A model-based methodology for the early warning detection of cucumber downy mildew in greenhouses: An experimental evaluation

Liu, Ran; Wang, Hui; Guzmán, José Luís; Li, Ming

doi:10.1016/j.compag.2022.106751

Cited by 16 publications

(10 citation statements)

References 25 publications

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“…For example, depthwise convolutional networks can find the Red Palm Weevil (RPW) with a 95.70% ± 1.46% accuracy [59], and the proposed deep learning Faster Regions with Convolutional Neural Networks (R-CNN) has the best recognition accuracy at 99.0% [60]. An early warning system for pest attacks on cucumber downy mildew using experimental evaluation method based on weather forecast input is implemented [61].…”

Section: Related Workmentioning

confidence: 99%

A Novel Controlling System for Smart Farming-based Internet of Things (IoT)

Setyawan,

Marjunus

et al. 2024

ijacsa

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The integration of IoT systems in agriculture has become a very important need amid the high population and increasingly limited farmland, which demands researchers to be more innovative in addressing these issues. Using IoT systems for automatic irrigation, fertilization, and cooling based on sensor values through internet networks. Poor internet connection leads to the failure of automation and sustainability in online conditions, which can be very dangerous for plants. This paper presents a new IoT-based control system divided into two parts: an automation system and an IoT system, which can maintain sustainability in online conditions to ensure that plants in the planting area are always controlled. In addition, the sensors used have undergone calibration processes to determine the increase in precision of the sensor values produced. The research results show that the system can maintain sustainability under online conditions. Mobile apps are available for control when the system is online, but if it goes offline and is unable to reconnect, the Arduino Mega will fully manage control using soil moisture sensor values for irrigation processes if the values fall below a certain threshold. This demonstrates the sustainability of the system in online conditions, allowing continuous control and reducing the risk of plant death in the planting area. The calibration result shows an increase in precision for the air temperature and humidity (DHT 11 sensor) by 7.14 and 6.15, respectively. Additionally, the precision improvement for the soil pH sensor is 1.81, while for the soil moisture sensor and the water flow sensor, it is 0.13 and 0.008, respectively.

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Section: Related Workmentioning

confidence: 99%

A Novel Controlling System for Smart Farming-based Internet of Things (IoT)

Setyawan,

Marjunus

et al. 2024

ijacsa

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“…Using greenhouse simulation models, one can analyze the interactions between greenhouse plants and climate, including the shape of the structure, characteristics of the cover, the climate control equipment, and the surrounding weather conditions [23]. Analyzing the greenhouse climate becomes a complete task because of the enormous variety of boundary conditions and design elements.…”

Section: Greenhouse Simulation Modelsmentioning

confidence: 99%

Application of Internet of Things (IoT) in Protected Cultivation: A Review

Siddharam,

Aiswarya,

Kambale

et al. 2023

IJECC

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In step with the Food and Agriculture Organization of United Nations report food production is predicted to extend about 70 per cent in 2050 to fulfil the demand of food because the world’s population will exceed nine billion by that time. In agriculture, improving crop yield is crucial to fulfil the swiftly growing demand of food for population intensification. There are a lot of factors to manage for crop yield, including temperature, humidity, light intensity, moisture and carbon dioxide concentration. Greenhouse farming is an ‘all season garden’ method of providing favourable environmental conditions to plants by growing them under a framed structure covered with a transparent material. These protected agriculture uses artificial techniques to create environmental conditions suitable for the growth plants, with automated controls. With the development of agricultural sensor, wireless communication, cloud computing, and machine learning, IoT technology has emerged and is gradually being promoted and applied in the protected agriculture field. The major components in internet of things (IoT) include sensors, gateway, connectivity, cloud, analytics and user interface. Among these, sensors are the key component used for the continuous monitoring of greenhouse environment. There are different type of sensors used for monitoring each environmental factors. The physical layer, software, and sensors layer under data acquisition are linked wirelessly through standard communication protocols for transmitting data to a central base station for real-time or offline processing. Due to the importance of greenhouse climatic conditions and their effect on quantitative and qualitative crop yield, greenhouse climate simulation models are used, which includes climate modelling and crop growth models.

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“…The worldwide greenhouse area is roughly 3.64 million hectares, based on statistical data [3]. In recent years, food production in greenhouses has increased rapidly [4]; consequently, food production in greenhouses is a promising strategy for future sustainability [5] growing plants in greenhouses that requires a comprehensive consideration of both design and workflow [6], as well as greenhouse heat transfer [7]. The ability to regulate the atmosphere within a house is contingent on the size of the house.…”

Section: Introductionmentioning

confidence: 99%

Using Greenhouse Modelling to Identify the Optimal Conditions for Growing Crops in Northeastern Thailand

Wangkahart¹,

Junsiri²,

Srichat³

et al. 2022

MMEP

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The most essential components in escort farming are the determination of airflow patterns and the distribution impact of microclimate temperature variables. The objective of this study is to use Computational Fluid Dynamics (CFD) models to ascertain the aerodynamic and thermal dynamics of protected agricultural greenhouses suitable for cultivation in tropical Northeastern crops of Thailand by defining differences in greenhouse layouts, specifically Gable Roof (GR), Curved Roof (CR), Saw tooth roof (SR), and Double roof (DR). The root means square error (RMSE) indicated that the factor determination of CFD modelling using the standard k−ε turbulence model was satisfactory. In addition, they found that, at a significance level of 0.05, the average temperature in the greenhouses was statistically distinct based on the form of the back of the house and the location of the air input and outflow.

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A model-based methodology for the early warning detection of cucumber downy mildew in greenhouses: An experimental evaluation

Cited by 16 publications

References 25 publications

A Novel Controlling System for Smart Farming-based Internet of Things (IoT)

A Novel Controlling System for Smart Farming-based Internet of Things (IoT)

Application of Internet of Things (IoT) in Protected Cultivation: A Review

Using Greenhouse Modelling to Identify the Optimal Conditions for Growing Crops in Northeastern Thailand

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