Prediction of sunflower grain yield under normal and salinity stress by RBF, MLP and, CNN models

Khalifani, Sanaz; Darvishzadeh, Reza; Azad, Nasrin; Rahmani, Razgar Seyed

doi:10.1016/j.indcrop.2022.115762

Cited by 31 publications

(10 citation statements)

References 60 publications

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“…Each layer contains a number of neurons and nodes. Each neuron in this structure is fully connected to all neurons in the following layer or layers (Zhao et al, 2009;Khalifani et al, 2022) and has its own weight. Equation 1 represents the input function 𝑢, which computes the weighted sum of the input features:…”

Section: Prediction Algorithmmentioning

confidence: 99%

“…It is designed to solve the long-term dependency problem by means of short-term memory (Pak et al, 2018;Kratzert et al, 2018). Some studies tested the multi-layer perceptron (MLP) neural network and its ability to predict crop yields of winter wheat (Bhojani and Bhatt, 2020;Bazrafshan et al, 2022), blueberry (Sivanantham et al, 2022), and sunflower (Khalifani et al, 2022). They stated that activation functions in the neural network algorithms have an important impact on the model effectiveness of crop yield and other development variables.…”

Section: Introductionmentioning

confidence: 99%

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Crop Yield Prediction based on Reanalysis and Crop Phenology Data in the Agroclimatic Zones

Yeșilköy¹,

Demir²

2023

Preprint

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Crop yield and phenological stages are remarkably sensitive to not only environmental factors like atmospheric conditions and physical properties of soils but also agricultural activities. Accurate crop yield prediction plays a crucial role in food security and agricultural sustainability. There are several approaches that a wide range of researchers have tried to predict crop yield at different scales. In this study, we tested AgERA5 reanalysis product and crop phenological stage data to predict winter wheat yields in the agricultural lands of the agroclimatic regions of Turkey. The main objective is to propose a deep learning approach based on the combination of the reanalysis, which was extracted for the agricultural lands of the five most productive agroclimatic zones, and crop phenology data to predict winter wheat yields. Five performance indicators, such as normalized root mean squared error (NRMSE), mean absolute percentage error (MAPE), root mean squared error (RMSE), Nash-Sutcliffe Efficiency (NSE), and coefficient of determination (R2), are chosen to test the model’s accuracy and effectiveness. We have obtained promising findings and suggested that AgERA5 reanalysis data can be used as an input for the crop yield prediction of winter wheat with an error below 10% and a coefficient of determination above 0.9.

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Section: Prediction Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Crop Yield Prediction based on Reanalysis and Crop Phenology Data in the Agroclimatic Zones

Yeșilköy¹,

Demir²

2023

Preprint

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show abstract

“…If error quotient value is less than 1, a variable maybe removed from the model to improve model quality. Rank showed the numerical order of the input variables by declining error, i.e., a rank of 1 indicated the greatest significance for the network [32,33] .…”

Section: Sensitivity Analysismentioning

confidence: 99%

Prediction of <i>Wedelia trilobata</i> Growth under Flooding and Nitrogen Enrichment Conditions by Using Artificial Neural Network Model

Azeem,

Wenxuan,

Changyan

et al. 2023

Pol. J. Environ. Stud.

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The objective of this study is to produce multi-criteria model for the dry weight prediction of Wedelia trilobata under flooding and nitrogen conditions. Plants of W. trilobata were grown in a greenhouse, and treatments were given for two months. Growth parameters of 60 plants were used to build a numerical model. The neural network model was built using Quasi-Newton approaches that containing Broydenfletcher-goldfarb-shanno gradient (BFGS) learning algorithm, multilayer perceptron (MLP) training algorithm and sigmoid axon transfer function along with 10 neurons at the input network, 9 neurons in the hidden layer, and 1 neuron in the output layer (10-9-1). The selection and validation of the best predictor model were based on lower values of errors and higher value of R 2 . The selected model had a higher values of R 2 = 0.90 and lower values of errors i.e (relative approximate error, RAE = 0.004, root mean square error, RMS = 0.027, mean absolute error, MAE = 0.004, mean absolute percentage error, MAPE = 0.013). Moreover, the highest rank 1 was obtained for leaf area during sensitivity analysis followed by water potential and photosynthesis ranked 2 rd and 3 th , respectively. The constructed model of W. trilobata under flooding and nitrogen conditions is the new feature in the management of invasive plant species and gives direction to control its spread.

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“…2 Related works Currently, various identification methods are gradually applied to crop disease image identification (Pantazi et al, 2019;Zeng and Li, 2020). We categorize these methods into traditional methods, machine learning methods, and deep learning methods (Flores et al, 2021;Khalifani et al, 2022). In the following, we provide an overview and summary of these research works.…”

Section: Introductionmentioning

confidence: 99%

Dual-branch collaborative learning network for crop disease identification

et al. 2023

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Crop diseases seriously affect the quality, yield, and food security of crops. redBesides, traditional manual monitoring methods can no longer meet intelligent agriculture’s efficiency and accuracy requirements. Recently, deep learning methods have been rapidly developed in computer vision. To cope with these issues, we propose a dual-branch collaborative learning network for crop disease identification, called DBCLNet. Concretely, we propose a dual-branch collaborative module using convolutional kernels of different scales to extract global and local features of images, which can effectively utilize both global and local features. Meanwhile, we embed a channel attention mechanism in each branch module to refine the global and local features. Whereafter, we cascade multiple dual-branch collaborative modules to design a feature cascade module, which further learns features at more abstract levels via the multi-layer cascade design strategy. Extensive experiments on the Plant Village dataset demonstrated the best classification performance of our DBCLNet method compared to the state-of-the-art methods for the identification of 38 categories of crop diseases. Besides, the Accuracy, Precision, Recall, and F-score of our DBCLNet for the identification of 38 categories of crop diseases are 99.89%, 99.97%, 99.67%, and 99.79%, respectively. 811

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Prediction of sunflower grain yield under normal and salinity stress by RBF, MLP and, CNN models

Cited by 31 publications

References 60 publications

Crop Yield Prediction based on Reanalysis and Crop Phenology Data in the Agroclimatic Zones

Crop Yield Prediction based on Reanalysis and Crop Phenology Data in the Agroclimatic Zones

Prediction of <i>Wedelia trilobata</i> Growth under Flooding and Nitrogen Enrichment Conditions by Using Artificial Neural Network Model

Dual-branch collaborative learning network for crop disease identification

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