Artificial neural network model in predicting yield of mechanically transplanted rice from transplanting parameters in Bangladesh

Basir,; Chowdhury, M. A. H.; Islam, Nafiul; Ashik-E-Rabbani, Muhammad

doi:10.1016/j.jafr.2021.100186

Cited by 22 publications

(14 citation statements)

References 19 publications

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“…Artificial intelligence technology is also known as machine intelligence [ 7 , 8 ]. Usually, artificial intelligence refers to human intelligence technology achieved by means of ordinary computer programs.…”

Section: Artificial Intelligence Technologymentioning

confidence: 99%

[Retracted] Application Model of Public Health Visual Art Creation Concept Based on Artificial Intelligence Technology in Venice Biennale

Xie

2022

Journal of Environmental and Public Health

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There are some problems in the creation process of public health visual art works, such as low accuracy and poor content quality. In order to further improve the accuracy and quality of artistic creation, based on artificial intelligence technology, a neural network model and an error backpropagation algorithm are used to analyze artistic creation, so as to obtain the corresponding optimization model. This model can analyze the application of public health visual art creation concepts in the Venice Biennale, so as to obtain the corresponding model calculation results under different indicators. Finally, data comparison is used to verify the accuracy of the model. Relevant studies show that activation values have different trends in connection weights under the action of different factors. The fluctuation of the curve is obvious in the initial state, but when the factor is high, the corresponding connection weight tends to be stable gradually. The range of learning rate in the initial state is relatively small, and the connection weights show a V-shaped change at higher factors. It can be seen from the curve corresponding to the ω learning method that the increase in learning speed will lead to further compression of calculation time, and the curve shows a trend of fluctuation. The corresponding learning error decreases gradually with the increase in factors, which indicates that higher factors will promote the accuracy of connection weights. Artificial intelligence-based visual models of art can calculate public health. It can be seen from the calculation results that the curve corresponding to artistic connotation has the largest range of variation and the highest influence on the model. The theme, form, and style of art all show a linear trend of improvement with the increase in time, while the content of art shows a downward trend. This research can provide support for the application of artificial intelligence theory in the field of public health.

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Section: Artificial Intelligence Technologymentioning

confidence: 99%

[Retracted] Application Model of Public Health Visual Art Creation Concept Based on Artificial Intelligence Technology in Venice Biennale

Xie

2022

Journal of Environmental and Public Health

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“…RF models leverage an ensemble of decision trees to make robust predictions, while ANNs models simulate the interconnectedness of neurons in the human brain to capture complex relationships ( Liu et al., 2012 ). These algorithms have been successfully applied in various agricultural contexts, such as digital soil mapping ( Rostaminia et al., 2021 ; Mousavi et al., 2022 ; Khosravani et al., 2023 ; Rezaei et al., 2023 ), showcasing their effectiveness in predicting crop yields based on environmental factors and soil properties ( Taghizadeh-Mehrjardi et al., 2020 ; Wang et al., 2020 ; Basir et al., 2021 ). As regards, Boori et al.…”

Section: Introductionmentioning

confidence: 99%

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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“…[21] proposed a 3D convolutional neural network for rice yield prediction and [22], [23] developed a rice yield estimation model using Convolutional Neural Networks CNN. Another prediction model based on artificial neural network is developed by [24]. However, since the prediction model uses the previous state of a variable, it is important to remember each input over time.…”

Section: Introductionmentioning

confidence: 99%

Deep Predictive Models Based on IoT and Remote Sensing Big Time Series for Precision Agriculture

Khatraty¹,

Nauwynck²,

Diallo³

et al. 2022

IJETAE

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Managing time series data generated by the intelligent objects around us requires cleaning and processing techniques, as well as a prediction model with high accuracy and low complexity. This study attempts to fuse climate time series data (Temperature, Humidity, Wind speed and Rainfall) and remote sensing data to predict rice yield. We performed statistical analysis of the data, additive decomposition, and measured the correlation between the different variables. We checked the stationarity of the data by using the Augmented Dickey-Fuller ADF test to apply statistical prediction models based on AutoRegression and moving averages for univariate series such as ARIMA (Autoregressive Integrated Moving Average) and for multivariate series such as Vector AutoRegression VAR and Vector Autoregressive Moving Average VARMAX. In addition, we applied a non-parametric model such as KNearest Neighbors KNN and Recurrent Neural Network models such as Long short-term memory LSTM and Gated recurrent unit GRU for rice yield prediction. The best yield estimation is achieved using LSTM with a Root Mean Square Error RSME error of 0.100. Keywords— Agriculture, ARIMA ,KNN, LSTM, Time series.

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Artificial neural network model in predicting yield of mechanically transplanted rice from transplanting parameters in Bangladesh

Cited by 22 publications

References 19 publications

[Retracted] Application Model of Public Health Visual Art Creation Concept Based on Artificial Intelligence Technology in Venice Biennale

[Retracted] Application Model of Public Health Visual Art Creation Concept Based on Artificial Intelligence Technology in Venice Biennale

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

Deep Predictive Models Based on IoT and Remote Sensing Big Time Series for Precision Agriculture

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