Gowtham Rajendiran

@srmist.edu.in

Assistant Professor, Engineering & Technology
Assistant Professor, Engineering & Technology
SRM Institute of Science and Technology

Gowtham Rajendiran


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https://researchid.co/gowtham.r
Myself, Gowtham Rajendiran, a Full-time Research Scholar in the Department of CSE(Computing Technologies) under the School Of Computing, SRM University(Kattankulathur, Chengalpattu District). I feel quite interested in carrying out my research work in the Agricultural domain with the help of Machine Learning and IoT technologies. Eager to get the expected output of my research work as well as to fulfill the doctoral degree and kick start my career with good knowledge and experience.

EDUCATION

B. E (CSE), M. Tech (CSE), Ph. D in CSE (Pursuing)

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Science, Artificial Intelligence, Computer Engineering, Computer Science Applications
10

Scopus Publications

124

Scholar Citations

8

Scholar h-index

7

Scholar i10-index

Scopus Publications

  • UniTriRob: a robust machine learning regression model for predicting lettuce yields in aeroponic vertical farming
    Gowtham Rajendiran, Jebakumar Rethnaraj, Shrikant Zade, Ramakrishna Guttula, Krishna Kant Pandey
    Scientific Reports, 2026
    Aeroponic vertical tower farming is a cost-effective, sustainable method for optimizing the food crop- Lactuca Sativa (lettuce-a greeny leaf vegetable); yet accurate biomass prediction of the lettuce crop remains challenging due to the non-linear relationship between the climatic conditions and the variable lettuce growth parameters. To address this challenge, a robust machine learning model called UniTriRob regression model has been developed. This model primarily focuses on mitigating the effects of outliers and heteroskedastic errors across key growth-related parameters, including pH, total dissolved solids (TDS), temperature, electrical conductivity (EC), turbidity, humidity, light intensity and growth. The experimental validation highlights the model’s capability with high R-squared value of 97.8386% and the minimized error rate of 0.46, that outperforms the conventional forecasting methods. Hence, the model presents a viable alternative for maximizing aeroponic lettuce production efficiency and increasing yield forecast accuracy, contributing to sustainable agricultural practices.
  • Lettuce Yield Prediction: ElasticNet Regression Model (ElNetRM) for Indoor Aeroponic Vertical Farming System
    Gowtham Rajendiran, Jebakumar Rethnaraj
    International Journal of Electrical and Computer Engineering Systems, 2025
    Indoor aeroponic vertical farming systems have revolutionized agriculture by allowing efficient use of space and resources, eliminating the need for soil. These systems improve crop productivity and growth rates. However, accurately predicting lettuce yield in aeroponic environments remains a complex task due to the intricate interactions between environmental, nutrient, and growth parameters. This work aims to address these issues by integrating advanced sensor technologies with ElasticNet Regression Model (ElNetRM) for its hybrid L1 and L2 regularization capabilities, handling multicollinearity and feature selection problems effectively in order to develop a reliable yield prediction framework. The predictive results showcases that the ElNetRM model forecasts lettuce yield with high accuracy of 92% and less error score (RMSE) of 2.28 using a comprehensive dataset from a sensor-equipped indoor aeroponic system. Also, the results demonstrate the superior predictive power of ElNetRM in capturing complex variable relationships, enhancing yield prediction reliability.
  • IoT-integrated machine learning-based automated precision agriculture-indoor farming techniques
    Gowtham Rajendiran, Jebakumar Rethnaraj
    Using Traditional Design Methods to Enhance AI Driven Decision Making, 2024
    Precision agriculture driven by the integration of the advanced technologies like internet of things (IoT) and machine learning (ML) is revolutionary precision agriculture, especially the indoor farming techniques. This chapter explores the comprehensive application of IoT and ML in automating indoor cultivation practices, examining their diverse benefits and practical uses in comparison with the traditional farming methodologies. IoT enables the indoor farmers to create controlled environments through interconnected sensors, monitoring crucial variables but not limited to temperature, humidity, and light intensity. Complemented by ML algorithms, data analysis becomes efficient, providing predictive models for crop growth, pest detection, and disease outbreaks. Automated environment climate control systems optimize resource utilization, while precision irrigation minimizes water usage. Real-time monitoring and early detection of plant health issues reduce crop losses, ensuring high-quality produce.
  • Enhanced CNN Model for Lettuce Disease Identification in Indoor Aeroponic Vertical Farming Systems
    Gowtham Rajendiran, Jebakumar Rethnaraj, Janani Malaisamy
    4th International Conference on Sustainable Expert Systems Icses 2024 Proceedings, 2024
    India, like many other nations, heavily relies on agricultural exports to fund its national budget. Economic losses may occur when crop and plant diseases reduce production quality and quantity. Hence, it is crucial to detect infections early on in crop farming. Commercial crops grown using aeroponic vertical farming techniques include lettuce in particular. To increase the crop yield, the crop should be free from disease-causing agents like viral, bacterial, or fungal. As a response, scientists have utilized deep learning algorithms for the automatic identification of abnormalities in both indoor and outdoor farming crops by capturing leaf images. In this work, fine-tuned convolutional neural networks (CNN) were utilized to identify diseases in the aeroponic lettuce crops in an effective manner. Further, the Convolutional Neural Networks (CNN) were fine-tuned by training them on lettuce images. While testing, a higher accuracy of 95.6258% was achieved. Hence, this research work signifies that the proposed model would be appropriate for lettuce disease classification in controlled environment agriculture.
  • Cricket performance predictions: A comparative analysis of machine learning models for predicting cricket player's performance in the One Day International (ODI) world cup 2023
    Swamynathan Sanjaykumar, Karthikeyan Udaichi, Gowtham Rajendiran, Marian Cretu, Zhanneta Kozina
    Health Sport Rehabilitation, 2024
    Background and purpose
 Cricket, a globally renowned bat and ball sport, is the second most popular sport worldwide. The objective of the study is to utilize machine learning algorithms to predict the performance probabilities of Indian cricket players participating in the ODI Cricket World Cup 2023. Furthermore, we aim to assess and compare the predictive precision of three machine learning models such as, Random Forest, Support Vector Regression, and XGBoost.
 Materials and Methods
 Data collection centered on Indian One Day International cricket statistics, encompassing matches played, batting and bowling averages, catches taken, and performance predictions. We sourced this data from reputable platforms such as ESPNcricinfo and the International Cricket Council website. Our performance prediction utilized of three machine learning models such as, Random Forest, Support Vector Regression, and XGBoost. Comparative analysis was conducted, evaluating these models through essential metrics including Mean Squared Error, Root Mean Squared Error, Mean absolute Error, and R-squared.
 Results
 The comparative analysis revealed that the XGBoost model consistently outperformed the others. It exhibited lower errors with the lowest Mean Squared Error, Root Mean Squared Error, and Mean Absolute Error, signifying greater predictive accuracy. XGBoost achieved the highest R-squared value, indicating a robust relationship between predictions and actual performance probabilities. Random Forest produced satisfactory results but fell short of XGBoost's accuracy, while Support Vector Regression displayed less accurate predictions across all metrics.
 Conclusions
 This research demonstrates the superior predictive ability of the XGBoost model in the performance probabilities of Indian cricket players in the ODI Cricket World Cup. The practical implications underscore the significance of data-driven insights for team selection and strategy.
  • Sustainable agriculture practices: Dealing with innovative plant disease management technologies in challenging farming environments
    Advances in Engineering Research, 2023
  • Lettuce Crop Yield Prediction Analysis using Random Forest Regression Machine Learning Model in Aeroponics System
    Gowtham Rajendiran, Jebakumar Rethnaraj
    Proceedings of the 2023 2nd International Conference on Augmented Intelligence and Sustainable Systems Icaiss 2023, 2023
    Aeroponics is a type of soilless agriculture where plants are grown in a misted environment without the use of the soil. The process of yield prediction in aeroponics system for the lettuce crop is one of the key challenges because the crop yield is affected by variety of growth factors such as temperature, humidity, light and nutrient levels. Hence, to predict the lettuce crop yield in aeroponics, the machine learning models were utilized and these models can be trained on the historical data on crop yields and environmental variables. Here, in this work, the Random Forest (RF) regression is chosen as the machine learning model for yield prediction. The data used in this work were collected from the aeroponic lettuce farming tower. The purpose of the research work is to investigate the potential of the random forest model for efficient prediction of the lettuce crop yield in aeroponics system. The RF model can produce accurate prediction results of 92% which is better than the other regression algorithms in terms of forecasting lettuce crop production. Comparing this to the MAE of other regression models, which may be anywhere from 0.098 to 3.76 of the average reported yield, and it is observed that RF has a much more acceptable MAE. Hence, this work provides valuable information for farmers who are interested in utilizing this type of indoor farming to produce the high-yield of lettuce crops.
  • A Machine Learning Approach for Aeroponic Lettuce Crop Growth Monitoring System
    R. Gowtham, R. Jebakumar
    Lecture Notes in Networks and Systems, 2023
  • Analysis and Prediction of Lettuce Crop Yield in Aeroponic Vertical Farming using Logistic Regression Method
    R Gowtham, R Jebakumar
    2nd International Conference on Sustainable Computing and Data Communication Systems Icscds 2023 Proceedings, 2023
    Human population growth strains food supplies, resulting in more than one billion of the world’s 6.5 billion people facing hunger, which is the leading cause of death. In consequence, increasing crop production through traditional farming methods will not address the global food scarcity problem. As a precision agriculture approach, aeroponics, a soilless agriculture technique, can assist in cultivating in-demand food crops, which will help serve the people. Because aeroponics is an indoor farming technology, factors such as pH, EC, light, temperature, PPM, and turbidity will affect crop growth. Hence, monitoring an aeroponic crop growth system efficiently is crucial for achieving a high crop yield and reducing the need for food crops. Even though, many crop monitoring systems use IoT and machine learning algorithms, their prediction accuracy is not up to the level and if they are deployed in the real world, they may not meet customer expectations. In order to efficiently monitor crops, more advanced models are needed. However, understanding the large volume of data can be very tedious. Data visualization techniques can help make the distribution of the dataset easier to understand. Machine learning methods and packages can be used for environmental monitoring and visualization of the data where, the collection of crop growth dataset is carried out using various IoT sensors deployed in the growth environment. This study provides a comprehensive explanation of the data visualization techniques used to study the lettuce crop dataset and predict lettuce yield using the logistic regression machine learning method which is been implemented using Python coding language.
  • Smart Aeroponic Farming System: Using IoT with LCGM-Boost Regression Model for Monitoring and Predicting Lettuce Crop Yield
    Gowtham Rajendiran, Jebakumar Rethnaraj
    International Journal of Intelligent Engineering and Systems, 2023
    : Aeroponics is a popular soilless crop cultivation technology that integrates plant nutrition, physiology, and ecological control. It offers automated monitoring, protected cultivation, improved growth mechanisms, better yield and requires less maintenance. Here, to predict the crop yield, two systems are available: manual and automated. Manual systems often fail to produce better prediction results, leading to substantial crop losses whereas, the automated systems use machine intelligence for growth monitoring. This article proposes a lettuce crop growth monitoring-boost (LCGM-Boost) regression model for lettuce yield forecasting in aeroponic vertical farming system. This model is highly robust to outliers, produces better prediction results of 95.86% and lower error rates of 0.36 (MAE), 0.40 (MSE), and 0.63 (RMSE) than other machine learning models namely, support vector, random forest and XGBoost regressors. Hence, it is preferable for growth monitoring and yield prediction of the lettuce crop in the real-time aeroponics system.

RECENT SCHOLAR PUBLICATIONS

  • UniTriRob: a robust machine learning regression model for predicting lettuce yields in aeroponic vertical farming
    G Rajendiran, J Rethnaraj, S Zade, R Guttula, KK Pandey
    Scientific Reports , 2026
    2026
  • Lettuce Yield Prediction: ElasticNet Regression Model (ElNetRM) for Indoor Aeroponic Vertical Farming System
    G Rajendiran, J Rethnaraj
    International Journal of Electrical and Computer Engineering Systems 16 (09 … , 2025
    2025
    Citations: 3
  • Revolutionizing Agriculture: IoT with Machine Learning for Global Plant Growth Analysis and Food Sustainability
    G Rajendiran, J Rethnaraj, J Malaisamy
    Advances in Engineering Research (Numbered Series) https://doi.org/10.52305 … , 2025
    2025
  • Smart Forecasting Device for Aeroponic Lettuce Yield Prediction
    G Rajendiran, J Rethnaraj
    2024
  • Enhanced CNN Model for Lettuce Disease Identification in Indoor Aeroponic Vertical Farming Systems
    G Rajendiran, J Rethnaraj, J Malaisamy
    2024 4th International Conference on Sustainable Expert Systems (ICSES … , 2024
    2024
    Citations: 3
  • Optimizing Lettuce Crop Yield Prediction in an Indoor Aeroponic Vertical Farming System Using IoT-Integrated Machine Learning Regression Models
    G Rajendiran, J Rethnaraj
    Revue d'Intelligence Artificielle 38 (3), 825-836 , 2024
    2024
    Citations: 12
  • Cricket performance predictions: a comparative analysis of machine learning models for predicting cricket player’s performance in the One Day International (ODI) world cup 2023
    S Sanjaykumar, K Udaichi, G Rajendiran, M Cretu, Z Kozina
    Health, sport, rehabilitation 10 (1), 6-19 , 2024
    2024
    Citations: 19
  • IoT-Integrated Machine Learning-Based Automated Precision Agriculture-Indoor Farming Techniques
    G Rajendiran, J Rethnaraj
    Using Traditional Design Methods to Enhance AI-Driven Decision Making (ISBN … , 2024
    2024
    Citations: 15
  • Sustainable Agriculture Practices: Dealing with Innovative Plant Disease Management Technologies in Challenging Farming Environments
    G Rajendiran, J Rethnaraj
    Advances in Engineering Research (Numbered Series) ISBN: 979-8-89113-326-6 … , 2023
    2023
    Citations: 4
  • Smart Aeroponic Farming System: Using IoT with LCGM-Boost Regression Model for Monitoring and Predicting Lettuce Crop Yield.
    G Rajendiran, J Rethnaraj
    International Journal of Intelligent Engineering & Systems (IJIES) 16 (5 … , 2023
    2023
    Citations: 21
  • Lettuce crop yield prediction analysis using random forest regression machine learning model in aeroponics system
    G Rajendiran, J Rethnaraj
    2023 Second International Conference on Augmented Intelligence and … , 2023
    2023
    Citations: 10
  • Future of Smart Farming Techniques: Significance of Urban Vertical Farming Systems Integrated with IoT and Machine Learning
    G Rajendiran, J Rethnaraj
    Open Access Journal of Agricultural Research 8 (3) , 2023
    2023
    Citations: 10
  • Analysis and prediction of lettuce crop yield in aeroponic vertical farming using logistic regression method
    R Gowtham, R Jebakumar
    2023 International Conference on Sustainable Computing and Data … , 2023
    2023
    Citations: 9
  • A machine learning approach for aeroponic lettuce crop growth monitoring system
    R Gowtham, R Jebakumar
    International Conference on Intelligent Sustainable Systems, 99-116 , 2023
    2023
    Citations: 12
  • AN IOT-BASED PLANT LEAF DISEASE DETECTION USING MACHINE LEARNING AND AUTO SPRAYING MECHANISM
    R Gowtham, R Jebakumar
    Journal of Positive School Psychology, 283-297 , 2022
    2022
    Citations: 6
  • SECURE DATA TRANSMISSION FOR CLUSTERBASED WIRELESS SENSOR NETWORKS
    R Gowtham, D Jagadesh, V Kailash, K Suganya
    INTERNATIONAL RESEARCH JOURNAL IN ADVANCEDENGINEERING AND TECHNOLOGY (IRJAET … , 2019
    2019

MOST CITED SCHOLAR PUBLICATIONS

  • Smart Aeroponic Farming System: Using IoT with LCGM-Boost Regression Model for Monitoring and Predicting Lettuce Crop Yield.
    G Rajendiran, J Rethnaraj
    International Journal of Intelligent Engineering & Systems (IJIES) 16 (5 … , 2023
    2023
    Citations: 21
  • Cricket performance predictions: a comparative analysis of machine learning models for predicting cricket player’s performance in the One Day International (ODI) world cup 2023
    S Sanjaykumar, K Udaichi, G Rajendiran, M Cretu, Z Kozina
    Health, sport, rehabilitation 10 (1), 6-19 , 2024
    2024
    Citations: 19
  • IoT-Integrated Machine Learning-Based Automated Precision Agriculture-Indoor Farming Techniques
    G Rajendiran, J Rethnaraj
    Using Traditional Design Methods to Enhance AI-Driven Decision Making (ISBN … , 2024
    2024
    Citations: 15
  • Optimizing Lettuce Crop Yield Prediction in an Indoor Aeroponic Vertical Farming System Using IoT-Integrated Machine Learning Regression Models
    G Rajendiran, J Rethnaraj
    Revue d'Intelligence Artificielle 38 (3), 825-836 , 2024
    2024
    Citations: 12
  • A machine learning approach for aeroponic lettuce crop growth monitoring system
    R Gowtham, R Jebakumar
    International Conference on Intelligent Sustainable Systems, 99-116 , 2023
    2023
    Citations: 12
  • Lettuce crop yield prediction analysis using random forest regression machine learning model in aeroponics system
    G Rajendiran, J Rethnaraj
    2023 Second International Conference on Augmented Intelligence and … , 2023
    2023
    Citations: 10
  • Future of Smart Farming Techniques: Significance of Urban Vertical Farming Systems Integrated with IoT and Machine Learning
    G Rajendiran, J Rethnaraj
    Open Access Journal of Agricultural Research 8 (3) , 2023
    2023
    Citations: 10
  • Analysis and prediction of lettuce crop yield in aeroponic vertical farming using logistic regression method
    R Gowtham, R Jebakumar
    2023 International Conference on Sustainable Computing and Data … , 2023
    2023
    Citations: 9
  • AN IOT-BASED PLANT LEAF DISEASE DETECTION USING MACHINE LEARNING AND AUTO SPRAYING MECHANISM
    R Gowtham, R Jebakumar
    Journal of Positive School Psychology, 283-297 , 2022
    2022
    Citations: 6
  • Sustainable Agriculture Practices: Dealing with Innovative Plant Disease Management Technologies in Challenging Farming Environments
    G Rajendiran, J Rethnaraj
    Advances in Engineering Research (Numbered Series) ISBN: 979-8-89113-326-6 … , 2023
    2023
    Citations: 4
  • Lettuce Yield Prediction: ElasticNet Regression Model (ElNetRM) for Indoor Aeroponic Vertical Farming System
    G Rajendiran, J Rethnaraj
    International Journal of Electrical and Computer Engineering Systems 16 (09 … , 2025
    2025
    Citations: 3
  • Enhanced CNN Model for Lettuce Disease Identification in Indoor Aeroponic Vertical Farming Systems
    G Rajendiran, J Rethnaraj, J Malaisamy
    2024 4th International Conference on Sustainable Expert Systems (ICSES … , 2024
    2024
    Citations: 3
  • UniTriRob: a robust machine learning regression model for predicting lettuce yields in aeroponic vertical farming
    G Rajendiran, J Rethnaraj, S Zade, R Guttula, KK Pandey
    Scientific Reports , 2026
    2026
  • Revolutionizing Agriculture: IoT with Machine Learning for Global Plant Growth Analysis and Food Sustainability
    G Rajendiran, J Rethnaraj, J Malaisamy
    Advances in Engineering Research (Numbered Series) https://doi.org/10.52305 … , 2025
    2025
  • Smart Forecasting Device for Aeroponic Lettuce Yield Prediction
    G Rajendiran, J Rethnaraj
    2024
  • SECURE DATA TRANSMISSION FOR CLUSTERBASED WIRELESS SENSOR NETWORKS
    R Gowtham, D Jagadesh, V Kailash, K Suganya
    INTERNATIONAL RESEARCH JOURNAL IN ADVANCEDENGINEERING AND TECHNOLOGY (IRJAET … , 2019
    2019