, this study aims to explore the sensitivity of WV2 VIs for estimating mangrove LAI by comparing artificial neural network regression (ANNR), support vector regression (SVR) and random forest regression (RFR). The results suggest that the RFR algorithm yields the best results (RMSE = 0.45, 14.55% of the average LAI), followed by ANNR (RMSE = 0.49, 16.04% of the average LAI), and then SVR (RMSE = 0.51, 16.56% of the average LAI) algorithms using 5-fold cross validation (CV) using all VIs. Quantification of the variable importance shows that the VIs derived from the red-edge band consistently remain the most important contributor to LAI estimation. When the red-edge band-derived VIs are removed from the models, estimation accuracies measured in relative RMSE (RMSEr) decrease by 3.79%, 2.70% and 4.47% for ANNR, SVR and RFR models respectively. VIs derived from red-edge band also yield better accuracy compared with other traditional bands of WV2, such as near-infrared-1 and near-infrared-2 band. Furthermore, the estimated LAI values vary significantly across different mangrove species. The study demonstrates the utility of VIs of WV2 imagery and the selected machine-learning algorithms in developing LAI models in mangrove forests. The results indicate that the red-edge band of WV2 imagery can help alleviate the saturation problem and improve the accuracy of LAI estimation in a mangrove area.

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mentioning

confidence: 99%

Exploring the Potential of WorldView-2 Red-Edge Band-Based Vegetation Indices for Estimation of Mangrove Leaf Area Index with Machine Learning Algorithms

Zhu

Liu

et al. 2017

Remote Sensing

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“…Different transfer functions, e.g., tansig, logsig and purelin were used and the performance of the ANN was optimized by changing the number of neurons in the hidden layers. The study suggested the need of critical analysis of the backpropagation ANN in terms of selection of the best transfer function and other network parameters for better results [38].…”

Section: Spinachmentioning

confidence: 99%

Artificial neural networks (ANNs) in food science – A review

Goyal¹

2013

International Journal of Scientific World

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Artificial Neural Networks (ANNs) have been applied in several areas, viz., science and technology, engineering, agriculture, life sciences and medicine as they have a remarkable ability to provide accurate results. This communication highlights the systematic information available in the literature concerning the implementation of ANN models for predicting properties of dairy products, fruits, vegetables and meat. The information presented in this review paper shows that over the last decade research related to ANN based predictive modelling in food science has picked up.

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“…The advantage of using satellite remote sensing data over the other methods is the spatial coverage. The effectiveness of machine learning methods has been tested on test-bed [55], airborne [56], UAV [57] and field spectrometry [58] datasets for the retrieval of crop-related parameters. Table 2 shows the summary of machine learning methods based on UAV, aerial and field spectrometry remote sensing [54][55][56][57][58][59][60][61][62].…”

Section: Croplands Biomass Retrievalmentioning

confidence: 99%

“…The effectiveness of machine learning methods has been tested on test-bed [55], airborne [56], UAV [57] and field spectrometry [58] datasets for the retrieval of crop-related parameters. Table 2 shows the summary of machine learning methods based on UAV, aerial and field spectrometry remote sensing [54][55][56][57][58][59][60][61][62]. A literature review suggests that the use of machine learning methods in combination with spaceborne satellite remote sensing data is more frequent for crop classification and mapping, which is a non-quantitative approach of guessing how much biomass there is by calculating the number of pixels in each class, which are surrogates of area calculation [42,44,45], and, finally, biomass allocation.…”

Section: Croplands Biomass Retrievalmentioning

confidence: 99%

Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data

et al. 2015

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Abstract:The enormous increase of remote sensing data from airborne and space-borne platforms, as well as ground measurements has directed the attention of scientists towards new and efficient retrieval methodologies. Of particular importance is the consideration of the large extent and the high dimensionality (spectral, temporal and spatial) of remote sensing data. Moreover, the launch of the Sentinel satellite family will increase the availability of data, especially in the temporal domain, at no cost to the users. To analyze these data and to extract relevant features, such as essential climate variables (ECV), specific methodologies need to be exploited. Among these, greater attention is devoted to machine learning methods due to their flexibility and the capability to process large number of inputs and to handle non-linear problems. The main objective of this paper is to provide a review of research that is being carried out to retrieve two critically important terrestrial biophysical quantities (vegetation biomass and soil moisture) from remote sensing data using machine learning methods.

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Retrieval of spinach crop parameters by microwave remote sensing with back propagation artificial neural networks: A comparison of different transfer functions

Cited by 55 publications

References 17 publications

Exploring the Potential of WorldView-2 Red-Edge Band-Based Vegetation Indices for Estimation of Mangrove Leaf Area Index with Machine Learning Algorithms

Exploring the Potential of WorldView-2 Red-Edge Band-Based Vegetation Indices for Estimation of Mangrove Leaf Area Index with Machine Learning Algorithms

Artificial neural networks (ANNs) in food science – A review

Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data

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