A novel approach to intelligent monitoring of gas composition and light mode of greenhouse crop growing zone on the basis of fuzzy modelling and human-in-the-loop techniques

Laktionov, Ivan; Rutkowski, Leszek; Vovna, Oleksandr; Byrski, Aleksander; Kabanets, Maryna

doi:10.1016/j.engappai.2023.106938

Cited by 6 publications

(1 citation statement)

References 14 publications

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“…Taking into account the theoretical and applied features of the research problem and prior experience in developing applied information systems based on fuzzy logic [16,17,49], the Takagi-Sugeno algorithm (type 1) was chosen as the baseline algorithm.…”

Section: Algorithm Of Neuro-fuzzy Transformation Techniquementioning

confidence: 99%

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

Laktionov,

Diachenko,

Rutkowska

et al. 2023

Journal of Artificial Intelligence and Soft Computing Research

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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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Section: Algorithm Of Neuro-fuzzy Transformation Techniquementioning

confidence: 99%

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

Laktionov,

Diachenko,

Rutkowska

et al. 2023

Journal of Artificial Intelligence and Soft Computing Research

Self Cite

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Process safety 4.0: Artificial intelligence or intelligence augmentation for safer process operation?

Arunthavanathan,

Sajid,

Amin

et al. 2024

AIChE Journal

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The growth of artificial intelligence (AI) has allowed industries to automate and improve their efficiency in operations. Especially in process industries, AI helps to develop intelligent models and tools to proactively monitor and predict equipment or system failures, minimize downtime, and optimize maintenance schedules. With the advancements in AI and its ability to perform tasks, there is a growing belief that AI may eventually replace humans. However, the absence of human involvement in operations in the process industry raises safety concerns. Therefore, AI should collaborate with humans rather than replace them in processing facility operations. This technology is referred to as intelligence augmentation (IA). This article (i) presents a detailed comparison between AI and IA's potential in process systems, (ii) identifies the feasibility of using AI and IA in process safety, and (iii) identifies the risk associated with the implementation of AI or IA in process industries.

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An optimized approach to hourly temperature and humidity setpoint generation for reducing tomato disease and saving power cost in greenhouses

Wang,

Laktionov,

Rodríguez Díaz

et al. 2024

Computers and Electronics in Agriculture

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A novel approach to intelligent monitoring of gas composition and light mode of greenhouse crop growing zone on the basis of fuzzy modelling and human-in-the-loop techniques

Cited by 6 publications

References 14 publications

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

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

Process safety 4.0: Artificial intelligence or intelligence augmentation for safer process operation?

An optimized approach to hourly temperature and humidity setpoint generation for reducing tomato disease and saving power cost in greenhouses

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