Faeze Behzadipour’s research while affiliated with Sari Agricultural Sciences and Natural Resources University and other places

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Publications (2)


Schematic of intelligent irrigation system in the greenhouse
(1) Submersible pump (2) Water source (3) Pipe (4) Intelligent control box (5) Flow meter (6) Solenoid valve (7) Soil humidity and temperature sensor (8) Wi-Fi (9) Laptop, (10) wire.
Pictures of the intelligent irrigation system in the greenhouse
The effect of different irrigation treatments on crop yield (kg.m⁻²)
The effect of different irrigation treatments on the length (cm) and diameter (mm) of the crop
The effect of different irrigation treatments on tissue firmness (kg) and dry matter percentage of the crop

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Optimizing water use efficiency in greenhouse cucumber cultivation: A comparative study of intelligent irrigation systems
  • Article
  • Full-text available

October 2024

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115 Reads

Faeze Behzadipour

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Mahmoud Ghasemi-Nejad-Raeini

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[...]

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This study investigated the use of an intelligent irrigation system for greenhouse cucumber cultivation, aiming to manage water consumption efficiently. During the initial phase, irrigation was tested at four levels: 80%, 90%, and 100% of Field Capacity (FC), and Conventional Flood Irrigation (CFI). Data on environmental conditions and water usage were meticulously recorded. Optimal yields and crop quality (measured by size and firmness) were achieved at CFI and 100% FC, with CFI consuming the most water (0.148 m³/m²) Consequently, 100% FC was identified as the best practice, informing the intelligent system’s calibration in the subsequent phase. This adjustment resulted in reduced water consumption and a 15.6% improvement in Water Use Efficiency (WUE) over CFI. Additionally, by examining the product performance and the color characteristics, chlorophyll content, and photosynthesis of the leaves, it was observed that the quality and optimal water supply and the product performance were maintained in the smart irrigation system. The study concludes that, considering long-term outcomes, the intelligent irrigation system is preferable to CFI, offering significant water savings and enhanced WUE without compromising crop quality.

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A smart IoT-based irrigation system design using AI and prediction model

September 2023

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356 Reads

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13 Citations

Neural Computing and Applications

Implementing intelligent irrigation and adjusting the irrigation system is essential in today’s agricultural system to control the amount of water required for the plant. This study focuses on data obtained from sensors measuring (soil temperature and humidity, temperature and humidity of ambient and light) and image processing of plant leaves. To analyze the data, two models were implemented including a regression model in SPSS software and another model by genetic programming in MATLAB 2018 software. The optimal model was a combined model of sensors and images in genetic programming with higher R2 and lower standard error of 0.88 and 0.03, respectively. This model was superior to the regression model which had an R2 and standard error of 0.86 and 0.21, respectively, so this optimal model was selected to adjust the microcontroller for the intelligent irrigation system. The following year by replanting the crop, the intelligent irrigation system was presented as the superior system with 11% water saving compared to the previous year (irrigation by the user). Also, no changes were observed in the yield and color indicators of the plant at the level of 5%, which indicates the superiority of the intelligent irrigation system and its high accuracy of this system.

Citations (1)


... Several studies have demonstrated the potential of AI to optimize specific agricultural practices. For instance, Behzadipour et al. (2023) designed an IoT-based smart irrigation system integrated with AI and predictive models. This system saved up to 11% of irrigation water compared to traditional methods, while maintaining high crop yields. ...

Reference:

Smart Viniculture: Applying Artificial Intelligence for Improved Winemaking and Risk Management
A smart IoT-based irrigation system design using AI and prediction model

Neural Computing and Applications