A Back Propagation Neural Networks for Grading &lt;i&gt;Jatropha curcas&lt;/i&gt; Fruits Maturitiy

Effendi,

doi:10.3844/ajassp.2010.390.394

Cited by 34 publications

(14 citation statements)

References 18 publications

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“…One of the most popular classes of artificial neural networks is the Multilayer Perceptrons (MLP) with the backpropagation algorithm as the training method. The MLP have been applied to several areas, for example, agriculture (Effendi et al, 2010), medicine (Benamrane et al, 2005;Isa et al, 2007;Eiamkanitchat et al, 2010), face recognition (Rizon et al, 2006), electric power systems (Benslimane et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Moisture Content Prediction of Dried Longan Aril from Dielectric Constant Using Multilayer Perceptrons and Support Vector Regression

Maher¹

2010

American Journal of Applied Sciences

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Problem statement: Estimation of moisture contents of dried food products from their dielectric constants was an important step in moisture measurement systems. The regression models that provide good prediction performance are desirable. Approach: The Multilayer Perceptrons (MLP) and Support Vector Regression (SVR) were applied in this research to predict the moisture contents of dried longan arils from their dielectric constants. The data set was collected from 1500 samples of dried longan aril with five different moisture contents of 10, 14, 18, 22 and 25% Wet basis (Wb.) Dielectric constant of dried longan aril was measured by using our previously proposed electrical capacitance-based system. The results from the MLP and SVR models were compared to that from the linear regression and polynomial regression models. To take into account the generalization of the models, the four-fold cross validation was applied. Results: For the training sets, the average mean absolute errors over three bulk densities of 1.30, 1.45 and 1.60 g cm −3 were 1.7578, 0.6157, 0.3812, 0.3113, 0.0103 and 0.0044% Wb for the linear regression, second-, third-, fourth-order polynomial regression, MLP and SVR models, respectively. For the validation sets, the average mean absolute errors over the three bulk densities were 1.7616, 0.6192, 0.3844, 0.3146, 0.0126 and 0.0093% Wb for the linear regression, 2nd, 3rd and 4th-order polynomial regression, MLP and SVR models, respectively. Conclusion: The regression models based on MLP and SVR yielded better performances than the models based on linear regression and polynomial regression on both training and validation sets. The models based on MLP and SVR also provided robustness to the variation of bulk density. Not only for dried longan aril, the proposed models can also be adapted and applied to other materials or dried food products.

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Section: Introductionmentioning

confidence: 99%

Moisture Content Prediction of Dried Longan Aril from Dielectric Constant Using Multilayer Perceptrons and Support Vector Regression

Maher¹

2010

American Journal of Applied Sciences

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“…Each subsequent layer incorporates a connection in the last layer as well as the final layer generates the network's output. Feedforward networks can easily be utilised for those sorts of input to output mapping (Effendi et al, 2010). Hence a feedforward network which has a single hidden layer together with enough neurons within the hidden layers can certainly fit any finite input-output mapping problem (Fortin et al, 2010).…”

Section: Feed Forward Neural Network Training and Linear Regression Mmentioning

confidence: 99%

Neuro-fuzzy-based smart DSS for crop specific irrigation control and SMS notification generation for precision agriculture

Mohapatra¹,

Lenka

2016

IJCONVC

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A feed forward neural network and fuzzy logic-based hybrid smart decision support system (DSS) for crop specific irrigation notification and control in precision agriculture (PA) is proposed in this paper. This proposed neuro-fuzzy smart DSS can be implemented in any farm land, green-house and poly-house for efficient irrigation management and control for PA. A feed forward neural network is trained and linear regression is performed to predict soil moisture content (MC) in hourly basis. The predicted soil MC is utilised by fuzzy logic-based smart DSS model to produce SMS notification to the farmer. The proposed DSS model can work on real-time mode using National Instruments LabVIEW. This hybrid smart DSS prediction algorithm is implemented using data group of 24 cases measured in the farming land located in Bhubaneswar, the southern part of India. Crop wise evapotranspiration is also calculated using Blaney-Criddle method to notify the farmers via SMS service.Keywords: feedforward neural network; linear regression; LabVIEW; decision support system; DSS; fuzzy logic; SMS notification; GSM modem interface.Reference to this paper should be made as follows: Mohapatra, A.G. and Lenka, S.K. (2016) This paper is a revised and expanded version of a paper entitled 'Hybrid decision model for weather dependent farm irrigation using resilient backpropagation based neural network pattern classification and fuzzy logic' presented at

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“…This work has used supervised learning with back propagation neural network. For pattern classification, Back propagation neural networks architecture is most commonly used neural networks [13][14] [15]. Another reason to chose back propagation due to its ability to perform pattern classification on data where the input and the output had no linear relationship, as in the case of this application [16].…”

Section: Classifier Designmentioning

confidence: 99%

Domain Specific Knowledge based Machine Learning for Flower Classification using Soft Computing

Sarkate¹,

Khanale²

2014

IJCA

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Machine learning is ability of the machine to capture the data, analyze it and make decision as like human being perform in everyday life. Sometimes it also referred as pattern recognition or classification. With use of latest computing technology and soft computing, it is now possible for machines to act with intelligence as human. Various industries like automobile, medical diagnosis are using machines for fast and accurate data capturing and analyzing.Our study is conducted to justify these machine abilities in the field of floriculture. A knowledge base in flower domain is used for the Intra-class sorting purpose. The flower sorter is designed to capture the flower images and with artificial neural network classifier, the images are classified in four flower color classes. In the study, supervised learning algorithm is used for machine learning. The classification accuracy of flower sorter is found to be 98%.

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A Back Propagation Neural Networks for Grading <i>Jatropha curcas</i> Fruits Maturitiy

Cited by 34 publications

References 18 publications

Moisture Content Prediction of Dried Longan Aril from Dielectric Constant Using Multilayer Perceptrons and Support Vector Regression

Moisture Content Prediction of Dried Longan Aril from Dielectric Constant Using Multilayer Perceptrons and Support Vector Regression

Neuro-fuzzy-based smart DSS for crop specific irrigation control and SMS notification generation for precision agriculture

Domain Specific Knowledge based Machine Learning for Flower Classification using Soft Computing

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