Implementation of Real-Time Monitoring on Agricultural Land of Rice Plants Using Smart Sensor

Rahmat, R F; Lini, Tifani Zata; Pujiarti,; Hizriadi, Ainul

doi:10.1109/elticom47379.2019.8943912

Cited by 8 publications

(6 citation statements)

References 3 publications

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“…Deep learning algorithms were found to be more effective compared to other machine learning algorithms for modeling paddy rice yield [48], [76], [77], [109]- [112], [157]. Maximum quality of paddy rice harvested can be obtained by using sensors to monitor humidity, temperature, pH, soil moisture and light intensity in real [113], [114].…”

Section: Results and Conclusionmentioning

confidence: 99%

“…Besides that, smart sensors for climate and soil [36], [164], Radio-frequency identification (RFID), load Sensor and Global Positioning System (GPS) are also used in estimating paddy rice yield [113], [114]. Table 3 tabulates the applications of smart sensors (Internet of Things (IoT)) in various tasks involved in paddy rice smart farming.…”

Section: ) Smart Irrigation System For Paddy Ricementioning

confidence: 99%

“…Estimating Paddy Rice Yield [113] Smart sensors for humidity, temperature, pH, soil moisture and light intensity…”

Section: Tasks Study Devices Findingsmentioning

confidence: 99%

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Towards Paddy Rice Smart Farming: A Review on Big Data, Machine Learning, and Rice Production Tasks

et al. 2021

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Big Data (BD), Machine Learning (ML) and Internet of Things (IoT) are expected to have a large impact on Smart Farming and involve the whole supply chain, particularly for rice production. The increasing amount and variety of data captured and obtained by these emerging technologies in IoT offer the rice smart farming strategy new abilities to predict changes and identify opportunities. The quality of data collected from sensors greatly influences the performance of the modelling processes using ML algorithms. These three elements (e.g., BD, ML and IoT) have been used tremendously to improve all areas of rice production processes in agriculture, which transform traditional rice farming practices into a new era of rice smart farming or rice precision agriculture. In this paper, we perform a survey of the latest research on intelligent data processing technology applied in agriculture, particularly in rice production. We describe the data captured and elaborate role of machine learning algorithms in paddy rice smart agriculture, by analyzing the applications of machine learning in various scenarios, smart irrigation for paddy rice, predicting paddy rice yield estimation, monitoring paddy rice growth, monitoring paddy rice disease, assessing quality of paddy rice and paddy rice sample classification. This paper also presents a framework that maps the activities defined in rice smart farming, data used in data modelling and machine learning algorithms used for each activity defined in the production and post-production phases of paddy rice. Based on the proposed mapping framework, our conclusion is that an efficient and effective integration of all these three technologies is very crucial that transform traditional rice cultivation practices into a new perspective of intelligence in rice precision agriculture. Finally, this paper also summarizes all the challenges and technological trends towards the exploitation of multiple sources in the era of big data in agriculture.

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Section: Results and Conclusionmentioning

confidence: 99%

Section: ) Smart Irrigation System For Paddy Ricementioning

confidence: 99%

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Towards Paddy Rice Smart Farming: A Review on Big Data, Machine Learning, and Rice Production Tasks

et al. 2021

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“…The integration of sensors, satellites, and drones complements machine learning tools in improving productivity. Machine learning tools utilize the big data collected from these devices for estimating rice yield [93,94], monitoring growth [94], and providing a smart irrigation system [95]. Kiruthika et al [96] propose a system to detect paddy rice crop diseases such as leaf blast disease, brown spot disease, and bacterial blight disease.…”

Section: Big Data and Artificial Intelligencementioning

confidence: 99%

Disruptive Technologies in Smart Farming: An Expanded View with Sentiment Analysis

Yadav

Kaushik

Sharma³

et al. 2022

AgriEngineering

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Smart Farming (SF) is an emerging technology in the current agricultural landscape. The aim of Smart Farming is to provide tools for various agricultural and farming operations to improve yield by reducing cost, waste, and required manpower. SF is a data-driven approach that can mitigate losses that occur due to extreme weather conditions and calamities. The influx of data from various sensors, and the introduction of information communication technologies (ICTs) in the field of farming has accelerated the implementation of disruptive technologies (DTs) such as machine learning and big data. Application of these predictive and innovative tools in agriculture is crucial for handling unprecedented conditions such as climate change and the increasing global population. In this study, we review the recent advancements in the field of Smart Farming, which include novel use cases and projects around the globe. An overview of the challenges associated with the adoption of such technologies in their respective regions is also provided. A brief analysis of the general sentiment towards Smart Farming technologies is also performed by manually annotating YouTube comments and making use of the pattern library. Preliminary findings of our study indicate that, though there are several barriers to the implementation of SF tools, further research and innovation can alleviate such risks and ensure sustainability of the food supply. The exploratory sentiment analysis also suggests that most digital users are not well-informed about such technologies.

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“…However, this design also still uses LCDs as output outputs to monitor and control the irrigation system based on the internet of things. Table 1 shows the results of a literature review that focuses on controlling the irrigation system so that the output produced can run automatically and can be controlled using the Internet of Things method [13,14,15,16,17,18,19,20,21,22]. Conventional irrigation systems that are still found in Indonesia cause a lack of efficiency and effectiveness of farmers in controlling and monitoring irrigation systems on plants that they have planted to overcome this.…”

Section: Research On Smart Irrigation: State Of the Artmentioning

confidence: 99%

Design a Monitoring and Control in Irrigation Systems using Arduino Wemos with the Internet of Things

Silalahi

Budiyanto

Silaban

et al. 2021

JIAE

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Irrigation door is a big issue for farmers. The factor that became a hot issue at the irrigation gate was the irresponsible attitude of the irrigation staff regarding the schedule of opening/closing the irrigation door so that it caused the rice fields to becoming dry or submerged. In this research, an automatic prototype system for irrigation system will be designed based on integrating several sensors, including water level sensors, soil moisture sensors, acidity sensors. This sensor output will be displayed on Android-based applications. The integration of communication between devices (Arduino Nano, Arduino Wemos and sensors supporting the irrigation system) is the working principle of this prototype. This device will control via an Android-based application to turn on / off the water pump, to open/close the irrigation door, check soil moisture, soil acidity in real time. The pump will automatically turn on based on the water level. This condition will be active if the water level is below 3cm above ground level. The output value will be displayed on the Android-based application screen and LCD screen. Based on the results of testing and analysis of the prototype that has been done in this research, the irrigation door will open automatically when the soil is dry. This condition occurs if the water level is less than 3 cm. The calibrated Output value, including acidity sensor, soil moisture sensor and water level sensor, will be sent to the server every 5 seconds and forwarded to an Android-based application as an output display.

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Implementation of Real-Time Monitoring on Agricultural Land of Rice Plants Using Smart Sensor

Cited by 8 publications

References 3 publications

Towards Paddy Rice Smart Farming: A Review on Big Data, Machine Learning, and Rice Production Tasks

Towards Paddy Rice Smart Farming: A Review on Big Data, Machine Learning, and Rice Production Tasks

Disruptive Technologies in Smart Farming: An Expanded View with Sentiment Analysis

Design a Monitoring and Control in Irrigation Systems using Arduino Wemos with the Internet of Things

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