A prediction model for population occurrence of paddy stem borer (Scirpophaga incertulas), based on Back Propagation Artificial Neural Network and Principal Components Analysis

Yang, Liyuan; Lin, Peng; Zhang, Limin; Zhang, Lilian; Yang, Shisheng

doi:10.1016/j.compag.2009.06.003

Cited by 53 publications

(24 citation statements)

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“…Agrophys., 2014Agrophys., , 28, 73-83 doi: 10.2478Agrophys., /intag-2013 Modelling and analysis of compressive strength properties of parboiled paddy and milled rice The artificial neural network (ANN) method has recently been of interest to researches and engineers in various research areas and industries. ANN is increasingly being applied to process control and other areas, including the dynamic modelling of process operations, process prediction, optimizing, non-linear transformation, remote sensing technology and parameter estimation for the design of controllers (Yang et al, 2009). ANNs and rice producing have been coupled by many researchers.…”

Section: Introductionmentioning

confidence: 99%

“…An ANN model was established to predict the flow rate of paddy rice grains through orifices on horizontal rotating cylindrical drum of a hand or tractor-drawn or self-propelled drum seeder (Kumar et al, 2009). Yang et al (2009) successfully used back propagation neural network (BP-ANN) and principal components analysis (PCA) to build a prediction model for the population occurrence of paddy stem borer. A three-layer BP-ANN model which could rapidly and objectively predict the grades of milled rice based on the surface lipid content was revealed by Chen and Huang (2010).…”

Section: Introductionmentioning

confidence: 99%

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Modelling and analysis of compressive strength properties of parboiled paddy and milled rice

Nasirahmadi¹,

Abbaspour‐Fard²,

Emadi³

et al. 2014

International Agrophysics

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A b s t r a c t. The present investigation deals with analyzing the compressive strength properties of two varieties (Tarom and Fajr) of parboiled paddy and milled rice including: ultimate stress, modulus of elasticity, rupture force and rupture energy. Combined artificial neural network and genetic algorithm were also applied to model these properties. The parboiled samples were prepared with three soaking temperatures (25, 50 and 75°C) and three steaming times (10, 15 and 20 min). The samples were then dried to final moisture contents of 8, 10 and 12% (w.b.). In general, Tarom variety had higher compressive strength properties for paddy and milled rice than Fajr variety. With increase in steaming time from 10 to 20 min, all mentioned properties increased significantly, whereas these properties were decreased with increasing moisture content from 8 to 12% (w.b.). Coupled artificial neural network and genetic algorithm model with one hidden layer, three inputs (soaking temperature, steaming time and moisture content), was developed to predict the compressive strength properties as model outputs. Results indicated that this model could predict these properties with high correlation and low mean squared error.K e y w o r d s: artificial neural network, genetic algorithm, paddy, milled rice INTRODUCTION Rice (Oryza sativa L.) is the principal food cereal in the world and it is the main food of over half of the world population (Razavi and Farahmandfar, 2008). The majority of Iranian rice (75%) is grown in Mazandaran and Guilan provinces (Zareiforoush et al., 2010). The production rate is still far from the country rice self-sufficiency. Increasing the grain severity and hence reducing the processing losses could be a possible way of compenzation for the increasing demand. The primary step in rice processing is dehusking of paddy, which results in brown rice. Polishing or removing of bran to yield white rice is the second step. Knowledge of the compressive strength properties of rice is important in the design of milling equipment to predict their cracking behaviour and to minimize the losses during handling, drying, cleaning and milling processes. For this reason, extensive studies have been done in this area, that would be reviewed here. Shitanda et al. (2001) explored the physical and mechanical properties of paddy and rice. They showed that shortgrain rice was harder than long-grain variety and had less broken grain. They noted that husking characteristics of rice are related to shape and size of rice. Cao et al. (2004) explored the effect of moisture content on some mechanical properties of brown rice. They showed that moisture content has a significant effect on mechanical properties ie compressive strength and tensile strength decreased with increasing moisture content. Corrêa et al. (2007) explored mechanical behaviour of grains in rice processing. They showed that compressive force was significantly affected by processing. Zareiforoush et al. (2010) investigated the mechanical properties of two rice varieti...

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modelling and analysis of compressive strength properties of parboiled paddy and milled rice

Nasirahmadi¹,

Abbaspour‐Fard²,

Emadi³

et al. 2014

International Agrophysics

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“…In addition to commonly considered temperature variables, rainfall and the associated ambient humidity can also greatly affect survival, development, fecundity, and behavior (Willmer 1982;Guarneri et al 2002;Broufas et al 2009) and the population dynamics of insects (Barlow and Mutchmor 1963;King 1972;Mullens and Peterson 2005;Day 2006). Because of their great effects on insect populations, temperature, rainfall, and the associated ambient humidity have been widely used to forecast the likelihood and extent of pest infestation (Zhang and Zhang 2006;Yang et al 2009;Feng et al 2010), to develop strategies for establishing biocontrol agents (Fenoglio and Trumper 2007), and even to predict long-term shifts of insect populations and distribution under global climate change (Williams and Liebhold 2002;Vanhanen et al 2007;Seidl et al 2009). …”

Section: Introductionmentioning

confidence: 99%

Effect of relative humidity on population growth of Apolygus lucorum (Heteroptera: Miridae)

2011

Appl Entomol Zool

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Apolygus lucorum (Meyer-Dür) (Heteroptera: Miridae) is a significant insect pest of cotton, Chinese dates, grapes and many other crops in China, and its populations typically increase after heavy rains. However, the intrinsic mechanism of the rainfall-dependent outbreak is not yet fully understood. In our study, the effect of different relative humidity (RH), 40, 50, 60, 70, and 80% RH, on population growth of A. lucorum was evaluated in the laboratory. High humidity (e.g. 70 and 80% RH) was observed to significantly increase egg and nymph survival, prolong adult life longevity, and improve female fecundity. However, low humidity (e.g. 40 and 50% RH) led to unfavorable effects on survival and fecundity. As a result, the intrinsic capacity for increase (r m ), net production (R o ), and the finite rate of increase (k) of A. lucorum population greatly increased with increasing relative humidity. Additionally, the relationships between r m and R o and relative humidity were good fits to the logistic models y = 36.82/ (1 ? Exp(10.76 -0.19x)) (p \ 0.001) and y = 0.10/ (1 ? Exp(9.26 -0.19x)) (p = 0.003), respectively. This study provides insight into the phenology of A. lucorum, and may contribute to modeling of its population dynamics.

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“…In addition, it also shows its complexity and randomness in a system. Many different methods of chaos recognition have appeared, such as the power spectrum method [8], the Lyapunov method [9], the Poincaré section method [10] and the principal component analysis method [11]. Comparative studies show that the principal component analysis method can obtain a preferable performance among alternatives.…”

Section: A Summary Of the Chaos Recognition Methodsmentioning

confidence: 98%

A new approach to the prediction of passenger flow in a transit system

Zhao

Tong-he

Yang

et al. 2011

Computers & Mathematics with Applications

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a b s t r a c tA non-linear model is proposed for predicting the rate of passenger flow in a transit system, and its chaotic characteristic is observed. Using wavelets analysis, the passenger flow data for a whole day are decomposed in a multi-scale way to obtain decomposition sequences. Subsequently, a neural network approach is used to predict the sequences. Finally the passenger flow value can be predicted when the predicted sequences are reconstructed. Results show that the present approach is a feasible method for passenger flow prediction.

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A prediction model for population occurrence of paddy stem borer (Scirpophaga incertulas), based on Back Propagation Artificial Neural Network and Principal Components Analysis

Cited by 53 publications

References 6 publications

Modelling and analysis of compressive strength properties of parboiled paddy and milled rice

Modelling and analysis of compressive strength properties of parboiled paddy and milled rice

Effect of relative humidity on population growth of Apolygus lucorum (Heteroptera: Miridae)

A new approach to the prediction of passenger flow in a transit system

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