Complex Event Processing Methods for Greenhouse Control

Jia, Yunsong; Li, Xiang

doi:10.3390/agriculture11090811

Cited by 6 publications

(2 citation statements)

References 27 publications

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“…Several interactions between the outside conditions, the greenhouse climate and the greenhouse crop exist with strong variation of the adjusted parameters according to the development stage of the crops [37]. So many control parameters in the greenhouse still need to be dynamically identified for suitable plant growth [38][39][40].…”

Section: Effects Of Microclimate On Crop Growth In a Closed Greenhous...mentioning

confidence: 99%

Opportunities for Implementing Closed Greenhouse Systems in Arid Climate Conditions

2022

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The closed greenhouse is an innovative crop system in the horticulture sector, integrating appropriate climate control equipment and optimized techniques to collect, store, and reuse solar energy for heating and/or cooling the greenhouse. This concept aims to improve the crop yield and quality with energy efficient and water-saving technologies. A specific focus on the opportunities of implementing closed greenhouses under arid climate conditions is detailed in this work. Guidelines for selecting appropriate techniques and design parameters are investigated, aiming for profitable and sustainable greenhouse production. This paper provides an overview of the design aspects of the closed greenhouse and a state of the art of its applications in arid areas. Firstly, the microclimate parameters, including temperature, relative humidity (RH), light intensity, and CO2 concentration are introduced. Then, an in-depth focus on the effects of these parameters on crop productivity, water, and energy efficiency are thoroughly discussed. Finally, the limitations of closed greenhouse applications are pointed out as opportunities for further research and development in this emerging agriculture field.

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Section: Effects Of Microclimate On Crop Growth In a Closed Greenhous...mentioning

confidence: 99%

Opportunities for Implementing Closed Greenhouse Systems in Arid Climate Conditions

2022

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“…Mass spectrometry (MS) was probably one of the most frequently used confirmation methods to predict pesticide residue levels during the risk assessment stage [8]. To get mass spectrometry, it first detaches that material's particles into ions of diverse masses.…”

Section: Introductionmentioning

confidence: 99%

Q-Learning-Based Pesticide Contamination Prediction in Vegetables and Fruits

Sellamuthu¹,

Kaliappan²

2023

Computer Systems Science and Engineering

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Pesticides have become more necessary in modern agricultural production. However, these pesticides have an unforeseeable long-term impact on people's wellbeing as well as the ecosystem. Due to a shortage of basic pesticide exposure awareness, farmers typically utilize pesticides extremely close to harvesting. Pesticide residues within foods, particularly fruits as well as veggies, are a significant issue among farmers, merchants, and particularly consumers. The residual concentrations were far lower than these maximal allowable limits, with only a few surpassing the restrictions for such pesticides in food. There is an obligation to provide a warning about this amount of pesticide use in farming. Previous technologies failed to forecast the large number of pesticides that were dangerous to people, necessitating the development of improved detection and early warning systems. A novel methodology for verifying the status and evaluating the level of pesticides in regularly consumed veggies as well as fruits has been identified, named as the Hybrid Chronic Multi-Residual Framework (HCMF), in which the harmful level of used pesticide residues has been predicted for contamination in agro products using Q-Learning based Recurrent Neural Network and the predicted contamination levels have been analyzed using Complex Event Processing (CEP) by processing given spatial and sequential data. The analysis results are used to minimize and effectively use pesticides in the agricultural field and also ensure the safety of farmers and consumers. Overall, the technique is carried out in a Python environment, with the results showing that the proposed model has a 98.57% accuracy and a training loss of 0.30.

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