Kernel Ridge Regression Hybrid Method for Wheat Yield Prediction with Satellite-Derived Predictors

Ahmed, A. A. Masrur; Sharma, Ekta; Jui, Shangling; Deo, Ravinesh C.; Nguyen‐Huy, Thong; Ali, Mumtaz

doi:10.3390/rs14051136

Cited by 27 publications

(12 citation statements)

References 98 publications

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“…Even though more conventional methods of statistical modelling are capable of producing reliable models, the application of artificial intelligence (AI) techniques could be able to facilitate the development of high-quality prediction models [11], [12]. The authors of this paper propose a machine learning solution in the form of a multi-layer perceptron (MLP) artificial neural network (ANN) [13] in order to illustrate the progression of the disease.…”

Section: Related Workmentioning

confidence: 99%

Predicting psycho-somatic disorders in online activity using multi-layer perceptron

Gadiparthi,

Reddy

2024

IJ-AI

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<p>Internet services such as social media, blogs, and websites make it possible for people to acquire knowledge instantly. Due to these websites, it is now considerably easier to communicate information. As a result, individuals increasingly devote a higher amount of time to social networking programmes. This study provides estimates about the potential future ramifications of how individuals will utilise social networks. This work presents an accurate and applicable model for forecasting undesirable consequences. The model is of sufficient quality to be useful. This has been the case throughout. Using the model that has been proposed, significant properties are identified from datasets. After recovering the properties, they are categorised using the complicated computational method of multi-layer perceptron-based (MLP) artificial neural networks (ANN). 70% of this data was utilised during the training phase of the machine learning algorithm, while the remaining 30% was utilised during the validation phase of model construction. The proposed model's results were compared to those of more standard machine learning techniques. The approach utilises social networks to predict the issue. The simulation results indicate that the suggested model generates more precise predictions than the support vector machine, logistic regression, and random forest decision tree classifier techniques combined.</p>

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Section: Related Workmentioning

confidence: 99%

Predicting psycho-somatic disorders in online activity using multi-layer perceptron

Gadiparthi,

Reddy

2024

IJ-AI

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“…Table 1 provides a comparative analysis of the advantages and disadvantages of regression algorithms [14][15][16][17][18] for rice nutrient prediction. These algorithms effectively capture both linear and nonlinear correlations among various nutrients.…”

Section: Literature Reviewmentioning

confidence: 99%

Concentration Prediction‐Based Crop Digital Twin Using Nutrient Co‐Existence and Composition in Regression Algorithms

Ghazvini,

Sharef

2023

Preprint

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Crop digital twin is redefining traditional farming practices, offering unprecedented opportunities for real-time monitoring, predictive and simulation analysis, and optimization. This research embarks on an exploration of the synergy between precision agriculture, crop modeling, and regression algorithms to create a digital twin for augmenting farmers the concentration and composition prediction-based crop nutrient recovery. This captures the holistic representation of crop characteristics, considering the intricate relationships between environmental factors, nutrient concentrations, and crop compositions. However, the complexity arising from diverse soil and environmental conditions makes nutrient content analysis expensive and time-consuming. This paper presents the result of a predictive digital twin case study that employs six regression algorithms namely Elastic Net, Polynomial, Stepwise, Ridge, Lasso, and Linear Regression to predict rice nutrient content efficiently, particularly considering the coexistence and composition of multiple nutrients. Our research findings highlight the superiority of the Polynomial Regression model in predicting nutrient content, with a specific focus on accurate nitrogen percentage prediction. This insight can be used for nutrient recovery intervention by knowing the precise amount of nutrient to be added into the crop medium. The adoption of the Polynomial Regression model offers a valuable tool for nutrient management practices in the crop digital twin, potentially resulting in higher-quality rice production and a reduced environmental impact. The proposed method can be replicable in other low-resourced crop digital twin system.

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“…The crop yield prediction along with the prediction of climate change will be useful for the farmers in cultivation. The major challenge of crop prediction is climate change as the weather decides the crops yield [4]. Over the last few years, various researchers have concentrated on the improvement of crop yield prediction through a variety of methods such as crop growth process-oriented methods and statistical empirical models [5].…”

Section: Introductionmentioning

confidence: 99%

Agriculture crop yield prediction using inertia based cat swarm optimization

Reddy,

Madapuri

2024

IJECE

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Crop yield prediction is among the most important and main sources of income in the Indian economy. In this paper, the improved cat swarm optimization (ICSO) based recurrent neural network (RNN) model is proposed for crop yield prediction using time series data. The inertia weight parameter is added to position equation that is selected randomly, and a new velocity equation is produced which enhances the searching ability in the best cat area. By using inertia weight, the ICSO enhances performance of feature selection and obtains better convergence in minimum iteration. The RNN is applied to produce direct graph using sequence of data and decides current layer output by involving all other existing calculations. The performance of the model is estimated using coefficient of determination (R2), root mean square error (RMSE), mean squared error (MSE), and mean absolute error (MAE) on the yield from the years 2011 to 2021 with an annual prediction for 120 records of approximately 8 million nuts. The evaluated result shows that the proposed ICSO-RNN model delivers metrics such as R2, MAE, MSE, and RMSE values of 0.99, 0.77, 0.68, and 0.82 correspondingly, which ensures accurate yield prediction when compared with the existing methods which are hybrid reinforcement learning-random forest (RL-RF) and machine learning (ML) methods.

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Kernel Ridge Regression Hybrid Method for Wheat Yield Prediction with Satellite-Derived Predictors

Cited by 27 publications

References 98 publications

Predicting psycho-somatic disorders in online activity using multi-layer perceptron

Predicting psycho-somatic disorders in online activity using multi-layer perceptron

Concentration Prediction‐Based Crop Digital Twin Using Nutrient Co‐Existence and Composition in Regression Algorithms

Agriculture crop yield prediction using inertia based cat swarm optimization

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