An Interpretable Artificial Intelligence Based Smart Agriculture System

作     者：Fariza Sabrina Shaleeza Sohail Farnaz Farid Sayka Jahan Farhad Ahamed Steven Gordon 

作者机构：School of Engineering and TechnologyCentral Queensland UniversitySydneyAustralia College of EngineeringScience and EnvironmentThe University of NewcastleSydneyAustralia School of Social SciencesWestern Sydney UniversitySydneyAustralia Department of Environmental SciencesMacquarie UniversitySydneyAustralia School of ComputingData and MathematicalSciencesWestern Sydney UniversitySydneyAustralia School of Engineering and TechnologyCentral Queensland UniversityCairnsAustralia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第72卷第8期

页      面：3777-3797页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0801[工学-力学（可授工学、理学学位）] 

基　　金：This work was supported by the Central Queensland University Research Grant RSH5345(partially)and the Open Access Journal Scheme 

主　　题：Explainable artificial intelligence fuzzy logic internet of things machine learning sensors smart agriculture 

摘      要：With increasing world population the demand of food production has increased *** of Things(IoT)based smart agriculture system can play a vital role in optimising crop yield by managing crop requirements in *** can be an important factor to make such systems trusted and easily adopted by *** this paper,we propose a novel artificial intelligence-based agriculture system that uses IoT data to monitor the environment and alerts farmers to take the required actions for maintaining ideal conditions for crop *** strength of the proposed system is in its interpretability which makes it easy for farmers to understand,trust and use *** use of fuzzy logic makes the system customisable in terms of types/number of sensors,type of crop,and adaptable for any soil types and weather *** proposed system can identify anomalous data due to security breaches or hardware malfunction using machine learning *** ensure the viability of the system we have conducted thorough research related to agricultural factors such as soil type,soil moisture,soil temperature,plant life cycle,irrigation requirement and water application timing for Maize as our target *** experimental results show that our proposed system is interpretable,can detect anomalous data,and triggers actions accurately based on crop requirements.