Internet of Things based Smart Irrigation Control System for Paddy Field

Liu, Liwei; Ismail, Mohd Hasmadi; Wang, Yumin; Lin, Wen-Shin

doi:10.17503/agrivita.v43i2.2936

Cited by 10 publications

(5 citation statements)

References 29 publications

(32 reference statements)

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“…In this system, fuzzy logic was used to control the flow of water and control the flow of alkaline and acid solutions in the soil to maintain its humidity and pH level. In [25], the authors presented a water-saving method based on Smart Field Cultivation Server (SFCS) for paddy fields. SFCS considered additional data for requirement of rice growth, pest development, and wildlife management.…”

Section: Review Discussionmentioning

confidence: 99%

Study of applications of Internet of Things and Machine Learning for Smart Agriculture

Gupta

Malhotra

Kumar

2022

2022 IEEE International Conference on Current Development in Engineering and Technology (CCET)

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The concept of "smart agriculture" relies on the integration of sensors within an Internet of Things (IoT) network. Machine learning (ML) algorithms are integrated at various levels of the IoT system design to augment its functionality and enhance its capability. This article is a bibliometric review of 42 articles published between 2018 and 2022 using the Web of Science database. The results of the review showed an exponential growth in the use of ML algorithms in IoT systems for different agriculture applications. Additionally, two key research questions are addressed in this article, one being the development of IoT-ML-enabled smart agriculture over the past five years, and the second being the main research gaps for applications of machine learning and IoT in smart agriculture. The article concludes with a discussion of the results and future directions for research in the field.

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Section: Review Discussionmentioning

confidence: 99%

Study of applications of Internet of Things and Machine Learning for Smart Agriculture

Gupta

Malhotra

Kumar

2022

2022 IEEE International Conference on Current Development in Engineering and Technology (CCET)

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“…In addition, the established rice blast forecasting model in this study is based on large-scale environment observation. We suggest that in-situ environment monitoring system, e.g., Internet of Things based smart irrigation control system [62], may be used to collect microclimate for each field and to develop the small-scale rice blast forecasting model in further studies. Moreover, the large-scale dataset with advanced ML even deep learning algorithms, such as long short-term memory (LSTM), random vector functional link network (RVFL) and generative adversarial network (GAN), may be considered for the further research on rice blast forecasting.…”

Section: Discussionmentioning

confidence: 99%

Rice Blast (Magnaporthe oryzae) Occurrence Prediction and the Key Factor Sensitivity Analysis by Machine Learning

et al. 2021

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This study aimed to establish a machine learning (ML)-based rice blast predicting model to decrease the appreciable losses based on short-term environment data. The average, highest and lowest air temperature, average relative humidity, soil temperature and solar energy were selected for model development. The developed multilayer perceptron (MLP), support vector machine (SVM), Elman recurrent neural network (Elman RNN) and probabilistic neural network (PNN) were evaluated by F-measures. Finally, a sensitivity analysis (SA) was conducted for the factor importance assessment. The study result shows that the PNN performed best with the F-measure (β = 2) of 96.8%. The SA was conducted in the PNN model resulting in the main effect period is 10 days before the rice blast happened. The key factors found are minimum air temperature, followed by solar energy and equaled sensitivity of average relative humidity, maximum air temperature and soil temperature. The temperature phase lag in air and soil may cause a lower dew point and suitable for rice blast pathogens growth. Through this study’s results, rice blast warnings can be issued 10 days in advance, increasing the response time for farmers preparing related preventive measures, further reducing the losses caused by rice blast.

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“…The management of the indoor environment is essential to ensuring the health of plants. To produce an appropriate environment for the care of plants [23]- [25], it is necessary to carefully monitor and control these variables without causing mistakes. This requires knowing the logical relationship between humidity, temperature, photosynthesis, and carbon monoxide concentration.…”

Section: Problem Definition and Algorithmic Solutionmentioning

confidence: 99%

Smart indoor gardening: elevating growth, health, and automation

Alrawashdeh,

Alkore Alshalabi,

Al-Jaafreh

et al. 2024

Bulletin EEI

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Recently, indoor systems for growing plants have emerged as a promising approach to address the problems related to extreme weather conditions outdoors. However, such systems must manage the plants surrounding environments to satisfy the environmental and the economical requirements. In this line, any proposed solution must address challenging factors such as plants diseases and unordinary climate situations. In this paper, propose an internet of things (IoT) indoor system that can be used to facilitate the plant growing process. The proposed system is designed to provide alternatives for outdoor climate dependency such as the vitamins provided through sunlight. Moreover, renewable energy sources (sunlight) are employed to reduce the impact on the environment. With the help of several types of sensors, the system continuously monitors the plants through their growing journey. Whereas actuator devices are employed to control the plant-feeding process based on the sensors’ reported values. All the collected data will be uploaded to the cloud for analysis, utilizing a website. Additionally, the architecture of the provided system eliminates the need for human involvement, which has a degrading effect on the plant growing process.

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Internet of Things based Smart Irrigation Control System for Paddy Field

Cited by 10 publications

References 29 publications

Study of applications of Internet of Things and Machine Learning for Smart Agriculture

Study of applications of Internet of Things and Machine Learning for Smart Agriculture

Rice Blast (Magnaporthe oryzae) Occurrence Prediction and the Key Factor Sensitivity Analysis by Machine Learning

Smart indoor gardening: elevating growth, health, and automation

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