Contributions to case-based reasoning enabled decision support system for smart agriculture (2020)

Nowadays, high demands for food from the world-wide growing population are impacting the environment and putting many pressures on agricultural productivity. As a farming management concept, smart agriculture tries to integrate advanced technologies like Internet of Things, Artificial Intelligence, and Remote Sensing into current farming practices for the purpose of boosting productivity and improving the quality of agricultural products. The core of smart agriculture emphasizes on the use of information systems and communication technologies in the cyber-physical farm management cycle. However, farmers can hardly take advantage of collected information to make proper decisions because it is difficult to transfer the explosive amount of raw data from sensors, actuators, and networks into practical knowledge for managing farming operations. Therefore, delivering an agricultural decision support system to farmers to assist them in making evidence-based decisions is needed. The ultimate objective of this thesis is to design and implement a decision support system within the Aggregate Farming in the Cloud (AFarCloud) platform. Meanwhile, the proposed decision support system tries to overcome the current challenging problems in this topic. To achieve this objective, this thesis follows the below three research areas. The first area aims at providing a general solution for delivering an agricultural decision support system for the AFarCloud platform. An architectural proposal of the decision support system framework for managing farming operations is presented in this thesis. The proposed framework defines an algorithm manager and an algorithm toolbox. The former component is responsible to configure registered decision support algorithms, while the latter component is capable of selecting a certain algorithm to generate decision supports. The proposed framework demonstrates how smart agriculture can benefit from the support of a decision support system, and therefore assist farmers in making evidence-based decisions. The second area focuses on designing a case-based reasoning (CBR) algorithm to generate decision supports for farmers. This CBR algorithm is implemented within the framework proposed in the first research area, in particular, within the algorithm toolbox component. According to the nature of the CBR algorithm, it can be divided into five steps, including representation, retrieval, reuse, revision, and retention. In this thesis, an improved CBR algorithm is proposed to overcome the detected shortcomings of the current research work. Firstly, an associated case representation formalism is presented for enhancing the typical feature vector representation. The proposed representation formalism contains the similar and dissimilar iii associations between past cases, enabling to compare potential similar cases preferentially. Secondly, a triangular similarity measure is designed by taking advantage of cosine and Euclidean distance measures. For providing a precise measurement, the magnitude differences between two compared N-dimensional vectors are taken into consideration. Thirdly, a fast case retrieval algorithm is developed, enabling to determine a list of similar past cases by comparing a fewer number of cases. As a consequence, the retrieval efficiency is improved while the retrieval accuracy can be guaranteed as well. Fourthly, a learning-based approach for solution reuse and revision is studied. This reuse and revision approach tries to identify the difference between the problem part of compared cases, and then update the retrieved solution based on previous experiences. Lastly, an associated case retention approach is put forward. Apart from the typical addition and deletion strategies, the proposed retention approach also concerns to update the existed associations and generate new associations for the learned cases. By enhancing each step of the CBR loop, the proposed CBR algorithm is able to generate promising decision supports with great efficiency and accuracy. The third area considers a hybrid decision support mechanism for the AFarCloud platform. It is noted that though the improved CBR algorithm can generate a satisfied result for the most queries, it may be unable to generate the decision supports when the CBR algorithm fails to retrieve a list of similar past cases. Under this circumstance, the decision support system should start other registered algorithms to carry on the task. Therefore, for coordinating the interaction between various decision support algorithms, a mediator design pattern is adopted in this hybrid decision support mechanism. Owing to the design of the mediator component, different decision support algorithms do no need to interact with each other directly. Instead, the communication work between the algorithm manager and decision support algorithms is handled by this mediator component. This hybrid decision support mechanism is verified through a preliminary proof, considering the CBR algorithm and an artificial neural network algorithm. The result suggests that the hybrid decision support mechanism can enhance the robustness of the overall decision support system. Lastly, the proposed decision support system, along with the improved CBR algorithm, are all verified by simulation. The simulation results demonstrate that the proposal in this thesis is effective and achieves better performance than previous works.