Seed yield prediction of sesame using artificial neural network

Emamgholizadeh, Samad; Parsaeian, Mahdieh; Baradaran, M

doi:10.1016/j.eja.2015.04.010

Cited by 100 publications

(59 citation statements)

References 24 publications

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“…Regression‐based models are simply based on linear and additive associations of the explanatory variables, and they have been used extensively with satisfactory results. Nevertheless, in the case of variables with complex and nonlinear relationships, these methods are not perfectly efficient (Emamgholizadeh et al, ; Khairunniza‐Bejo et al, ; Singh et al, ).…”

Section: Methodsmentioning

confidence: 99%

“…These complex relationships need nonlinear methods such as artificial neural networks (ANN), genetic expression programming (GEP), adaptive neuro‐fuzzy inference system (ANFIS), or Bayesian classification (BC) to overcome the drawbacks of linear methods (Goel, Bapat, Vyas, Tambe, & Tambe, ; Iquebal et al, ; Khoshnevisan, Rafiee, & Mousazadeh, ; Samadianfard, Nazemi, & Ashraf Sadraddini, ; Silva et al, ; Zeng, Xu, Wu, & Huang, ). In the last few decades, ANN have been widely used to predict SY in different crops like soybean, corn (Kaul, Hill, & Walthall, ), rice (Ji, Sun, Yang, & Wan, ), wheat (Alvarez, ), barley (Gholipour, Rohani, & Torani, ), sunflower (Zeng et al, ), and sesame (Emamgholizadeh, Parsaeian, & Baradaran, ) as well as genomic selection (Yong‐Jun, Lei, Wang, & Chang‐Hong, ). However, the first ANN model was presented by McCulloch and Pitts () and later extended by others.…”

Section: Introductionmentioning

confidence: 99%

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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: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

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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“…Some studies have been carried out to estimate the yield of crops by remote sensing, stochastic, ANN and simulation models based on weather, soil and growth characteristics as input data. ANN models have been reported as a useful simple prediction tool for nonlinear data such as fruit properties .…”

Section: Introductionmentioning

confidence: 99%

Using artificial neural network in determining postharvest LIFE of kiwifruit

et al. 2019

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BACKGROUND Artificial intelligence systems have been employed for the development of predictive models that estimate many agricultural processes. RESULTS In present study, the predictive capabilities of artificial neural networks (ANNs) were evaluated with respect to assessing fruit firmness as a postharvest life index, with determinations made at four stages of storage: 1, 60, 120 and 180 days after harvesting. Single concentrations of nitrogen (N), potassium (K), calcium (Ca) and magnesium (Mg) on fruit (D1), all of these nutrient concentrations (D2), the ratios of the nutrient concentrations alone (D3), and a combination of nutrient concentrations and their ratios (D4), were considered. CONCLUSION The results obtained showed that fruit firmness at 1 and 60 days after harvesting was not influenced by nutrients. However, the ANN model estimated fruit firmness of 120 and 180 days, respectively, for D1 and D3 more accurately than for the D2 and D4 datasets. Application of D3 (nitrogen/calcium ratio) as the input dataset improved predictions of fruit firmness, with a correlation coefficient of 0.85 between the measured and estimated data. © 2019 Society of Chemical Industry

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“…Sesame is grown in several countries, especially in Africa and Asia, and plays an important role in human consumption and in the pharmaceutical and cosmetic industries with the production of seeds and oil (Anilakumar et al, 2010). The oil in the seeds represents between 34% and 59% of its constitution (Emamgholizadeh et al, 2015) and it is considered noble; its application can be seen in several sectors of the industry, such as in the manufacture of pies, margarine, lubricants, soap, perfumes, and drugs (Dias et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Physical properties of sesame seeds harvested at different maturation stages and thirds of the plant

Araújo

Barbosa

Gomes

et al. 2018

Chil. j. agric. res.

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The quality of sesame (Sesamum indicum L.) seeds is directly influenced by their physical characteristics, which are seldom studied notwithstanding their extreme importance in the entire production chain. Therefore, the objective of this study was to quantify the physical properties of two types of sesame seeds, collected at three points in the plant with different maturation levels, measured with digital caliper, and to evaluate the feasibility of using a digital image to determine their physical characteristics. The seeds were initially evaluated for water content and mass of 1000 seeds, and later with the data of the characteristic dimensions A (length), B (width), and C (thickness), obtained by a caliper and digital images, we determined their sphericity, circularity, projected area, and geometric diameter. We observed that water content did not affect the physical properties of the cream (CNPA G4) and black sesame cultivars. In addition, seeds of the black cultivar presented higher results than the seeds of the cream cultivar for the mass of 1000 seeds (2.7 ± 0.08 g), sphericity (58.8 ± 0.47%), projected area (4.03 ± 0.12 mm 2 ), and geometric diameter (1.65 ± 0.03 mm). Seed batches harvested after 70% maturation had the highest mass of 1000 seeds (2.7 ± 0.12 g). The results of the physical analyses of sesame seeds performed with caliper and digital image are equivalent, which shows that digital images can be used as tools to obtain the physical properties of seeds.

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Seed yield prediction of sesame using artificial neural network

Cited by 100 publications

References 24 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

Using artificial neural network in determining postharvest LIFE of kiwifruit

Physical properties of sesame seeds harvested at different maturation stages and thirds of the plant

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