Evidence map›Paper›PMID 41682397›Full record

ReviewSensors (Basel, Switzerland)2026

Precision Farming with Smart Sensors: Current State, Challenges and Future Outlook.

Bonface O Manono, Boniface Mwami, Sylvester Mutavi, Faith Nzilu

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Bonface O ManonoColorado State University Extension, Fort Collins, CO 80523, USA.ORCID 0000-0001-9150-2126
Boniface MwamiSchool of Agriculture, Environment, Water and Natural Resources Management, South Eastern Kenya University, P.O. Box 170, Kitui 90200, Kenya.ORCID 0000-0002-5368-2151
Sylvester MutaviSchool of Agriculture and Enterprise Development, Kenyatta University, Nairobi P.O. Box 43844-00100, Kenya.
Faith NziluEcodev Associates Limited, Machakos, Nairobi P.O. Box 5653-00100, Kenya.ORCID 0000-0003-2691-6634

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The agricultural sector, a vital industry for human survival and a primary source of food and raw materials, faces increasing pressure due to global population growth and environmental strains. Productivity, efficiency, and sustainability constraints are preventing traditional farming methods from adequately meeting the growing demand for food. Precision farming has emerged as a transformative paradigm to address these issues. It integrates advanced technologies to improve decision making, optimize yield, and conserve resources. This approach leverages technologies such as wireless sensor networks, the Internet of Things (IoT), robotics, drones, artificial intelligence (AI), and cloud computing to provide effective and cost-efficient agricultural services. Smart sensor technologies are foundational to precision farming. They offer crucial information regarding soil conditions, plant growth, and environmental factors in real time. This review explores the status, challenges, and prospects of smart sensor technologies in precision farming. The integration of smart sensors with the IoT and AI has significantly transformed how agricultural data is collected, analyzed, and utilized to optimize yield, conserve resources, and enhance overall farm efficiency. The review delves into various types of smart sensors used, their applications, and emerging technologies that promise to further innovate data acquisition and decision making in agriculture. Despite progress, challenges persist. They include sensor calibration, data privacy, interoperability, and adoption barriers. To fully realize the potential of smart sensors in ensuring global food security and promoting sustainable farming, the challenges need to be addressed.

Indexed as

agricultural resource managementagricultural technologydata-driven agricultureprecision agriculturesmart farmingsmart sensorssustainable agriculture

Identifiers

PMID41682397
PMCPMC12899811

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.