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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science
Panduranga, Kiran Muniswamy
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Computer Science & IT Electrical & Electronics Engineering

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Sustainability insights on learning-based approaches in precision agriculture in internet-of-things Panduranga, Kiran Muniswamy; Ranganathasharma, Roopashree Hejjaji
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3495-3511

Abstract

Precision agriculture (PA) is meant to automate the complete agricultural processes with the sole target of enhanced crop yield with reduced cost of operation. However, deployment of PA in internet of things (IoT) based architecture demands solutions towards addressing various challenges where most are related to proper and precise predictive management of agricultural data. In this perspective, it is noted that learning-based approaches have made some contributory success towards addressing different variants of issues in PA; however, such methods suffer from certain loopholes, primarily related to the non-inclusion of practical constraints of IoT infrastructure in PA and lack of emphasis towards bridging the trade-off between higher accuracy and computational burden that is eventually associated with this. This paper contributes towards highlighting the strengths and weaknesses of recent learning approaches and contributes towards novel findings.
A multi-tier framework of decentralized computing environment for precision agriculture (DCEPA) Panduranga, Kiran Muniswamy; Ranganathasharma, Roopashree Hejjaji
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp1072-1080

Abstract

Although collecting enormous volumes of heterogeneous data from many sensors and guaranteeing real-time decision-making are problems, precision agriculture (PA) has emerged as a promising approach to increase agricultural efficiency. The efficacy of current centralized solutions is limited in large-scale agricultural settings due to resource limitations and data saturation. In order to solve these problems, this paper suggests a decentralized computing environment for precision agriculture (DECPA), which divides resource management and data processing among several layers (end, edge, and cloud). DECPA optimizes task execution and resource allocation in the field by utilizing ensemble machine learning models (deep neural network (DNN), long short-term memory (LSTM), autoencoder (AE), and support vector machine (SVM)) and a multi-tier architecture. The findings demonstrate that DECPA combined with DNN performs better than alternative models, achieving a 20% decrease in energy usage, an 18% speedup in response time, a 5% improvement in accuracy, and a 51% reduction in latency. This illustrates the system’s capacity to manage massive amounts of data effectively while preserving peak performance. To sum up, DECPA uses decentralized resources and cutting-edge machine learning models to provide a scalable and affordable precision agriculture solution. To improve the system’s flexibility and real-time responsiveness, future research will investigate additional optimization and use in various agricultural contexts.
Co-Authors Ranganathasharma, Roopashree Hejjaji