Predicting Tomato Yield Under Heat Stress in Tamil Nadu Using Machine Learning Models - Research Article

Aug 3, 2026

Rising temperatures and their unpredictability adversely affect tomato growth and yield, making temperature variability a key driver of productivity losses in tomato (Solanum lycopersicum L.). This study assessed how temperature variability influences tomato yield and built predictive models using machine learning. Tomato yield was forecast with Random Forest, XGBoost, and K-nearest neighbors under changing temperatures, with performance compared through train-test split and K-fold cross validation. Train-test split performed best, and the Random Forest model achieved R2=0.84, MSE=7.88, RMSE=2.81, and MAE=1.19. Kernel density estimation further analyzed yield–temperature relationships, and future yields for 2023–2026 were projected under SSP2-4.5 and SSP5-8.5, indicating gradual increases with decline under extreme heat.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages12

Reading Time30 min

AccessFree to download

Abstract

Introduction

Temperature effects on tomato growth and yield

Materials and Methods

Machine learning models and evaluation

Results and Discussion

Model performance and error metrics

Yield–temperature correlation using KDE

Future yield projections

SSP scenarios (2023–2026)

PLANT SCIENCE TODAY Vol 12(sp1): 01–12 [https://doi.org/10.14719/pst.9940](https://doi.org/10.14719/pst.9940) eISSN 2348-1900 RESEARCH ARTICLE Predicting tomato yield under heat stress in Tamil Nadu using Machine Learning Models Musierose C1, Maragatham N2*, Sathyamoorthy N K1, Djanaguiraman M3, Indu Rani C4, Somasundaram E5, Sakthivel N6 & Sivasakthivelan P6 1Agro Climate Research Centre, Tamil Nadu Agricultural University, Coimbatore 641 003, Tamil Nadu, India 2Centre for Students Welfare, Tamil Nadu Agricultural University, Coimbatore 641 003, Tamil Nadu, India 3Department of Crop Physiology, Tamil Nadu Agricultural University, Coimbatore 641 003, Tamil Nadu, India 4Department of Vegetable Sciences, Tamil Nadu Agricultural University, Coimbatore 641 003, Tamil Nadu, India 5Directorate of Agri Business Management, Tamil Nadu Agricultural University, Coimbatore 641 003, Tamil Nadu, India 6Agricultural Research Station, Tamil Nadu Agricultural University, Bhavanisagar 638 451, Tamil Nadu, India *[Correspondence email -mm65@tnau.ac.in](Correspondence email -mm65@tnau.ac.in) Received: 09 June 2025; Accepted: 21 July 2025; Available online: Version 1.0: 09 September 2025 Cite this article: Musierose C, Maragatham N, Sathyamoorthy NK, Djanaguiraman M, Indu RC, Somasundaram E, Sakthivel N, Sivasakthivelan P. Predicting tomato yield under heat stress in Tamil Nadu using Machine Learning Models. Plant Science Today. 2025;12(sp1):01–12. [https:/doi.org/10.14719/pst.9940](https:/doi.org/10.14719/pst.9940) Abstract Rise in temperature and its unpredictability has an adverse effect on growth and yield, making it an important variable in tomato (Solanum lycopersicum L.) production. This study aimed at evaluating the impact of temperature variability on tomato yield and developing predictive models using Machine Learning (ML) techniques to forecast future productivity under changing climate. The tomato yield was predicted using Machine Learning Models (MLM) such as Random Forest (RF), XGBoost (XG) and K-Nearest Neighbours (KNN) in response to temperature changes. The model was evaluated and improved by comparing both Train-Test split (T-T) and K-fold cross validation techniques. Among these, the T-T method performed better and was used for model training and testing. The findings showed that RF model outperformed the others, with the T-T dataset, achieving Coefficient of Determination (R2) = 0.84, Mean Squared Error (MSE)= 7.88, Root Mean Square Error (RMSE) = 2.81 and Mean Absolute Error (MAE) = 1.19, followed by XGBoost and KNN. Additionally, Kernel Density Estimation (KDE) correlation analysis was employed to examine the relationship between yield and temperature. Moreover, future tomato yields were predicted under Shared Socio-economic Pathways (SSP2-4.5 and SSP5-8.5) for the period of 2023-2026 using the RF model. Tomato productivity is likely to increase gradually in the immediate future and eventually fall under extreme heat. These findings illustrate the potential of machine learning in forecasting tomato yield under varying temperature conditions, thereby aiding climate adaptation strategies and agricultural planning. Keywords: k-nearest neighbour; machine learning models; random forest; temperature; tomato; XGBoost; yield prediction Introduction Tomato (Solanum lycopersicum L.), a member of the family Solanaceae, is a commercially and nutritionally important vegetable crop cultivated worldwide, with a global production value exceeding USD 182 billion (1). It is rich in a potent antioxidant, lycopene which is an anticarcinogen (2). In India, it is grown in both tropical and sub-tropical areas and ranked second in vegetable production next to potato (3). Tamil Nadu, with its diverse agro-climatic zones, supports extensive tomato cultivation, producing 7.94 lakh tonnes annually from an area of 41392 hectares, with an average yield of 30.51 t/ha. Tomatoes are also valuable sources of ascorbic

FAQs

Which machine learning models were used to predict tomato yield under heat stress?

Random Forest, XGBoost, and K-nearest neighbors (KNN) were used to forecast tomato yield in response to temperature changes.

How did the study evaluate and improve the predictive models?

Model quality was compared using both train-test split (T-T) and K-fold cross validation, and the T-T approach performed better and was used for training and testing.

What were the key performance results for the best model?

The Random Forest model outperformed the others using the T-T dataset, achieving R2=0.84, MSE=7.88, RMSE=2.81, and MAE=1.19.

How were future tomato yields projected and what trend was expected?

Future yields for 2023–2026 were predicted under SSP2-4.5 and SSP5-8.5 using the Random Forest model, suggesting gradual increases in the near term followed by declines under extreme heat.

Aug 3, 2026

Upload by Evangeline