Neural Network-Based Irrigation Control for Precision Agriculture

Capraro, Flavio; Patiño, Daniel; Tosetti, Santiago; Schugurensky, C.

doi:10.1109/icnsc.2008.4525240

Cited by 36 publications

(19 citation statements)

References 5 publications

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“…Figure 13 presents randomly selected brake intensity prediction results using the open-loop NARX network with 10 prediction horizon steps, which equal to 1 s. It can be found that the predicted MCPs are quite close to real ones. Since the open-loop NARX network follows a direct prediction strategy, the output layer is composed of measurable data of the system's output (refer to MCP), which increases the complexity of the prediction model, but more accurate results are achieved [37]. On the other hand, the prediction performance of the close-loop NARX network and LMBP apparently deteriorated with the increase of the prediction horizon.…”

Section: Performance Analysis Of Brake Intensity Predictionmentioning

confidence: 99%

Control Oriented Prediction of Driver Brake Intention and Intensity Using a Composite Machine Learning Approach

et al. 2019

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Driver perception, decision, and control behaviors are easily affected by traffic conditions and driving style, showing the tendency of randomness and personalization. Brake intention and intensity are integrated and control-oriented parameters that are crucial to the development of an intelligent braking system. In this paper, a composite machine learning approach was proposed to predict driver brake intention and intensity with a proper prediction horizon. Various driving data were collected from Controller Area Network (CAN) bus under a real driving condition, which mainly contained urban and rural road types. ReliefF and RReliefF (they don’t have abbreviations) algorithms were employed as feature subset selection methods and applied in a prepossessing step before the training. The rank importance of selected predictors exhibited different trends or even negative trends when predicting brake intention and intensity. A soft clustering algorithm, Fuzzy C-means, was adopted to label the brake intention into categories, namely slight, medium, intensive, and emergency braking. Data sets with misplaced labels were used for training of an ensemble machine learning method, random forest. It was validated that brake intention could be accurately predicted 0.5 s ahead. An open-loop nonlinear autoregressive with external input (NARX) network was capable of learning the long-term dependencies in comparison to the static neural network and was suggested for online recognition and prediction of brake intensity 1 s in advance. As system redundancy and fault tolerance, a close-loop NARX network could be adopted for brake intensity prediction in the case of possible sensor failure and loss of CAN message.

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Section: Performance Analysis Of Brake Intensity Predictionmentioning

confidence: 99%

Control Oriented Prediction of Driver Brake Intention and Intensity Using a Composite Machine Learning Approach

et al. 2019

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“…Pulido-Calvo and Gutiérrez-Estrada (2009) proposed an FIS tuned by a genetic algorithm for irrigation water demand forecasting. Capraro et al (2008) applied a neural approach to infer the water demand and time needed to take the soil moisture level to a desired value. Martí et al (2013) proposed an ANN for the estimation of W st of citrus trees under a single irrigation treatment and a limited dataset.…”

Section: Introductionmentioning

confidence: 99%

Soft computing applied to stem water potential estimation: A fuzzy rule based approach

Valdés-Vela

Abrisqueta

Conejero

et al. 2015

Computers and Electronics in Agriculture

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“…Artificial neural network, grey model are the common prediction techniques of agriculture water consumption [1][2]. However, artificial neural network (ANN) has inherent drawbacks, such as local optimization solution, lack generalization, and uncontrolled convergence.…”

Section: Introductionmentioning

confidence: 99%

Agriculture irrigation water demand forecasting based on rough set theory and weighted LS-SVM

Ding

Jinhu

2010

2010 Second International Conference on Communication Systems, Networks and Applications

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Forecasting agriculture water demand is significant to optimize confirmation of water resources. In this study, we introduce a hybrid model which combines rough set theory and least square support vector machine to forecast the agriculture irrigation water demand. Through a certain district agriculture irrigation water demand dataset experiment, we have proved that the reduction feature set exacted by RST has good subject-independence and intrinsic good separability.Weighted LS-SVM predictor demonstrated promising prediction accuracy, better generalization ability and more rapid execution speed than most of the all benchmarking methods listed in this study.

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Neural Network-Based Irrigation Control for Precision Agriculture

Cited by 36 publications

References 5 publications

Control Oriented Prediction of Driver Brake Intention and Intensity Using a Composite Machine Learning Approach

Control Oriented Prediction of Driver Brake Intention and Intensity Using a Composite Machine Learning Approach

Soft computing applied to stem water potential estimation: A fuzzy rule based approach

Agriculture irrigation water demand forecasting based on rough set theory and weighted LS-SVM

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