Publications

2023 International Conference on Power, Instrumentation, Control and Computing (PICC)
Publisher: IEEE

Added to IEEE Xplore: June 8, 2023

DOI: 10.1109/PICC57976.2023.10142376

Abstract - 

The practise of farming has endured significant transformation as technology advances every day. The constraints of area and nonlinear nature of climatic conditions, polyhouse kind of concepts are increasing, which is helpful in production of flowers, vegetables and fruits. The proposed work discusses such an automated irrigation system that highlights the optimum solution for the efficient use of water and electricity for agricultural purposes. Field survey and literature shows that the existing systems are available with two solutions, one is timer-based and another one is moisture-based automization. Moreover, the timer-based system has demerits like being semi-automated i.e., timer needs to be changed manually according to climate. Similarly, in moisture-based systems, reliability is the issue. Therefore, the main objectives of the proposed work are to overcome the demerits of the present systems by integrating both the systems, to develop a fully automated irrigation system, to manage the use of water, electricity, and to add a remote controlling system. The report includes algorithm for the integration of moisture and timer-based system which provides the optimum efficiency on the water use and the use of solenoidal valve. 

International Research Journal of Engineering and Technology (IRJET)

Publisher: IRJET

Added to IRJET: Sep, 2023

Volume: 10, Issue: 09

Abstract - 

This project's abstract centers on the task of predicting individual insurance premiums by leveraging personal health data and assessing seven regression models, including Linear Regression, Decision Tree Regression, Random Forest Regression, Gradient Boosting Regression, and KNN. Following model training on a dedicated dataset and the subsequent prediction generation, rigorous accuracy testing against real-world data highlighted the superior performance of Gradient Boosting and Random Forest algorithms. The project's progression toward Heroku deployment via MLOps technologies involved establishing automated data pipelines, model versioning, CI/CD pipelines for testing and deployment, containerization, Heroku configuration, scalability, security, and user interface development. Continuous monitoring, optimization, and documentation completion ensured a successful transition from development to a production-ready insurance premium prediction service on Heroku.. 

International Research Journal of Engineering and Technology (IRJET)

Publisher: IRJET

Added to IRJET: Nov, 2023

Volume: 10, Issue: 11

Abstract - 

This research pioneers a transformative approach in precision agriculture by leveraging machine learning and innovative technologies to predict optimal irrigation schedules for greenhouse cultivation. The project seamlessly integrates a multidisciplinary collaboration between agriculture, data science, and technology. A robust dataset, meticulously curated through extensive surveys and consultations, captures diverse crops and their attributes. The implementation involves the development and evaluation of a Decision Tree Regression model, chosen after a comprehensive comparative analysis of supervised learning models. Leveraging technologies such as Flask, the model incorporates key attributes like temperature, humidity, and growth stage to accurately predict crop water requirements. Rigorous data preprocessing and validation strategies are employed, ensuring the model's reliability. Practical application is demonstrated through the creation of precise irrigation schedules, optimizing resource utilization and enhancing crop yield. The project culminates in a sophisticated irrigation scheduling system, considering factors like weather conditions, soil moisture, and plant growth stages. The integration of Flask technology facilitates a user-friendly interface, enhancing accessibility. The findings underscore the model's accuracy, interpretability, and adaptability, showcasing the transformative potential of machine learning and technology in addressing critical challenges in modern farming practices. This research not only advances precision agriculture but also exemplifies the synergy between machine learning algorithms, Flask technology, and sustainable agricultural innovation.