OAK4XAI: Model towards Out-Of-Box eXplainable Artificial Intelligence for Digital Agriculture

Recent machine learning approaches have been effective in Artificial Intelligence (AI) applications. They produce robust results with a high level of accuracy. However, most of these techniques do not provide human-understandable explanations for supporting their results and decisions. They usually act as black boxes, and it is not easy to understand how decisions have been made. Explainable Artificial Intelligence (XAI), which has received much interest recently, tries to provide human-understandable explanations for decision-making and trained AI models. For instance, in digital agriculture, related domains often present peculiar or input features with no link to background knowledge. The application of the data mining process on agricultural data leads to results (knowledge), which are difficult to explain. In this paper, we propose a knowledge map model and an ontology design as an XAI framework (OAK4XAI) to deal with this issue. The framework does not only consider the data analysis part of the process, but it takes into account the semantics aspect of the domain knowledge via an ontology and a knowledge map model, provided as modules of the framework. Many ongoing XAI studies aim to provide accurate and verbalizable accounts for how given feature values contribute to model decisions. The proposed approach, however, focuses on providing consistent information and definitions of concepts, algorithms, and values involved in the data mining models. We built an Agriculture Computing Ontology (AgriComO) to explain the knowledge mined in agriculture. AgriComO has a well-designed structure and includes a wide range of concepts and transformations suitable for agriculture and computing domains.Comment: AI-2022 Forty-second SGAI International Conference on Artificial Intelligenc

Knowledge Representation in Digital Agriculture: A Step Towards Standardised Model

In recent years, data science has evolved significantly. Data analysis and mining processes become routines in all sectors of the economy where datasets are available. Vast data repositories have been collected, curated, stored, and used for extracting knowledge. And this is becoming commonplace. Subsequently, we extract a large amount of knowledge, either directly from the data or through experts in the given domain. The challenge now is how to exploit all this large amount of knowledge that is previously known for efficient decision-making processes. Until recently, much of the knowledge gained through a number of years of research is stored in static knowledge bases or ontologies, while more diverse and dynamic knowledge acquired from data mining studies is not centrally and consistently managed. In this research, we propose a novel model called ontology-based knowledge map to represent and store the results (knowledge) of data mining in crop farming to build, maintain, and enrich the process of knowledge discovery. The proposed model consists of six main sets: concepts, attributes, relations, transformations, instances, and states. This model is dynamic and facilitates the access, updates, and exploitation of the knowledge at any time. This paper also proposes an architecture for handling this knowledge-based model. The system architecture includes knowledge modelling, extraction, assessment, publishing, and exploitation. This system has been implemented and used in agriculture for crop management and monitoring. It is proven to be very effective and promising for its extension to other domains

Growth, survival and food utilization efficiency of longfin batfish (<em>Platax teira</em> Forsskål, 1775) larvae reared under different salinity levels

Salinity is crucial in fish larval rearing. In longfin batfish (Platax teira), little research has been conducted regarding the specific effects of salinity on growth, survival, deformity, and food utilization efficiency. This study aimed to determine the optimal salinity level for larval rearing of the longfin batfish by testing five different salinity levels (10, 15, 20, 25, and 30‰). Larvae of 1.5 cm in length and 0.2 g/fish in weight were stocked in cylindrical fiberglass tanks (300 L) at a density of 1 fish/L. The fish were fed to meet their dietary requirement and divided into four daily feedings. Each treatment was replicated three times over a 28-day period of rearing. The results revealed that salinity significantly influenced the growth (length, weight, biomass), and food utilization efficiency of the longfin batfish larvae. Overall, larvae reared at salinity levels of 15-20‰ exhibited superior performance compared to those exposed to salinity levels of 10, 25, and 30‰. However, salinity did not affect the coefficient of variation, survival, and deformity. From these findings, it is recommended to rear longfin batfish larvae at a salinity of 15-20‰ to achieve optimal growth and food utilization efficiency. This study provides valuable insights for longfin batfish larval rearing guidance, contributing to the aquaculture development of this economically valuable species

MEPA: A New Protocol for Energy-Efficient, Distributed Clustering in Wireless Sensor Networks

Abstract-Clustering is an effective approach to hierarchically organizing network topology for efficient data aggregation in wireless sensor networks. Distributed protocols with simple local computations to accomplish a desired global goal, offer a good prospect for achieving energy efficiency. This paper presents MEPA -an energy-efficient distributed clustering protocol using simple and local message-passing rules. Our proposed clustering protocol combines both node residual energy and network topology features to recursively elect a near-optimal set of cluster heads. Simulation results show that MEPA can produce a set of cluster heads with compelling characteristics, and effectively prolong the network lifetime

Identifying undamaged-beam status based on singular spectrum analysis and wavelet neural networks

In this paper, the identifying undamaged-beam status  based on singular spectrum analysis (SSA) and wavelet neural networks (WNN)  is presented. First, a database is built from measured sets and SSA which  works as a frequency-based filter. A WNN model is then designed which consists of the wavelet frame building, wavelet structure designing and  establishing a solution for training the WNN. Surveys via an experimental  apparatus for estimating the method are carried out. In this work, a  beam-typed iron frame, Micro-Electro-Mechanical (MEM) sensors and a  vibration-signal processing and measuring system named LAM_BRIDGE are all  used

Broadcast Gossip Based Distributed Hypothesis Testing in Wireless Sensor Networks

We consider the scenario that N sensors collaborate to observe a single event. The sensors are distributed and can only exchange messages through a network to reach a consensus about the observed event. In this paper, we propose a very robust and simple method using broadcast gossip algorithm to solve the distributed hypothesis testing problem. The simulation result shows that our method has good performance and is very energy efficient comparing to existing methods