Adaptive control and supply chain management of intelligent agricultural greenhouse by intelligent fuzzy auxiliary cognitive system
Author:
- 27 July 2022
- journal article
- retracted article
- Published by Wiley in Expert Systems
- Vol. 41 (5)
Abstract
This work aims to avoid the complex control process caused by complex coupling and serious interference of control variables in traditional greenhouse environmental control and achieve the adaptive control of agricultural greenhouse. This work studies smart agricultural greenhouses' adaptive control and supply chain management based on the intelligent fuzzy auxiliary cognitive system (IFACS). This work firstly expresses the intelligent fuzzy control theory and analyses data according to the fuzzy control set. Secondly, the adaptive control and supply chain management of the intelligent agricultural greenhouse are studied by using an intelligent fuzzy aided cognitive system. Finally, the light intensity and carbon dioxide content of the intelligent agricultural greenhouse are analysed; the temperature and humidity content of the intelligent agricultural greenhouse of the traditional control method and adaptive control are compared; the impact of carbon dioxide emissions on the environment under supply chain management is analysed. The research results indicate that the adaptive control of the intelligent agricultural greenhouse based on the IFACS can keep the greenhouse temperature within a specific range and reduce the impact of temperature on the development of agricultural greenhouses. The IFACS is effectively managed in the adaptive control of the intelligent agricultural greenhouse. From 2015 to 2020, the smart agricultural greenhouse supply chain management gradually reduced the content of greenhouse gas (GHG) emitted by the production and suppliers through the IFACS. The content of greenhouse gases emitted by product transporters did not change significantly, and the greenhouse gases emitted by non‐manufacturing suppliers gradually decreased. In 2020, supply chain GHG emission intensity decreased by 20% compared with 2015, and GHG emissions from product transportation also decreased by 6% compared with the previous year. This research has reference significance for the adaptive control of intelligent agricultural greenhouse by IFACS and the environmental control of agricultural greenhouse by supply chain management.Keywords
This publication has 25 references indexed in Scilit:
- Membership-Function-Dependent Stabilization of Event-Triggered Interval Type-2 Polynomial Fuzzy-Model-Based Networked Control SystemsIEEE Transactions on Fuzzy Systems, 2019
- Ventilation online monitoring and control system from the perspectives of technology applicationIndoor and Built Environment, 2019
- Sustainable Economic Development and Greenhouse Gas Emissions: The Dynamic Impact of Renewable Energy Consumption, GDP, and CorruptionEnergies, 2019
- Profit Analysis of Papaya Crops under Greenhouses as an Alternative to Traditional Intensive Horticulture in Southeast SpainInternational Journal of Environmental Research and Public Health, 2019
- Combining the effects of increased atmospheric carbon dioxide on protein, iron, and zinc availability and projected climate change on global diets: a modelling studyThe Lancet Planetary Health, 2019
- Analysis of intelligent agricultural system and control mode based on fuzzy control and sensor networkJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology, 2019
- Supervisory Model Predictive Control for Optimal Energy Management of Networked Smart Greenhouses Integrated MicrogridIEEE Transactions on Automation Science and Engineering, 2019
- Remote Control of Greenhouse Vegetable Production with Artificial Intelligence—Greenhouse Climate, Irrigation, and Crop ProductionSensors, 2019
- Development of Wireless Sensor Node and Controller Complying with Communication Interface Standard for Smart FarmingJournal of Biosystems Engineering, 2019
- Pythagorean fuzzy set and its application in career placements based on academic performance using max–min–max compositionComplex & Intelligent Systems, 2019