Artificial neural networks on integrated multispectral and SAR data for high-performance prediction of eucalyptus biomass

Domingues, Getúlio Fonseca; Soares, Vicente Paulo; Leite, Hélio Garcia; Ferraz, Antônio Santana; Ribeiro, Carlos Antônio Álvares Soares; Lorenzon, Alexandre Simões; Marcatti, Gustavo Eduardo; Teixeira, Tiago; Castro, Nero Lemos Martins de; Mota, Pedro Henrique Santos; Souza, Guilherme Silvério Aquino de; Menezes, Sady Júnior Martins da Costa de; Santos, Alexandre Rosa dos; Amaral, Cibele Hummel do

doi:10.1016/j.compag.2019.105089

Cited by 20 publications

(17 citation statements)

References 28 publications

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“…Determining the construction of the MLP has the main function in its performance [ 37 , 38 ]. In the construction of this model, it is necessary to determine the number of neurons in each layer and the number of hidden layers [ 38 ].…”

Section: Methodsmentioning

confidence: 99%

“…where x i is the i th input variable, w 0 represents bias related to the neuron of output, w j0 is bias of the j th neuron of hidden layer, f represents transfer functions for the output layer, g is the transfer functions for hidden layer, w ji is the weight connecting the j th neuron of hidden layer and the i th input variable, and w j represents weight linking the neuron of output layer and the j th neuron of hidden layer. Determining the construction of the MLP has the main function in its performance [37,38]. In the construction of this model, it is necessary to determine the number of neurons in each layer and the number of hidden layers [38].…”

Section: Mlp Modelmentioning

confidence: 99%

“…Eventually, the optimal number of neurons in the hidden layer should be calculated by using trial and error, however, the reported offers could be employed as an initiating point. A large number of neurons contributes to the complexity of the network while a low number of them makes for simplicity of the network, therefore should be noted that a too simple network results in under-fitting, and conversely, becoming too complex causes over-fitting [37,43].…”

Section: Mlp Modelmentioning

confidence: 99%

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The application of artificial neural networks in modeling and predicting the effects of melatonin on morphological responses of citrus to drought stress

Jafari

Shahsavar

2020

PLoS ONE

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Drought stress as one of the most devastating abiotic stresses affects agricultural and horticultural productivity in many parts of the world. The application of melatonin can be considered as a promising approach for alleviating the negative impact of drought stress. Modeling of morphological responses to drought stress can be helpful to predict the optimal condition for improving plant productivity. The objective of the current study is modeling and predicting morphological responses (leaf length, number of leaves/plants, crown diameter, plant height, and internode length) of citrus to drought stress, based on four input variables including melatonin concentrations, days after applying treatments, citrus species, and level of drought stress, using different Artificial Neural Networks (ANNs) including Generalized Regression Neural Network (GRNN), Radial basis function (RBF), and Multilayer Perceptron (MLP). The results indicated a higher accuracy of GRNN as compared to RBF and MLP. The great accordance between the experimental and predicted data of morphological responses for both training and testing processes support the excellent efficiency of developed GRNN models. Also, GRNN was connected to Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to optimize input variables for obtaining the best morphological responses. Generally, the validation experiment showed that ANN-NSGA-II can be considered as a promising and reliable computational tool for studying and predicting plant morphological and physiological responses to drought stress.

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Section: Methodsmentioning

confidence: 99%

Section: Mlp Modelmentioning

confidence: 99%

Section: Mlp Modelmentioning

confidence: 99%

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The application of artificial neural networks in modeling and predicting the effects of melatonin on morphological responses of citrus to drought stress

Jafari

Shahsavar

2020

PLoS ONE

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“…With respect to approaches for forest AGB estimation, machine learning regression algorithms received a lot of attention over the past decades [17][18][19]. These include the classification and regression tree (CART) learner [20] and its extensions using bagging or boosting techniques, followed by the development of random forests (RFs) and the gradient-boosted regression tree (GBRT) algorithm [7,[21][22][23].…”

Section: Introductionmentioning

confidence: 99%

“…These include the classification and regression tree (CART) learner [20] and its extensions using bagging or boosting techniques, followed by the development of random forests (RFs) and the gradient-boosted regression tree (GBRT) algorithm [7,[21][22][23]. In addition to RFs and the GBRT, the support vector regression (SVR) method [24,25], artificial neural networks (ANNs) [17], maximum entropy (MaxEnt) algorithm [5], k-nearest neighbors (kNN) method [26], and multivariate adaptive regression splines (MARS) algorithm [27,28] have been commonly used in the estimation of forest AGB. There are also some studies that compared the performances of these algorithms in retrieving forest AGB.…”

Section: Introductionmentioning

confidence: 99%

An Evaluation of Eight Machine Learning Regression Algorithms for Forest Aboveground Biomass Estimation from Multiple Satellite Data Products

Zhang

Liang

et al. 2020

Remote Sensing

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This study provided a comprehensive evaluation of eight machine learning regression algorithms for forest aboveground biomass (AGB) estimation from satellite data based on leaf area index, canopy height, net primary production, and tree cover data, as well as climatic and topographical data. Some of these algorithms have not been commonly used for forest AGB estimation such as the extremely randomized trees, stochastic gradient boosting, and categorical boosting (CatBoost) regression. For each algorithm, its hyperparameters were optimized using grid search with cross-validation, and the optimal AGB model was developed using the training dataset (80%) and AGB was predicted on the test dataset (20%). Performance metrics, feature importance as well as overestimation and underestimation were considered as indicators for evaluating the performance of an algorithm. To reduce the impacts of the random training-test data split and sampling method on the performance, the above procedures were repeated 50 times for each algorithm under the random sampling, the stratified sampling, and separate modeling scenarios. The results showed that five tree-based ensemble algorithms performed better than the three nonensemble algorithms (multivariate adaptive regression splines, support vector regression, and multilayer perceptron), and the CatBoost algorithm outperformed the other algorithms for AGB estimation. Compared with the random sampling scenario, the stratified sampling scenario and separate modeling did not significantly improve the AGB estimates, but modeling AGB for each forest type separately provided stable results in terms of the contributions of the predictor variables to the AGB estimates. All the algorithms showed forest AGB were underestimated when the AGB values were larger than 210 Mg/ha and overestimated when the AGB values were less than 120 Mg/ha. This study highlighted the capability of ensemble algorithms to improve AGB estimates and the necessity of improving AGB estimates for high and low AGB levels in future studies.

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Application of artificial intelligence models and optimization algorithms in plant cell and tissue culture

Hesami

Jones

2020

Appl Microbiol Biotechnol

134

133

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Artificial neural networks on integrated multispectral and SAR data for high-performance prediction of eucalyptus biomass

Cited by 20 publications

References 28 publications

The application of artificial neural networks in modeling and predicting the effects of melatonin on morphological responses of citrus to drought stress

The application of artificial neural networks in modeling and predicting the effects of melatonin on morphological responses of citrus to drought stress

An Evaluation of Eight Machine Learning Regression Algorithms for Forest Aboveground Biomass Estimation from Multiple Satellite Data Products

Application of artificial intelligence models and optimization algorithms in plant cell and tissue culture

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