Agriculture plays a vital role in India’s economy, and accurate crop yield prediction is essential for ensuring food security, efficient resource allocation, and policy formulation. Traditional prediction methods often fail to account for the complex interactions between climatic, soil, and management factors. This paper presents a comprehensive survey on the use of machine learning (ML) techniques for predicting Indian crop yield. Various supervised and unsupervised learning algorithms—such as Linear Regression, Random Forest, Support Vector Machines (SVM), Decision Trees, and Artificial Neural Networks (ANN)—have been analyzed for their performance in forecasting yield across diverse agro-climatic regions. The survey highlights the importance of feature selection, data preprocessing, and the integration of remote sensing and weather data for improving prediction accuracy. Additionally, it discusses challenges such as data inconsistency, regional variability, and model generalization. The study concludes that machine learning-based models offer a promising approach for building intelligent, data-driven agricultural systems that support sustainable farming and policy planning in India.

Keywords : Crop Yield Prediction, Machine Learning, Agriculture, Data Analytics, India

Authors : Mrs.K.Rehana

Title : A Survey on Prediction of Indian Crop Yield using machine Learning

Volume/Issue : 2021;3(1 (January - February))

Page No : 7 - 12

The digital transformation of organizational processes has significantly reshaped Human Resource Management (HRM), giving rise to Electronic Human Resource Management (e-HRM) systems. This paper examines the tools and efficacy of e-HRM in both manufacturing and service sectors. e-HRM integrates information technology into HR functions such as recruitment, training, performance appraisal, and employee engagement, enabling real-time data access and strategic decision-making. The study explores the various e-HRM tools—such as Human Resource Information Systems (HRIS), e-recruitment portals, learning management systems, and performance tracking applications—used to streamline HR operations. Comparative analysis between manufacturing and service organizations highlights differences in adoption levels, technological readiness, and perceived effectiveness. Findings indicate that while manufacturing sectors primarily leverage e-HRM for operational efficiency and workforce planning, service sectors emphasize talent management and employee experience enhancement. The paper concludes that the successful implementation of e-HRM depends on organizational culture, infrastructure, and employee adaptability, ultimately contributing to improved productivity and strategic HR alignment.

Keywords : e-HRM, HR Technology, Manufacturing Sector, Service Sector, Human Resource Management

Authors : Supriya Patnaik

Title : Electronic Human Resources Management(e-HRM) Tools and Efficacy: A Study of Manufacturing and Service Sector

Volume/Issue : 2021;3(1 (January - February))

Page No : 1 - 6