An Explainable AI Approach to Agrotechnical Monitoring and Crop Diseases Prediction in Dnipro Region of Ukraine

IF 3.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Journal of Artificial Intelligence and Soft Computing Research Pub Date : 2023-10-01 DOI:10.2478/jaiscr-2023-0018
Ivan Laktionov, Grygorii Diachenko, Danuta Rutkowska, Marek Kisiel-Dorohinicki
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Abstract

Abstract The proliferation of computer-oriented and information digitalisation technologies has become a hallmark across various sectors in today’s rapidly evolving environment. Among these, agriculture emerges as a pivotal sector in need of seamless incorporation of high-performance information technologies to address the pressing needs of national economies worldwide. The aim of the present article is to substantiate scientific and applied approaches to improving the efficiency of computer-oriented agrotechnical monitoring systems by developing an intelligent software component for predicting the probability of occurrence of corn diseases during the full cycle of its cultivation. The object of research is non-stationary processes of intelligent transformation and predictive analytics of soil and climatic data, which are factors of the occurrence and development of diseases in corn. The subject of the research is methods and explainable AI models of intelligent predictive analysis of measurement data on the soil and climatic condition of agricultural enterprises specialised in growing corn. The main scientific and practical effect of the research results is the development of IoT technologies for agrotechnical monitoring through the development of a computer-oriented model based on the ANFIS technique and the synthesis of structural and algorithmic provision for identifying and predicting the probability of occurrence of corn diseases during the full cycle of its cultivation.
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乌克兰第聂伯罗地区农业技术监测和作物病害预测的可解释人工智能方法
在当今快速发展的环境中,面向计算机和信息数字化技术的扩散已经成为各个部门的标志。其中,农业成为需要无缝整合高性能信息技术以满足全球各国经济紧迫需求的关键部门。本文的目的是通过开发一种智能软件组件来预测玉米在整个种植周期内发生病害的概率,从而为提高计算机农业技术监测系统的效率提供科学和实用的方法。研究对象是玉米病害发生和发展的因素土壤和气候数据的非平稳智能转化和预测分析过程。该研究的主题是对专门种植玉米的农业企业的土壤和气候条件测量数据进行智能预测分析的方法和可解释的人工智能模型。研究成果的主要科学和实际效果是,通过基于ANFIS技术开发面向计算机的模型,并综合结构和算法规定,识别和预测玉米全种植周期内发生病害的概率,开发了农业技术监测的物联网技术。
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来源期刊
Journal of Artificial Intelligence and Soft Computing Research
Journal of Artificial Intelligence and Soft Computing Research COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
7.00
自引率
25.00%
发文量
10
审稿时长
24 weeks
期刊介绍: Journal of Artificial Intelligence and Soft Computing Research (available also at Sciendo (De Gruyter)) is a dynamically developing international journal focused on the latest scientific results and methods constituting traditional artificial intelligence methods and soft computing techniques. Our goal is to bring together scientists representing both approaches and various research communities.
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