An efficient LoRa-based smart agriculture management and monitoring system using wireless sensor networks

Prasad, S. J. Suji; Thangatamilan, M.; Suresh, M.; Panchal, Hitesh; Rajan, Christober Asir; Sagana, C.; Gunapriya, B.; Sharma, Aditi; Panchal, Tusharkumar; Sadasivuni, Kishor Kumar

doi:10.1080/01430750.2021.1953591

Cited by 97 publications

(13 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e system improved the management efficiency of crops and saved a lot of manpower and material resources [4]. Meng et al is committed to designing and researching a crop growth monitoring system with strong reliability and high stability.…”

Section: Literature Reviewmentioning

confidence: 99%

Exploration of Agricultural Economic Management Methods under Internet + Mode

Luo

2022

Mobile Information Systems

View full text Add to dashboard Cite

In order to solve the problems that the production management of agricultural greenhouses consumes large human resources, low management efficiency, and the limitations of environmental monitoring, a visualized smart agriculture based on ZigBee and WiFi dual-protocol fusion Wireless Sensor Networks (WSNs) technology is proposed. A management system is proposed. The system takes STM32 as the main control core and builds a visual cloud computing platform based on the ESP8266WiFi module. The environmental parameters of the greenhouse are sent to the ZigBee terminal device through the serial port, and the user can remotely monitor the environmental parameters of the greenhouse in real time through the computer and mobile client, and control the working status of each actuator. The test results show that: (Transmission Control Protocol/Internet Protocol, TCP/IP) communication is not abnormal; under the control of the visual management system, when the system sends out control commands, the response time of each output terminal is basically between 1 and 2 s. The response of the output control unit has high sensitivity. Affected by the network delay, the fourth group of test data is larger, and in the case of network delay, the response sensitivity of each output terminal of the system decreases accordingly. Conclusion. The system has high accuracy of data collection, strong work reliability, can realize precise remote control, and has low cost, high stability, easy operation, and has certain promotion value.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Exploration of Agricultural Economic Management Methods under Internet + Mode

Luo

2022

Mobile Information Systems

View full text Add to dashboard Cite

show abstract

“…This explains why a lot of work in the field of smart agriculture uses solutions based on LoRa. An efficient LoRa-based smart agriculture is presented in [21] for humidity and temperature monitoring uses. In [22], the authors present novel watering management based on sensor nodes using LoRa technology for communication.…”

Section: The State Of the Artmentioning

confidence: 99%

Interference-Aware Nodes Deployment of a LoRa-Based Architecture for Smart Agriculture in the Southern Region of Senegal

Ndoye¹,

Diallo²,

Hakem³

et al. 2022

Adv. sci. technol. eng. syst. j.

View full text Add to dashboard Cite

In Senegal, agriculture has always been seen as the foundation on which the socioeconomic development of the country rests. However, in the rural world, agriculture remains traditional at a time when the challenges of food self-sufficiency to accompany emergence are launched. In the southern part of Senegal commonly called Casamance, the abundance of rain makes it possible to practice rice cultivation and market gardening research must therefore play a leading role in the introduction of technological innovations, techniques, and decision-support tools to promote productive, competitive, and sustainable agriculture. Therefore, smart agriculture must focus on new solutions for water irrigation, soil quality, and culture monitoring. The emergence of the Internet of Things (IoT) is perceived as a very important lever for successful high-end intelligent agriculture. Indeed, the appearance of increasingly specialized monitoring sensors combined with new wireless communication technologies constitutes good decision-making tools. The proposal of this paper consists of a new network architecture that can cover a large cultivation area to carry out water irrigation techniques in Casamance. It is, therefore, a question of identifying the best communication technology among new Low-Power, WideArea Networks (LPWANs) such as Long-Range (LoRa), SigFox, etc which is suited to the environment considered. Also, the choice of the best deployment of sensors for better coverage. The choice of technology must be motivated by the financial costs and the range of transmission. The deployment must fix the optimal distance between the sensors minimizing the interferences according to some parameters specific to the environment. An analytical study is used on the deployment to determine the optimal distance between two gateway nodes to reduce induced interference.

show abstract

“…The emergence of new sensing capabilities and revolutionary IoT and machine learning technologies are ideal technologies for effective soil fertility management to overcome the problems associated with traditional fertility mapping and monitoring [12]. IoT and machine learning are revolutionary technologies that have shown revolutionary changes in every sphere including agriculture in the form of smart agriculture [13], [14].…”

Section: Introductionmentioning

confidence: 99%

Internet of Things (IoT) Assisted Context Aware Fertilizer Recommendation

et al. 2022

View full text Add to dashboard Cite

An accurate amount of fertilizer according to the real-time context is the basis of precision agriculture in terms of sustainability and profitability. Many fertilizers recommendation systems are proposed without considering the real-time context in terms of soil fertility level, crop type, and soil type. The major obstacle in developing the real-time context-aware fertilizer recommendation system is related to the complexity associated with the real-time mapping of soil fertility. Furthermore, the existing methods of determining the real-time soil fertility levels for the recommendation of fertilizer are costly, timeconsuming, and laborious. Therefore, to tackle this issue, we propose a machine learning-based fertilizer recommendation methodology according to the real-time soil fertility context captured through the Internet of Things (IoT) assisted soil fertility mapping to improve the accuracy of the fertilizer recommendation system. For real-time soil fertility mapping, an IoT architecture is also proposed to support context-aware fertilizer recommendations. The proposed solution is practically implemented in real crop fields to assess the accuracies of IoT-assisted fertility mapping. The accuracy of IoT-assisted fertility mapping is assessed by comparing the proposed solution with the standard soil chemical analysis method in terms of observing Nitrogen (N), Phosphorous (P), and Potassium (K). The results reveal that the observations by both methods are in line with a mean difference of 0.34, 0.36, and -0.13 for N, P, and K observations, respectively. The context-aware fertilizer recommendation is implemented with the Logistic Regression (LR), Support Vector Machine (SVM), Gaussian Naïve Bayes (GNB), and K-Nearest Neighbor (KNN) machine learning models to assess the performance of these machine learning models. The evaluation of the proposed solution reveals that the GNB model is more accurate as compared to the machine learning models evaluated, with accuracies of 96% and 94% from training and testing datasets, respectively.INDEX TERMS Internet of Things (IoT), machine learning, soil fertility mapping, fertilizer recommendation, support vector machine (SVM), Gaussian Naïve Bayes (GNB), logistic regression (LR), k-nearest neighbor (KNN).

show abstract

An efficient LoRa-based smart agriculture management and monitoring system using wireless sensor networks

Cited by 97 publications

References 20 publications

Exploration of Agricultural Economic Management Methods under Internet + Mode

Exploration of Agricultural Economic Management Methods under Internet + Mode

Interference-Aware Nodes Deployment of a LoRa-Based Architecture for Smart Agriculture in the Southern Region of Senegal

Internet of Things (IoT) Assisted Context Aware Fertilizer Recommendation

Contact Info

Product

Resources

About