Comparison of artificial neural networks and logistic regression as potential methods for predicting weed populations on dryland chickpea and winter wheat fields of Kurdistan province, Iran

Mansourian, Sahar; Darbandi, Ebrahim Izadi; Mohassel, M H Rashed; Rastgoo, Mehdi; Kanouni, Homayoun

doi:10.1016/j.cropro.2016.11.015

Cited by 24 publications

(29 citation statements)

References 33 publications

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“…Therefore, it is possible that the inputs and outputs of the problem in this study exhibited a nonlinear relationship. Depending on the attribute examined, many researchers have used the sigmoid function for modeling in their studies (Emamgholizadeh et al, ; Mansouri, Fadavi, & Mortazavian, ; Mansourian, Izadi Darbandi, Rashed Mohasse, Rastgoo, & Kanouni, ). This seems to be explained by the high ability of this function to cover nonlinear variations compared with other transfer functions.…”

Section: Resultsmentioning

confidence: 99%

Modeling Oil Content of Sesame (Sesamum indicum L.) Using Artificial Neural Network and Multiple Linear Regression Approaches

Abdipour

Ramazani

Younessi-Hmazekhanlu

et al. 2018

J Americ Oil Chem Soc

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Sesame (Sesamum indicum L.) is an important ancient oilseed crop with high oil content (OC) and quality. The direct selection to improve OC of sesame (OCS) due to low heritability leads to a low profit. The OCS modeling and indirect selection through high-heritable characters associated with OCS using advanced modeling techniques is a beneficial approach to overcome this limitation that allows breeder to get a better idea of the plant properties that should be monitored during breeding experiments. This study, carried out in 2013 and 2014, compared the potential of artificial neural network (ANN) and multilinear regression (MLR) to predict OCS in the Imamzadeh Jafar plain of Gachsaran, Iran. Principal component analysis (PCA) and stepwise regression (SWR) were used to evaluate 18 input variables. Based on PCA and SWR, the 6 traits of number of capsules per plant (NCP), number of days from flowering to maturity (NDFM), plant height (PH), thousand seed weight, capsule length, and seed yield were chosen as input variables. The network with the sigmoid axon transfer function and 2 hidden layers was selected as the final ANN model. Results showed that the ANN predicted the OCS with more accuracy and efficacy (R 2 = 0.861, root mean square error [RMSE] = 0.563, and mean absolute error [MAE] = 0.432) compared with the MLR model (R 2 = 0.672, RMSE = 0.742, and MAE = 0.552). These results showed the potential of the ANN as a promising tool to predict OCS with good performance. Based on sensitivity tests, NCP followed by NDFM and PH, respectively, were the most influential factors in predicting OCS in both models. It seems that a breeding program to select or create long sesame genotypes with a long period from flowering to maturity can be a good approach to address OCS in the future. Keywords Artificial neural networks Á Diallel cross Á Multiple regression Á Oil content Á Principal component analysis Á Sesame J Am Oil Chem Soc (2018) 95: 283-297.

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Section: Resultsmentioning

confidence: 99%

Modeling Oil Content of Sesame (Sesamum indicum L.) Using Artificial Neural Network and Multiple Linear Regression Approaches

Abdipour

Ramazani

Younessi-Hmazekhanlu

et al. 2018

J Americ Oil Chem Soc

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“…For example, discriminant analysis was used to investigate the effect of rainfall-related variables on the occurrence of drought stress in maize (Zhang et al 2013) and the effect of fertilizer regimes on the structure of the soil microbial community and its functions (Lazcano et al 2013). Recently, a comparison of artificial neural networks and logistic regression was used to predict weed populations in chickpea and winter wheat (Mansourian et al 2017) and to investigate the contribution of topographic and soil-related traits, as well as land use and maximum rainfall intensity as landslide drivers in landslide susceptibility mapping (Gong et al 2018). The use of different approaches ensures a reliable depiction of the examined system as each approach relies on different assumptions and analytical solutions.…”

Section: Introductionmentioning

confidence: 99%

Epidemiology and agronomic predictors of herbicide resistance in rice at a large scale

Mascanzoni

Perego

Marchi³

et al. 2018

Agron. Sustain. Dev.

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Herbicide resistance is a major weed control issue that threatens the sustainability of rice cropping systems. Its epidemiology at large scale is largely unknown. Several rice weed species have evolved resistant populations in Italy, including multiple resistant ones. The study objectives were to analyze the impact in Italian rice fields of major agronomic factors on the epidemiology of herbicide resistance and to generate a large-scale resistance risk map. The Italian Herbicide Resistance Working Group database was used to generate herbicide resistance maps. The distribution of resistant weed populations resulted as not homogeneous in the area studied, with two pockets where resistance had not been detected. To verify the situation, random sampling was done in the pockets where resistance had never been reported. Based on data from 230 Italian municipalities, three different statistics, stepwise discriminant analysis, stepwise logistic regression, and neural network, were used to correlate resistance distribution in the main Italian rice growing area with seeding type, rotation rate, and soil texture. Through the integration of complaint monitoring, mapping, and neural network analyses, we prove that a high risk of resistance evolution is associated with traditional rice cropping systems with intense monoculture rates and where water-seeding is widespread. This is the first study that determines the degree of association between herbicide resistance and a few important predictors at large scale. It also demonstrates that resistance is present in areas where it had never been reported through extensive complaint monitoring. However, these resistant populations cause medium-low density infestations, likely not alarming rice farmers. This highlights the importance of integrated agronomic techniques at cropping system level to prevent the diffusion and impact of herbicide resistance or limit it to an acceptable level. The identification of concise, yet informative, agronomic predictors of herbicide resistance diffusion can significantly facilitate effective management and improve sustainability.

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“…Also, Lal and Varma (2014) constructed a model to identify functional aspects of cereal proteins according to structural composition. Goyal (2013) mentions the great support that ANN's represent for the cereal sector, as well as intelligent models that have superiority of precision of results on the traditional models (Beigi et al, 2016;Mansourian et al, 2017).…”

Section: Traditional Technological Strategiesmentioning

confidence: 99%

“…In the occurrence of weeds, prediction models are also developed for rice crops according to Barrero et al (2016) for wheat crops by Mansourian et al (2017).…”

Section: Traditional Technological Strategiesmentioning

confidence: 99%

Sustainable and technological strategies for basic cereal crops in the face of climate change: A literature review

GutiÃ©rrez¹,

Palacios²,

Ruiz-Vanoye³

et al. 2018

Afr. J. Agric. Res.

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At the international level, corn, wheat and rice are basic grains considered to be the most important food source for humans, as they are a fundamental part of the daily diet and represent more than 55% of the caloric intake. They make up the greater production and consumption in the world. Due to Climate Change, these highly vulnerable crops to extreme temperatures have suffered a reduction in quality and quantity of yields. In addition, there is an increase in the risk of production especially for small farmers who provide more than 75% of the world production. According to the projections for the year 2050, basic cereals will continue to be essential for food security and global survival. In turn, the temperature will continue to increase and will cause a decrease of up to 10% in yield for each increased degree celsius, in the absence of sustainable adaptation. The present work consists of a review of the literature of adaptation strategies in the face of Climate Change, which contributes to reestablishing the damage caused by the green revolution on basic cereal crops, the environment, and biodiversity, the technological strategies review was also included considering that it is a tool that offers valuable support to the farmer in the decision making inherent in the planning and improvement of their cereal crops.

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Comparison of artificial neural networks and logistic regression as potential methods for predicting weed populations on dryland chickpea and winter wheat fields of Kurdistan province, Iran

Cited by 24 publications

References 33 publications

Modeling Oil Content of Sesame (Sesamum indicum L.) Using Artificial Neural Network and Multiple Linear Regression Approaches

Modeling Oil Content of Sesame (Sesamum indicum L.) Using Artificial Neural Network and Multiple Linear Regression Approaches

Epidemiology and agronomic predictors of herbicide resistance in rice at a large scale

Sustainable and technological strategies for basic cereal crops in the face of climate change: A literature review

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