ArticleSensors (Basel, Switzerland)2019
Sensors Driven AI-Based Agriculture Recommendation Model for Assessing Land Suitability.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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.
Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of IoT Solutions for Smart Farming.Sensors (Basel, Switzerland) · 2020Pooled it
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- Internet of things enabled smart agriculture: Current status, latest advancements, challenges and countermeasures.Heliyon · 2025Review
- From tradition to smart: A comprehensive review of the evolution and prospects of land use planning tools.Heliyon · 2024Review
- Designing and modeling an IoT-based software system for land suitability assessment use case.Environmental monitoring and assessment · 2024Article
- AI-Enabled Traffic Control Prioritization in Software-Defined IoT Networks for Smart Agriculture.Sensors (Basel, Switzerland) · 2023Article
- Real-Time Context-Aware Recommendation System for Tourism.Sensors (Basel, Switzerland) · 2023Article
- Shape classification technology of pollinated tomato flowers for robotic implementation.Scientific reports · 2023Article
- Addressing Internet of Things security by enhanced sine cosine metaheuristics tuned hybrid machine learning model and results interpretation based on SHAP approach.PeerJ. Computer science · 2023Article
- Monitoring Ambient Parameters in the IoT Precision Agriculture Scenario: An Approach to Sensor Selection and Hydroponic Saffron Cultivation.Sensors (Basel, Switzerland) · 2022Article
- Agricultural Irrigation Recommendation and Alert (AIRA) system using optimization and machine learning in Hadoop for sustainable agriculture.Environmental science and pollution research international · 2022Article
- Subgroup Preference Neural Network.Sensors (Basel, Switzerland) · 2021Article
- Deployment Strategies of Soil Monitoring WSN for Precision Agriculture Irrigation Scheduling in Rural Areas.Sensors (Basel, Switzerland) · 2021Article
- Crop Enhancement of Cucumber Plants under Heat Stress by Shungite Carbon.International journal of molecular sciences · 2020Article
- Realizing Efficient Security and Privacy in IoT Networks.Sensors (Basel, Switzerland) · 2020Article
- Mobility Aware Duty Cycling Algorithm (MADCAL) A Dynamic Communication Threshold for Mobile Sink in Wireless Sensor Network.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The world population is expected to grow by another two billion in 2050, according to the survey taken by the Food and Agriculture Organization, while the arable area is likely to grow only by 5%. Therefore, smart and efficient farming techniques are necessary to improve agriculture productivity. Agriculture land suitability assessment is one of the essential tools for agriculture development. Several new technologies and innovations are being implemented in agriculture as an alternative to collect and process farm information. The rapid development of wireless sensor networks has triggered the design of low-cost and small sensor devices with the Internet of Things (IoT) empowered as a feasible tool for automating and decision-making in the domain of agriculture. This research proposes an expert system by integrating sensor networks with Artificial Intelligence systems such as neural networks and Multi-Layer Perceptron (MLP) for the assessment of agriculture land suitability. This proposed system will help the farmers to assess the agriculture land for cultivation in terms of four decision classes, namely more suitable, suitable, moderately suitable, and unsuitable. This assessment is determined based on the input collected from the various sensor devices, which are used for training the system. The results obtained using MLP with four hidden layers is found to be effective for the multiclass classification system when compared to the other existing model. This trained model will be used for evaluating future assessments and classifying the land after every cultivation.
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Registered trials
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.