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Inturu Chaithanya Sai, Kallem Akash, Dasari Thushar
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Page No: 1 - 9
Abstract : The rising demand for sustainable resource use in agriculture has resulted in a drive to find innovative answers for real-time monitoring and resourceful use of water and electricity. This project develops an overarching tracking application on the concerns of water and electricity, which is designed to help farmers and owners manage their farm resources effectively. Tracking use concerning water consumption, tracking of electricity consumption, and providing much-needed data concerning sustainability while optimizing operations is part of advanced mechanisms used. It monitors the water and communicates to the farmer in real-time current statistics about how much water has been used. It helps prime irrigation decisions and prevents wastage of water [1], [2]. This becomes a major feature of the application—the notification when water has dropped to a user-defined threshold forces the user to cut off the water pumps or motors from over-irrigating the crops and damaging them [4]. In return, this system optimizes the usage of water since it supports crop health as well as sustainable agriculture [6]. On the side of electricity, it monitors energy consumption allied with the water pumps of the farm and other electrical systems. Thus, this ensures that users know the real-time consumption patterns [5]. Users can therefore make efficient energy decisions, keep track of the cost associated with consumption, and predict electricity bills according to real usage [6]. Integrating the monitoring of water and electricity puts an overall view on the resource they consume, which is promoted by improved management and control [7]. This realizes alert systems that enable farm productivity through real-time alerts issued every time specific conditions concerning excess water levels are noticed for timely corrective measures [2]. The application also includes friendly user interfaces on charts and graphical displays about consumption trends that empower a user to render high-performance insights toward saving water and electricity, cutting costs, and sustainable farming [1], [2]. This project could actually help emphasize the possible role of digital tools in aiding resource-efficient agriculture, give a scalable solution, and contribute to environmental conservation on farms.
Keyword Sustainable agriculture, Resource management, Water tracking, Electricity monitoring, Real-time data, Farm resource optimization, Irrigation management, Energy efficiency
Reference:

Lopez, S., Sarada, V., Praveen, R. V. S., Pandey, A., Khuntia, M., & Haralayya, D. B. (2024). Artificial intelligence challenges and role for sustainable education in india: Problems and prospects. Sandeep Lopez, Vani Sarada, RVS Praveen, Anita Pandey, Monalisa Khuntia, Bhadrappa Haralayya (2024) Artificial Intelligence Challenges and Role for Sustainable Education in India: Problems and Prospects. Library Progress International44(3), 18261-18271.

Hanabaratti, K. D., Shivannavar, A. S., Deshpande, S. N., Argiddi, R. V., Praveen, R. V. S., & Itkar, S. A. (2024). Advancements in Natural Language Processing: Enhancing Machine Understanding of Human Language in Conversational AI Systems. International Journal of Communication Networks and Information Security16(4), 193-204.

Kumar, N., Kurkute, S. L., Kalpana, V., Karuppannan, A., Praveen, R. V. S., & Mishra, S. (2024, August). Modelling and Evaluation of Li-ion Battery Performance Based on the Electric Vehicle Tiled Tests using Kalman Filter-GBDT Approach. In 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS) (pp. 1-6). IEEE.

Praveen, R. V. S. (2024). Data Engineering for Modern Applications. Addition Publishing House.

Dhivya, R., Sagili, S. R., Praveen, R. V. S., VamsiLala, P. N. V., Sangeetha, A., & Suchithra, B. (2024, December). Predictive Modelling of Osteoporosis using Machine Learning Algorithms. In 2024 4th International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS) (pp. 997-1002). IEEE.

Kemmannu, P. K., Praveen, R. V. S., Saravanan, B., Amshavalli, M., & Banupriya, V. (2024, December). Enhancing Sustainable Agriculture Through Smart Architecture: An Adaptive Neuro-Fuzzy Inference System with XGBoost Model. In 2024 International Conference on Sustainable Communication Networks and Application (ICSCNA) (pp. 724-730). IEEE.

Anuprathibha, T., Praveen, R. V. S., Jayanth, H., Sukumar, P., Suganthi, G., & Ravichandran, T. (2024, October). Enhancing Fake Review Detection: A Hierarchical Graph Attention Network Approach Using Text and Ratings. In 2024 Global Conference on Communications and Information Technologies (GCCIT) (pp. 1-5). IEEE.

Sharma, S., Vij, S., Praveen, R. V. S., Srinivasan, S., Yadav, D. K., & VS, R. K. (2024, October). Stress Prediction in Higher Education Students Using Psychometric Assessments and AOA-CNN-XGBoost Models. In 2024 4th International Conference on Sustainable Expert Systems (ICSES) (pp. 1631-1636). IEEE.

Yamuna, V., Praveen, R. V. S., Sathya, R., Dhivva, M., Lidiya, R., & Sowmiya, P. (2024, October). Integrating AI for Improved Brain Tumor Detection and Classification. In 2024 4th International Conference on Sustainable Expert Systems (ICSES) (pp. 1603-1609). IEEE.

Praveen, R. V. S., Raju, A., Anjana, P., & Shibi, B. (2024, October). IoT and ML for Real-Time Vehicle Accident Detection Using Adaptive Random Forest. In 2024 Global Conference on Communications and Information Technologies (GCCIT) (pp. 1-5). IEEE.

M., Arul Selvan (2016). Averting Eavesdrop Intrusion in Industrial Wireless Sensor Networks. International Journal of Innovative Research in Computer Science and Engineering (Ijircse) 2 (1):8-13.

Selvan, M. A. (2023). INDUSTRY-SPECIFIC INTELLIGENT FIRE MANAGEMENT SYSTEM.