Conventional irrigation practices are inefficient and wasteful
Research gap analysis derived from 3 agriculture papers in our local library.
The gap
Conventional irrigation practices are inefficient and wasteful. There is a need for a Smart Irrigation System that combines Internet of Things technology and Machine Learning to enhance irrigation efficiency.
Evidence profile
Sourced from the stated research gap and synthesized and abstract of the source papers, classified as general, drawn from work published between 2025 and 2026, spanning 3 journals. Those papers have been cited 41 times in total.
Research trend
Established — well-defined area with open sub-problems.
Supporting evidence — 3 representative gaps
- Smart Irrigation System using IOT Device and Machine Learning (2026) · International Journal for Research in Applied Science and Engineering Technology · doi
Conventional irrigation practices are inefficient and wasteful. There is a need for a Smart Irrigation System that combines Internet of Things technology and Machine Learning to enhance irrigation efficiency.
generalstated research gapevidence 5/5Keywords: conventional irrigation practices inefficient wasteful there need smart - Integrating Artificial Intelligence for Sustainable Development of Sugarcane Irrigation Systems: A Study of Belagavi District, Karnataka (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi
Across this set, IoT-based smart irrigation is evaluated only on specific crops (sugarcane, shallots, generic crops) and regions (Belagavi district, urban gardens); no study applies the same multimodal ML framework across diverse crop types and geographical regions to assess generalizability and transferability of irrigation optimization models.
generalsynthesizedevidence 5/5Keywords: across set iot-based smart irrigation evaluated only specific - Research and Development of an IoT Smart Irrigation System for Farmland Based on LoRa and Edge Computing (2025) · Agronomy · cited 41× · doi
In response to the current key issues in the field of smart irrigation for farmland, such as the lack of data sources and insufficient integration, a low degree of automation in drive execution and control, and over-reliance on cloud platforms for analyzing and calculating decision making processes, we have developed nodes and gateways for smart irrigation.
generalabstractevidence 5/5Keywords: smart irrigation response current issues field farmland lack sources insufficient integration degree automation drive execution
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