Skip to content
Research Article Open access CC BY 4.0

Studying on Crop Response Model for Grapes under Climate Change Scenario: Statistical Study Approach

A. Eswari, S. Saravanakumar

International Journal of Environment and Climate Change · pp. 883–894 · Published 23 Dec 2022

10.9734/ijecc/2022/v12i121528

Abstract

Grapes vine originally a temperate fruit crop and it’s also grown successfully under tropical conditions. Grape is one of the economically important fruit crops grown in India. As Theni district is the leading producer of grape in Tamil Nadu, followed by Coimbatore and Dindigul, this study is centred on the Theni-Kambum block area. In this region, Muskat Humbug is a well-liked cultivar that yields more than other varieties. In this study, this cultivar was employed. In this study, an artificial neural network (ANN), multiple linear regression (MLR), and elastic net (ELNET) regression methods were used to construct a yield prediction model (ANN) using twelve years secondary data. Additionally, we evaluate our model over a two-year period using field-level data from GRS and neighbouring farms. Finding the best-fit model for predicting grape yield and PDI using meteorological parameters in the Theni district is the goal of this communication. The model is chosen according to many performance indicators including RMSE, MAPE, MAE, and R2. Among the three techniques developed in the study, the Artificial Neural Network is found to be best for prediction of grape yield based on weather and disease incidence for the available data in the studied region.

Grapevine fruit crops artificial neural network weather disease

References (20)

  1. 1 Ridge Regression: Biased Estimation for Nonorthogonal Problems [DOI]
  2. 2 Ridge Regression: Biased Estimation for Nonorthogonal Problems [DOI]
  3. 3 Modelling the impact of future climate scenarios on yield and yield variability of grapevine [DOI]
  4. 4 Statistical modelling of grapevine yield in the Port Wine region under present and future climate conditions [DOI]
  5. 5 Vineyard Yield Estimation, Prediction, and Forecasting: A Systematic Literature Review [DOI]
  6. 6 Early yield prediction in different grapevine varieties using computer vision and machine learning [DOI]
  7. 7 RELIABILITY OF CLIMATE CHANGE IMPACT ASSESSMENTS FOR VITICULTURE [DOI]
  8. 8 A High-Resolution Cumulative Degree Day-Based Model to Simulate Phenological Development of Grapevine [DOI]
  9. 9 Machine Learning predictive model of grapevine yield based on agroclimatic patterns [DOI]
  10. 10 Prediction of Grapevine Yield Based on Reproductive Variables and the Influence of Meteorological Conditions [DOI]
  11. 11 Influence of thermal requirement in the aerobiological and phenological behavior of two grapevine varieties [DOI]
  12. 12 A neural network approach for disease forecasting in grapes using weather parameters [DOI]
  13. 13 Near‐Infrared Calibration of Soluble Stem Carbohydrates for Predicting Drought Tolerance in Spring Wheat [DOI]
  14. 14 Assessment of the Current and Projected Conditions of Water Availability in the Sevastopol Region for Grape Growing [DOI]
  15. 15 DESARROLLO DE MODELOS PREDICTIVOS DE FENOLOGÍA Y EVOLUCIÓN DE MADUREZ EN VID PARA VINO CV. CABERNET SAUVIGNON Y CHARDONNAY [DOI]
  16. 16 Development of agro-climatic grape yield model with future prospective [DOI]
  17. 17 Meteorological and Climate Modelling Services Tailored to Viticulturists [DOI]
  18. 18 Development of agro-climatic grape yield model for Nashik region, Maharashtra, India [DOI]
  19. 19 glmnet: Lasso and Elastic-Net Regularized Generalized Linear Models [DOI]
  20. 20 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT) [DOI]

Cited by 2

PROSPECTS FOR THE DEVELOPMENT OF DIGITAL MODELS OF GRAPE VARIETIES FOR PREDICTING THE EFFECTIVENESS OF TECHNOLOGICAL PROCESSES

Margarita Igorevna Ivanova, Вячеслав Иосифович Иванченко, Dmitry Valerievich Potanin · Fruit growing and viticulture of South Russia · 2024

Showing 1 of 2 known citations — external sources report more than can currently be individually listed.

Related research

Article metrics

Real usage data collected on this platform.

51

Page views

0

PDF downloads

30

Outbound clicks

2

Citations

Views over time

Views by country

Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".

Traffic sources

Referring site, by host.

Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.