DSSHerbicide: Weed control in winter wheat with a decision support system in three South Baltic regions – Field experimental results

Sønderskov, Mette; Fritzsche, Rolf; Mol, Friederike de; Gerowitt, Bärbel; Goltermann, S.; Kierzek, R.; Krawczyk, Roman; Bøjer, Ole Mission; Rydahl, Per

doi:10.1016/j.cropro.2015.06.009

Cited by 24 publications

(11 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The pest, crop, and environment data are randomly generated within a reasonable interval. For example, the attribute of crop planting density is generated from 180–525 seeds/m 2 [68]. Since TSM is developed within a European project, entitled Aggregate Farming in the Cloud (AFarCloud), we expect to receive data from real fields as soon as the sensor deployment is complete.…”

Section: Experiments and Discussionmentioning

confidence: 99%

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

Zhai

Ortega

Castillejo

et al. 2019

Sensors

View full text Add to dashboard Cite

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.

show abstract

Section: Experiments and Discussionmentioning

confidence: 99%

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

Zhai

Ortega

Castillejo

et al. 2019

Sensors

View full text Add to dashboard Cite

show abstract

“…The accuracy considers the average precision [56] of retrieved top three similar cases. The formula of the average precision is defined in Equation (1).…”

Section: Experiments and Discussionmentioning

confidence: 99%

An Associated Representation Method for Defining Agricultural Cases in a Case-Based Reasoning System for Fast Case Retrieval

Zhai

Ortega

Beltrán

et al. 2019

Sensors

View full text Add to dashboard Cite

As an artificial intelligence technique, case-based reasoning has considerable potential to build intelligent systems for smart agriculture, providing farmers with advice about farming operation management. A proper case representation method plays a crucial role in case-based reasoning systems. Some methods like textual, attribute-value pair, and ontological representations have been well explored by researchers. However, these methods may lead to inefficient case retrieval when a large volume of data is stored in the case base. Thus, an associated representation method is proposed in this paper for fast case retrieval. Each case is interconnected with several similar and dissimilar ones. Once a new case is reported, its features are compared with historical data by similarity measurements for identifying a relative similar past case. The similarity of associated cases is measured preferentially, instead of comparing all the cases in the case base. Experiments on case retrieval were performed between the associated case representation and traditional methods, following two criteria: the number of visited cases and retrieval accuracy. The result demonstrates that our proposal enables fast case retrieval with promising accuracy by visiting fewer past cases. In conclusion, the associated case representation method outperforms traditional methods in the aspect of retrieval efficiency.

show abstract

“…Therefore, simulated data are used currently and they are generated within a given range. For instance, judging from the current literature [50], the planting density of rice is generated from 180 to 525 seeds/m 2 . The life cycle of rice can be categorized by "embryogenesis," "vegetative," "ripening" and "reproductive" stages [51], encoded by integers "1," "2," "3" and "4."…”

Section: Experimental Settingsmentioning

confidence: 99%

An Efficient Case Retrieval Algorithm for Agricultural Case-Based Reasoning Systems, with Consideration of Case Base Maintenance

et al. 2020

View full text Add to dashboard Cite

Case-based reasoning has considerable potential to model decision support systems for smart agriculture, assisting farmers in managing farming operations. However, with the explosive amount of sensing data, these systems may achieve poor performance in knowledge management like case retrieval and case base maintenance. Typical approaches of case retrieval have to traverse all past cases for matching similar ones, leading to low efficiency. Thus, a new case retrieval algorithm for agricultural case-based reasoning systems is proposed in this paper. At the initial stage, an association table is constructed, containing the relationships between all past cases. Afterwards, attributes of a new case are compared with an entry case. According to the similarity measurement, associated similar or dissimilar cases are then compared preferentially, instead of traversing the whole case base. The association of the new case is generated through case retrieval and added in the association table at the step of case retention. The association table is also updated when a closer relationship is detected. The experiment result demonstrates that our proposal enables rapid case retrieval with promising accuracy by comparing a fewer number of past cases. Thus, the retrieval efficiency of our proposal outperforms typical approaches.

show abstract

DSSHerbicide: Weed control in winter wheat with a decision support system in three South Baltic regions – Field experimental results

Cited by 24 publications

References 20 publications

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

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

An Associated Representation Method for Defining Agricultural Cases in a Case-Based Reasoning System for Fast Case Retrieval

An Efficient Case Retrieval Algorithm for Agricultural Case-Based Reasoning Systems, with Consideration of Case Base Maintenance

Contact Info

Product

Resources

About