Wheat yield prediction: Artificial neural network based approach

Kadir, Muhd Khairulzaman Abdul; Ayob, Mohd Zaki; Miniappan, Nadaraj

doi:10.1109/ice2t.2014.7006239

Cited by 19 publications

(7 citation statements)

References 13 publications

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“…Although, many researches have been performed to specify the best models for predicting biological process/ plants yield using different types of data 19,20 . Abdipour et al 5 expressed ANN forecasted safflower seed performance with greater precision and effectiveness than the multi linear regression method.…”

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confidence: 99%

An efficient estimation of crop performance in sheep fescue (Festuca ovina L.) using artificial neural network and regression models

Khalaki

Jahantab

Abdipour

et al. 2022

Sci Rep

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Festuca ovina L. (sheep fescue), a perennial grass plant found in mountainous regions, is important from both an ecological and economic viewpoint. However, the variability of biological yield of sheep fescue due to its reliance on different characteristics makes it difficult to accurately prediction using classic modeling techniques. In this study, machine learning methods and multiple regression models (linear and non-linear) are used to investigate the interdependence of various morphological and physiological characteristics on accurate prediction of the biological yield (BY) of sheep fescue. Principal components analysis and stepwise regression were used to select six agronomic parameters i.e. thousand seed weight (TSW), relative water content (RWC), canopy cover (CC), leaf area index, number of florescence, and viability (VA), while the output variable was BY. To optimized the artificial neural network (ANN) structure, different transfer functions and training algorithms, different number of neurons in each layer, different number of hidden layers and training iteration were tested. The accuracy of the models and algorithms is analyzed by root mean square error (RMSE), mean absolute error (MAE), and determination coefficient (R2). According to the findings, ANN models were more accurate than regression models. The ANN model with two hidden layers (i.e. structure of 6–4–8–1) which had RMSE, MAE and R2 scores of 0.087, 0.065 and 0.96, respectively, was discovered as the best model for predicting the BY. In addition, result of the sensitivity analysis showed TSW, RWC and CC, in that order, were the variables most important for high-quality BY estimation in both models regardless of input combination. Finally, the paper concludes that early flowering sheep fescue genotypes with long maturation and great TSW must be regarded as the most suitable model for increasing BY in breeding projects.

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mentioning

confidence: 99%

An efficient estimation of crop performance in sheep fescue (Festuca ovina L.) using artificial neural network and regression models

Khalaki

Jahantab

Abdipour

et al. 2022

Sci Rep

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“…These findings confirm the capability of ANNs in predicting crop yield, which is particularly significant given that crop yield depends on a variety of factors. Kadir et al. (2014) demonstrated the promising potential of ANNs predict WY, suggesting that this approach can be applied to other crops as well.…”

Section: Discussionmentioning

confidence: 97%

Spatial prediction of winter wheat yield gap: agro-climatic model and machine learning approaches

Mousavi,

Jahandideh Mahjenabadi,

Khoshru

et al. 2024

Front. Plant Sci.

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This study aimed to identify the most influential soil and environmental factors for predicting wheat yield (WY) in a part of irrigated croplands in southwest Iran, using the FAO-Agro-Climate method and machine learning algorithms (MLAs). A total of 60 soil samples and wheat grain (1 m × 1 m) in 1200 ha of Pasargad plain were collected and analyzed in the laboratory. Attainable WY was assessed using the FAO method for the area. Pearson correlation analysis was used to select the best set of soil properties for modeling. Topographic attributes and vegetation indices were used as proxies of landscape components and cover crop to map actual WY in the study area. Two well-known MLAs, random forest (RF) and artificial neural networks (ANNs), were utilized to prepare an actual continuous WY map. The k-fold method was used to determine the uncertainty of WY prediction and quantify the quality of prediction accuracy. Results showed that soil organic carbon (SOC) and total nitrogen (TN) had a positive and significant correlation with WY. The SOC, TN, normalized different vegetation index (NDVI), and channel network base level (CHN) were recognized as the most important predictors and justifying more than 50% of actual WY. The ANNs outperformed the RF algorithm with an R2 of 0.75, RMSE of 400 (kg ha−1), and RPD of 2.79, according to statistical indices. The uncertainty analysis showed that the maximum uncertainty of the prediction map [400 (kg ha−1)] was very low compared to the mean value [4937 (kg ha−1)] of WY map. Calculation yield gap using the FAO-agro-climatic model showed that the average yield gap of the region was about 50% of actual yield. The findings of this study demonstrated that integrating simulated attainable crop growth using crop model and a set of soil and environmental covariates with the ANNs algorithm can effectively predict WY gaps in large areas with acceptable and reasonable accuracy. The study emphasizes that the implementation of efficient management practices has the potential to enhance agricultural production in the study area and similar regions. These results represent a significant advancement of sustainable agriculture and provide valuable insights for ensuring global food security.

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“…These findings confirm the capability of ANNs in predicting crop yield, which is particularly significant given that crop yield depends on a variety of factors. Kadir et al (2014) demonstrated the promising potential of ANNs predict WY, suggesting that this approach can be applied to other crops as well. Other studies, such as Irmak et al (2006), Drummond et al (2003), and Liu et al (2001, also reported successful outcomes in using ANNs for data mining, crop yield prediction based on soil properties, and determining target corn yields, respectively.…”

Section: Discussionmentioning

confidence: 97%

Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Plant Breeding

Orlov

2024

Frontiers Research Topics

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Wheat yield prediction: Artificial neural network based approach

Cited by 19 publications

References 13 publications

An efficient estimation of crop performance in sheep fescue (Festuca ovina L.) using artificial neural network and regression models

An efficient estimation of crop performance in sheep fescue (Festuca ovina L.) using artificial neural network and regression models

Spatial prediction of winter wheat yield gap: agro-climatic model and machine learning approaches

Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Plant Breeding

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