Empirical Analysis for Crop Yield Forecasting in India

Selvamuthu, Dharmaraja; Jain, Vidyottama; Anjoy, Priyanka; Chandra, Hukum

doi:10.1007/s40003-019-00413-x

Cited by 21 publications

(11 citation statements)

References 7 publications

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“…An empirical analysis for crop yield forecasting was done by Dharmaraja et al (2020) as an attempt to focus on forecasting the yield of ‘bajra’ or the pearl millet crop through implementing appropriate statistical models such as regression and time-series models. Models like auto-regressive integrated moving average (ARIMA) and an ARIMA model with an exogenous variable (ARIMAX) were also used for prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

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An intelligent decision support system for crop yield prediction using hybrid machine learning algorithms

Anbananthen

Subbiah²,

Deisy³

et al. 2021

F1000Res

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Background: In recent times, digitization is gaining importance in different domains of knowledge such as agriculture, medicine, recommendation platforms, the Internet of Things (IoT), and weather forecasting. In agriculture, crop yield estimation is essential for improving productivity and decision-making processes such as financial market forecasting, and addressing food security issues. The main objective of the article is to predict and improve the accuracy of crop yield forecasting using hybrid machine learning (ML) algorithms. Methods: This article proposes hybrid ML algorithms that use specialized ensembling methods such as stacked generalization, gradient boosting, random forest, and least absolute shrinkage and selection operator (LASSO) regression. Stacked generalization is a new model which learns how to best combine the predictions from two or more models trained on the dataset. To demonstrate the applications of the proposed algorithm, aerial-intel datasets from the github data science repository are used. Results: Based on the experimental results done on the agricultural data, the following observations have been made. The performance of the individual algorithm and hybrid ML algorithms are compared using cross-validation to identify the most promising performers for the agricultural dataset. The accuracy of random forest regressor, gradient boosted tree regression, and stacked generalization ensemble methods are 87.71%, 86.98%, and 88.89% respectively. Conclusions: The proposed stacked generalization ML algorithm statistically outperforms with an accuracy of 88.89% and hence demonstrates that the proposed approach is an effective algorithm for predicting crop yield. The system also gives fast and accurate responses to the farmers.

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Section: Literature Reviewmentioning

confidence: 99%

“…A crop yield prediction using ML was proposed by Nishant et al (2020) . They used stacked regression for crop yield production, based on an additional factor of soil nutrients.…”

Section: Literature Reviewmentioning

confidence: 99%

An intelligent decision support system for crop yield prediction using hybrid machine learning algorithms

Anbananthen

Subbiah²,

Deisy³

et al. 2021

F1000Res

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“…The closer the fitting to the actual values, the prediction will become the most appropriate and the planning will be the best. Keeping in view the search for further best fitted model and getting motivated by many authors in the literature (Smith, 1938;Haque et al, 1988;Dharmaraja et al, 2020;Murugan et al, 2020) and the idea of the shape of the scatter plot, we have presented three growth models for the closest estimation and best forecast of peppermint yield as follows:…”

Section: Suggested Growth Modelsmentioning

confidence: 99%

“…Wen et al (2019) compared nine different growth models for describing the growth of partridges. Dharmaraja et al (2020) investigated an empirical analysis for crop yield production forecasting in India, while Lavanya et al (2020) explored a multiple linear regressions model for crop production prediction using the Adam optimizer and the Neural Network Mlraonn. Murugan et al (2020) discussed the linear regression methodology for crop yield forecasting.…”

Section: Introductionmentioning

confidence: 99%

Naive Regression Growth Models for Prediction of Peppermint Yield Production

Yadav

Sharma

Alade

et al. 2021

JRSS

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In this study, three novel regression models are introduced for estimating and forecasting peppermint yield production. Several indices of the goodness of fit are used to assess the quality of the suggested models. The proposed models for yield production are compared to current regression models that are well-known. Primary data from the Banki block of the Barabanki District of Uttar Pradesh State in India was used to validate the efficiency conditions for the suggested models to outperform the competition models. The empirical results suggest that the proposed models for estimating and predicting peppermint yield production are more efficient than competing estimators.

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“…Lately, other sophisticated methods like empirical analysis have also been applied to crop yield forecasting. Dharmaraja et al described linear regression and time-series models to predict crop yield using Bajra yield data of Alwar district in Rajasthan, India [19].…”

Section: Related Workmentioning

confidence: 99%

YieldPredict: A Crop Yield Prediction Framework for Smart Farms

Choudhary

Chukkapalli

Mittal

et al. 2020

2020 IEEE International Conference on Big Data (Big Data)

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In recent years, machine learning approaches are gaining popularity with the advent of big data. The massive amount of data generated, when served as an input to machine learning approaches, provides useful insights. Adoption of these approaches in the agricultural sector has immense potential to increase crop productivity and quality. In this paper, we analyze the crop data collected from an agriculture site in Rajasthan, India, that includes both Rabi and Kharif cropping patterns. In addition, we utilize a smart farm ontology that contains concepts and properties related to the agricultural domain. We link the collected data and our smart farm ontology to populate a knowledge graph. We utilize the generated knowledge graph to provide structural information and aggregate data by using SPARQL queries. The aggregated data is further used by our machine learning models to predict the crop yield to benefit farmers and various stakeholders. We also analyze and compare our results obtained for various machine learning models used.

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Empirical Analysis for Crop Yield Forecasting in India

Cited by 21 publications

References 7 publications

An intelligent decision support system for crop yield prediction using hybrid machine learning algorithms

An intelligent decision support system for crop yield prediction using hybrid machine learning algorithms

Naive Regression Growth Models for Prediction of Peppermint Yield Production

YieldPredict: A Crop Yield Prediction Framework for Smart Farms

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