Review—Machine Learning Techniques in Wireless Sensor Network Based Precision Agriculture

Mekonnen, Yemeserach; Namuduri, Srikanth; Burton, L. K.; Sarwat, Arif I.; Bhansali, Shekhar

doi:10.1149/2.0222003jes

Cited by 201 publications

(105 citation statements)

References 60 publications

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“…It was proved that the implementation of these big data techniques and tools in the agriculture arena resulted in several benefits. They could support the product control and cost reduction when applying big data [108]. Food safety and security were improved through product traceability technology [109].…”

Section: Techniques and Tools For Big Data Analysis In Agriculture Prmentioning

confidence: 99%

Enhancing Coffee Supply Chain towards Sustainable Growth with Big Data and Modern Agricultural Technologies

2021

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Modern agricultural technology management is nowadays crucial in terms of the economy and the global market, while food safety, quality control, and environmentally friendly practices should not be neglected. This review aims to give perspectives on applying big data analytic and modern technologies to increase the efficacy and effectiveness of the coffee supply chain throughout the process. It was revealed that several tools such as wireless sensor networks, cloud computing, Internet of Things (IoT), image processing, convolutional neural networks (CNN), and remote sensing could be implemented in and used to improve the coffee supply chain. Those tools could help in reducing cost as well as time for entrepreneurs and create a reliable service for the customer. It can be summarized that in the long term, these modern technologies will be able to assist coffee business management and ensure the sustainable growth for the coffee industry.

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Section: Techniques and Tools For Big Data Analysis In Agriculture Prmentioning

confidence: 99%

Enhancing Coffee Supply Chain towards Sustainable Growth with Big Data and Modern Agricultural Technologies

2021

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“…Table 1 shows several reviews that were conducted recently that are related to smart farming or precision agriculture. Several reviews conducted were focusing on the application of machine learning algorithms in smart farming [16], [17]. For instance, Sharma et al investigated the current state of research on machine learning (ML) applications in Agriculture Supply Chain (ASC) that includes the application of ML in four different phases in ASC; pre-production, production, processing and distribution [16].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Mekonnen et al conducted a review on the application of various machine learning methods in analyzing data captured from sensors within the agricultural ecosystem [17]. In this review, a limited number of machine learning algorithms is listed based on the data that are captured using different types of Wireless Sensor Networks (WSN) (e.g., ZigBee WSN, GSM and GPS WSN, LoRa WSN, Wifi and MQTT Sensor based with Raspberry pi and Arduino) and also remotely sensed data (multispectral or hyperspectral data) and vegetation indices.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The evolution of agriculture systems involves the adoption of incoming data from various sources [27] and also the application of big data applications in smart farming [26]. Together these big data technologies and the capability of machine learning algorithms in forecasting certain outcomes will cause major changes in the scope and organization of smart farming [16], [17], [26]. Lytos et al conducted a survey paper that covers the state-of-the-art big data architectures and agriculture systems in order to bridge the knowledge gap between agriculture systems and exploitation of big data.…”

Section: Literature Reviewmentioning

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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“…Mekonnen [12] described a WSN based on Arduino and ZigBee communication protocol that includes an off-grid photovoltaic cell to supply power to the system. They collect sensor data in a cloud platform to provide graphs about the sensor data evolution.…”

Section: State Of the Artmentioning

confidence: 99%

Autonomous Sensor Network for Rural Agriculture Environments, Low Cost, and Energy Self-Charge

et al. 2020

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Over the last years, existing technologies have been applied to agricultural environments, resulting in new precision agriculture systems. Some of the multiple profits of developing new agricultural technologies and applications include the cost reduction around the building and deployment of them, together with more energy-efficient consumption. Therefore, agricultural precision systems focus on developing better, easier, cheaper, and overall more efficient ways of handling agricultural monitoring and actuation. To achieve this vision, we use a set of technologies such as Wireless Sensor Networks, Sensors devices, Internet of Things, or data analysis. More specifically, in this study, we proposed a combination of all these technologies to design and develop a prototype of a precision agriculture system for medium and small agriculture plantations that highlights two major advantages: efficient energy management with self-charging capabilities and a low-cost policy. For the development of the project, several prototype nodes were built and deployed within a sensor network connected to the cloud as a self-powered system. The final target of this system is, therefore, to gather environment data, analyze it, and actuate by activating the watering installation. An analysis of the exposed agriculture monitoring system, in addition to results, is exposed in the paper.

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Review—Machine Learning Techniques in Wireless Sensor Network Based Precision Agriculture

Cited by 201 publications

References 60 publications

Enhancing Coffee Supply Chain towards Sustainable Growth with Big Data and Modern Agricultural Technologies

Enhancing Coffee Supply Chain towards Sustainable Growth with Big Data and Modern Agricultural Technologies

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

Autonomous Sensor Network for Rural Agriculture Environments, Low Cost, and Energy Self-Charge

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