Recent Advances in Agricultural Sensors: Towards Precision and Sustainable Farming

Global population growth, intensifying climate change, and escalating food security demands are mounting. In response, modern agriculture must transcend the limitations of traditional experience-based cultivation models to address issues such as low resource utilization, poor environmental adaptability, and significant yield fluctuations. As the core technical support of smart agriculture, agricultural sensors have become the key to transformation. This review systematically introduces the classification and working principles of current mainstream agricultural sensors: according to the monitoring parameters, they can be divided into humidity sensors, light sensors, gas sensors, pressure sensors, nutrient sensors, etc. At the same time, breakthroughs in emerging technologies such as microneedle sensing, nanosensing, and wireless sensor networks are being explored, which are breaking the application limitations of traditional sensors in complex agricultural environments. Combined with specific cases, the practical value of sensor technology is improving in agricultural drought monitoring, soil detection, and agricultural product quality assessment. Looking ahead, if agricultural sensors can overcome existing limitations through breakthroughs in material innovation, multi-sensor unit integration, and artificial intelligence algorithm fusion, this will provide stronger technological support for the further advancement of smart agriculture.

Authors

Institutions

Publication Details

Journal
Chemosensors
Published
2025-11-14
DOI
https://doi.org/10.3390/chemosensors13110399
Citations
1
Primary Topic
Smart Agriculture and AI
Type
article
Field-Weighted Citation Impact
1.75
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Recent Advances in Agricultural Sensors: Towards Precision and Sustainable Farming

Shuping Wu, Lin Jiaqi
1 citations
Chemosensors
Smart Agriculture and AI
1.75
article

Recent Advances in Agricultural Sensors: Towards Precision and Sustainable Farming

Shuping Wu, Lin Jiaqi
article en
1 citations

Abstract

Global population growth, intensifying climate change, and escalating food security demands are mounting. In response, modern agriculture must transcend the limitations of traditional experience-based cultivation models to address issues such as low resource utilization, poor environmental adaptability, and significant yield fluctuations. As the core technical support of smart agriculture, agricultural sensors have become the key to transformation. This review systematically introduces the classification and working principles of current mainstream agricultural sensors: according to the monitoring parameters, they can be divided into humidity sensors, light sensors, gas sensors, pressure sensors, nutrient sensors, etc. At the same time, breakthroughs in emerging technologies such as microneedle sensing, nanosensing, and wireless sensor networks are being explored, which are breaking the application limitations of traditional sensors in complex agricultural environments. Combined with specific cases, the practical value of sensor technology is improving in agricultural drought monitoring, soil detection, and agricultural product quality assessment. Looking ahead, if agricultural sensors can overcome existing limitations through breakthroughs in material innovation, multi-sensor unit integration, and artificial intelligence algorithm fusion, this will provide stronger technological support for the further advancement of smart agriculture.

ChemosensorsVol. 13(11)
Jiangsu University (CN)
Openalex Percentile: Top 11%
Smart Agriculture and AI
1.75
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.