Hybrid prediction strategy to predict agricultural information

Shastry, K. Aditya; Sanjay, H. A.

doi:10.1016/j.asoc.2020.106811

Cited by 16 publications

(1 citation statement)

References 20 publications

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“…An additional advantage of artificial neural networks is the possibility of using qualitative (linguistic) variables without the need to code them first, as is the case with conventional statistical techniques [51]. One of the widely used comparative methods for ANN-based analyses is multiple linear regression (MLR) [52][53][54][55][56][57]. Many studies have demonstrated the advantage of ANNs over MLR in forecasting crop yields.…”

Section: Introductionmentioning

confidence: 99%

Selection of Independent Variables for Crop Yield Prediction Using Artificial Neural Network Models with Remote Sensing Data

Hara

Piekutowska

Niedbała

2021

Land

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Knowing the expected crop yield in the current growing season provides valuable information for farmers, policy makers, and food processing plants. One of the main benefits of using reliable forecasting tools is generating more income from grown crops. Information on the amount of crop yielding before harvesting helps to guide the adoption of an appropriate strategy for managing agricultural products. The difficulty in creating forecasting models is related to the appropriate selection of independent variables. Their proper selection requires a perfect knowledge of the research object. The following article presents and discusses the most commonly used independent variables in agricultural crop yield prediction modeling based on artificial neural networks (ANNs). Particular attention is paid to environmental variables, such as climatic data, air temperature, total precipitation, insolation, and soil parameters. The possibility of using plant productivity indices and vegetation indices, which are valuable predictors obtained due to the application of remote sensing techniques, are analyzed in detail. The paper emphasizes that the increasingly common use of remote sensing and photogrammetric tools enables the development of precision agriculture. In addition, some limitations in the application of certain input variables are specified, as well as further possibilities for the development of non-linear modeling, using artificial neural networks as a tool supporting the practical use of and improvement in precision farming techniques.

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Section: Introductionmentioning

confidence: 99%

Selection of Independent Variables for Crop Yield Prediction Using Artificial Neural Network Models with Remote Sensing Data

Hara

Piekutowska

Niedbała

2021

Land

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Bioinformatics as a Powerful Tool to Foster Plant Science Research and Crop Breeding Through Its Involvement in a Multidisciplinary Research Activity

Essemine,

Xu,

Chen

et al. 2024

Bioinformatics for Plant Research and Crop Breeding

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Crop yield forecasting by long short‐term memory network with Adam optimizer and Huber loss function in Andhra Pradesh, India

Dwaram

Madapuri

2022

Concurrency and Computation

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Summary In recent times, crop yield prediction gains more attention among researchers communities to expand food production. In this article, an effort is done for crop yield prediction in Andhra Pradesh state (India), because AP highly contributes to the Indian economy. Especially, the crop yield prediction is done for Western Godavari, Prakasam, Guntur, Srikakulam, Krishna, Vizianagaram, Visakhapatnam, and Nellore districts of Andhra Pradesh (India) by estimating the rainfall based on the attributes: average temperature, minimum temperature, maximum temperature, precipitation, cloud cover, and velocity potential. After collecting the data from the Telangana agricultural report (2017–2018), the collected data are preprocessed utilizing the min‐max normalization technique, which is effective in preserving the relationship among data. The normalized data are classified using long short term memory network with Adam optimizer and Huber loss function. The proposed classifier has adaptive learning rates for dissimilar parameters by identifying the first and second‐moment gradient estimation so it has better computational effectiveness, unlike other classifiers. The proposed classifier predicts each site in the collected dataset into one of the three crops: rice, sugarcane, and onion. Finally, the agriculture experts validate the obtained results of the proposed model.

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Hybrid prediction strategy to predict agricultural information

Cited by 16 publications

References 20 publications

Selection of Independent Variables for Crop Yield Prediction Using Artificial Neural Network Models with Remote Sensing Data

Selection of Independent Variables for Crop Yield Prediction Using Artificial Neural Network Models with Remote Sensing Data

Bioinformatics as a Powerful Tool to Foster Plant Science Research and Crop Breeding Through Its Involvement in a Multidisciplinary Research Activity

Crop yield forecasting by long short‐term memory network with Adam optimizer and Huber loss function in Andhra Pradesh, India

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