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Applying case-based reasoning and a learning-based adaptation strategy to irrigation scheduling in grape farming
Computers and Electronics in Agriculture ( IF 7.7 ) Pub Date : 2020-11-01 , DOI: 10.1016/j.compag.2020.105741 Zhaoyu Zhai , José Fernán Martínez , Néstor Lucas Martínez , Vicente Hernández Díaz
Computers and Electronics in Agriculture ( IF 7.7 ) Pub Date : 2020-11-01 , DOI: 10.1016/j.compag.2020.105741 Zhaoyu Zhai , José Fernán Martínez , Néstor Lucas Martínez , Vicente Hernández Díaz
Abstract As a key part of vineyard management, irrigation of grapevines puts forward the need of scheduling water resources in a more precise and efficient way. Typically, irrigation plans are generated through the use of mathematical models. However, the unwelcoming fact is that such models usually require a massive quantity of monitored data that is often unavailable or incomplete in most developing countries. As a consequence, this paper takes advantage of advanced artificial intelligence techniques, in particular, the case-based reasoning approach, to estimate the reference evapotranspiration, and therefore, to calculate the amount of irrigation water in grape farming. For improving the current case-based reasoning approach, especially the solution revision part, this paper proposes a learning-based adaptation strategy by fully making use of the hidden information in the case base. Inspired by the feature vector representation, a revision task could be also considered as a situation and action pair. The situation part attempts to capture the difference between the target case and past cases, while the action pair aims at adapting the solution to reflect the detected difference by learning adaptation knowledge from past experiences. Two retrieval tasks are involved in the revision process. On the one hand, the first one tries to retrieve an adaptation case and evaluate the difference between the new case. On the other hand, the second retrieval task should identify a collection of past cases that shares the similar difference as detected before. By learning from how the solutions of retrieved past cases were updated to solve the adaptation case, the solution of the adaptation case can be revised accordingly based on the obtained adaptation knowledge, and therefore to solve the new case. The experiment focuses on verifying the effectiveness of the case-based reasoning approach in irrigation scheduling, and evaluating the accuracy of the learning-based adaptation strategy. The experimental results demonstrate that the system is able to output a reasonable irrigation plan, while the deviation between the predicted and recorded values is around 5.42% and 7.94% for the relevance evapotranspiration and the amount of irrigation water respectively. In conclusion, the proposal in this paper has great potential for modeling irrigation scheduling system with promising advantages.
更新日期：2020-11-01
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