Artificial neural networks and multiple linear regression as potential methods for modeling seed yield of safflower (Carthamus tinctorius L.)

Abdipour, Moslem; Younessi-Hmazekhanlu, Mehdi; Ramazani, Seyyed Hamid Reza; Omidi, Arash

doi:10.1016/j.indcrop.2018.10.050

Cited by 90 publications

(67 citation statements)

References 41 publications

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“…It was shown that all parameters of the model, i.e., determination coefficient (R 2 ), mean absolute error (MAE), and root mean square error (RMS) are better for the ANN model than for MLR. Similarly, in [8], the ANN and MLR methods were used to produce a model of safflower yield (Carthamus tinctorius L.). The results of analyses (R 2 , MAE, RMS) also confirm better results for ANN models than MLR models.…”

Section: Introductionmentioning

confidence: 99%

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Application of Artificial Neural Networks for Multi-Criteria Yield Prediction of Winter Rapeseed

Niedbała

2019

Sustainability

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The aim of the work was to produce three independent, multi-criteria models for the prediction of winter rapeseed yield. Each of the models was constructed in such a way that the yield prediction can be carried out on three dates: April 15th, May 31st, and June 30th. For model building, artificial neural networks with multi-layer perceptron (MLP) topology were used, on the basis of meteorological data (temperature and precipitation) and information about mineral fertilisation. The data were collected from the years, 2008–2015, from 328 production fields located in Greater Poland, Poland. An assessment of the quality of forecasts produced based on neural models was verified by determination of forecast errors using RAE (relative approximation error), RMS (root mean square error), MAE (mean absolute error) error indicators, and MAPE (mean absolute percentage error). An important feature of the produced prediction models is the ability to realize the forecast in the current agrotechnical year on the basis of the current weather and fertiliser information. The lowest MAPE error values were obtained for the neural model WR15_04 (April 15th) based on the MLP network with structure 15:15-18-11-1:1, which reached 7.51%. Other models reached MAPE errors of 7.85% for model WR31_05 (May 31st) and 8.12% for model WR30_06 (June 30th). The performed sensitivity analysis gave information about the factors that have the greatest impact on winter rapeseed yields. The highest rank of 1 was obtained by two networks for the same independent variable in the form of the sum of precipitation within a period from September 1st to December 31st of the previous year. However, in model WR15_04, the highest rank obtained a feature in the form of a sum of molybdenum fertilization in the current year (MO_CY). The models of winter rapeseed yield produced in the work will be the basis for the construction of new forecasting tools, which may be an important element of precision agriculture and the main element of decision support systems.

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Section: Introductionmentioning

confidence: 99%

“…Non-linear models are becoming more and more popular in practice. A particular increase can be observed in the use of artificial neural networks in agriculture, where better analysis results are often obtained than with classical statistical methods [1,[6][7][8][11][12][13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Neural Networks for Multi-Criteria Yield Prediction of Winter Rapeseed

Niedbała

2019

Sustainability

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“…Yield prediction methods reported in literature include, regression, simulation, expert systems, and artificial neural network (ANN). Regression models have been widely used in various studies particularly for prediction purposes [9,10]. These could be attributed to the fact that they are easy to use and often produce reliable standard tests [11].…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Neural Network for Predicting Maize Production in South Africa

et al. 2019

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The use of crop modeling as a decision tool by farmers and other decision-makers in the agricultural sector to improve production efficiency has been on the increase. In this study, artificial neural network (ANN) models were used for predicting maize in the major maize producing provinces of South Africa. The maize production prediction and projection analysis were carried out using the following climate variables: precipitation (PRE), maximum temperature (TMX), minimum temperature (TMN), potential evapotranspiration (PET), soil moisture (SM) and land cultivated (Land) for maize. The analyzed datasets spanned from 1990 to 2017 and were divided into two segments with 80% used for model training and the remaining 20% for testing. The results indicated that PET, PRE, TMN, TMX, Land, and SM with two hidden neurons of vector (5,8) were the best combination to predict maize production in the Free State province, whereas the TMN, TMX, PET, PRE, SM and Land with vector (7,8) were the best combination for predicting maize in KwaZulu-Natal province. In addition, the TMN, SM and Land and TMN, TMX, SM and Land with vector (3,4) were the best combination for maize predicting in the North West and Mpumalanga provinces, respectively. The comparison between the actual and predicted maize production using the testing data indicated performance accuracy adjusted R2 of 0.75 for Free State, 0.67 for North West, 0.86 for Mpumalanga and 0.82 for KwaZulu-Natal. Furthermore, a decline in the projected maize production was observed across all the selected provinces (except the Free State province) from 2018 to 2019. Thus, the developed model can help to enhance the decision making process of the farmers and policymakers.

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“…In particular, tools based on artificial intelligence-artificial neural networks (ANN), giving significantly lower prediction errors than in case of statistical methods-are very popular. Therefore, crop yield models are implemented in computer applications for precision agriculture and are becoming an important element of decision support systems [9][10][11][12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Neural Networks for Yield Modeling of Winter Rapeseed Based on Combined Quantitative and Qualitative Data

et al. 2019

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Rapeseed is considered as one of the most important oilseed crops in the world. Vegetable oil obtained from rapeseed is a valuable raw material for the food and energy industry as well as for industrial applications. Compared to other vegetable oils, it has a lower concentration of saturated fatty acids (5%–10%), a higher content of monounsaturated fatty acids (44%–75%), and a moderate content of alpha-linolenic acid (9%–13%). Overall, rapeseed is grown in all continents on an industrial scale, so there is a growing need to predict yield before harvest. A combination of quantitative and qualitative data were used in this work in order to build three independent prediction models, on the basis of which yield simulations were carried out. Empirical data collected during field tests carried out in 2008–2015 were used to build three models, QQWR15_4, QQWR31_5, and QQWR30_6. Each model was composed of a different number of independent variables, ranging from 21 to 27. The lowest MAPE (mean absolute percentage error) yield prediction error corresponded to QQWR31_5, it was 6.88%, and the coefficient of determination R2 was 0.69. As a result of the sensitivity analysis of the neural network, the most important independent variable influencing the final rapeseed yield was indicated, and for all the analyzed models it was “The kind of sowing date in the previous year” (KSD_PY).

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Artificial neural networks and multiple linear regression as potential methods for modeling seed yield of safflower (Carthamus tinctorius L.)

Cited by 90 publications

References 41 publications

Application of Artificial Neural Networks for Multi-Criteria Yield Prediction of Winter Rapeseed

Application of Artificial Neural Networks for Multi-Criteria Yield Prediction of Winter Rapeseed

Application of Artificial Neural Network for Predicting Maize Production in South Africa

Application of Artificial Neural Networks for Yield Modeling of Winter Rapeseed Based on Combined Quantitative and Qualitative Data

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