Next Article in Journal
A Radio Channel Model for D2D Communications Blocked by Single Trees in Forest Environments
Next Article in Special Issue
SensorTalk: An IoT Device Failure Detection and Calibration Mechanism for Smart Farming
Previous Article in Journal
Blind Fractionally Spaced Channel Equalization for Shallow Water PPM Digital Communications Links
Previous Article in Special Issue
Navigation Algorithm Based on the Boundary Line of Tillage Soil Combined with Guided Filtering and Improved Anti-Noise Morphology
Open AccessArticle

A Triangular Similarity Measure for Case Retrieval in CBR and Its Application to an Agricultural Decision Support System

Departamento de Ingeniería Telemática y Electrónica (DTE), Escuela Técnica Superior de Ingeniería y Sistemas de Telecomunicación (ETSIST), Universidad Politécnica de Madrid (UPM), C/Nikola Tesla, s/n, 28031 Madrid, Spain
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(21), 4605; https://doi.org/10.3390/s19214605
Received: 26 August 2019 / Revised: 1 October 2019 / Accepted: 22 October 2019 / Published: 23 October 2019
(This article belongs to the Special Issue Smart Agricultural Applications with Internet of Things)
Case-based reasoning has been a widely-used approach to assist humans in making decisions through four steps: retrieve, reuse, revise, and retain. Among these steps, case retrieval plays a significant role because the rest of processes cannot proceed without successfully identifying the most similar past case beforehand. Some popular methods such as angle-based and distance-based similarity measures have been well explored for case retrieval. However, these methods may match inaccurate cases under certain extreme circumstances. Thus, a triangular similarity measure is proposed to identify commonalities between cases, overcoming the drawbacks of angle-based and distance-based measures. For verifying the effectiveness and performance of the proposed measure, case-based reasoning was applied to an agricultural decision support system for pest management and 300 new cases were used for testing purposes. Once a new pest problem is reported, its attributes are compared with historical data by the proposed triangular similarity measure. Farmers can obtain quick decision support on managing pest problems by learning from the retrieved solution of the most similar past case. The experimental result shows that the proposed measure can retrieve the most similar case with an average accuracy of 91.99% and it outperforms the other measures in the aspects of accuracy and robustness. View Full-Text
Keywords: triangular similarity measure; case retrieval; case-based reasoning; decision support system; sustainable agriculture triangular similarity measure; case retrieval; case-based reasoning; decision support system; sustainable agriculture
Show Figures

Figure 1

MDPI and ACS Style

Zhai, Z.; Ortega, J.-F.M.; Castillejo, P.; Beltran, V. A Triangular Similarity Measure for Case Retrieval in CBR and Its Application to an Agricultural Decision Support System. Sensors 2019, 19, 4605.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop