TinyML Smart Sensor for Energy Saving in Internet of Things Precision Agriculture platform

Nicolas, Chollet; Bouchemal, Naila; Amar, Ramdane-Cherif

doi:10.1109/icufn55119.2022.9829675

Cited by 23 publications

(17 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The outcomes demonstrate precision, accuracy, data-driven, and intelligent agriculture. Other studies, such as research, have also demonstrated the same beneficial impacts, [63] show energy saving, [64] reduce soil matching errors to make the process more effective. Then, [30] ideas for sustainable and cost-effective agriculture.…”

Section: F the Implementation Of The Monitoring Tool Systemmentioning

confidence: 75%

The Monitoring System of Soil PH Factor Using IoT-Webserver-Android and Machine Learning: A Case Study

Sumarsono,

Farida Afiatna,

Muflihah

2024

Int. J. Adv. Sci. Eng. Inf. Technol.

View full text Add to dashboard Cite

In Indonesia, the agriculture industry has been more reluctant than other sectors to adopt IoT, IT, and AI technology. Utilizing this technology will enable precision agriculture. This research aims to make and implement an IoT-Webserver-Android and Machine Learning-based soil PH factor monitoring tool system. The steps for making the tool system are divided into three subsystems. The first is a multiple sensors data acquisition subsystem, consisting of sensors for soil PH-Moisture, Temperature-Humidity, and Sunlight. The sensors are connected to the Arduino Uno microcontroller for serial communication with the ESP 8266 microcontroller for the Wi-Fi module. The second part is the monitoring subsystem with the local web application, which contains a MySQL database and a local web page. The third part is the monitoring subsystem with the Android application, which includes a real-time Firebase database and the application for real-time and mobile data display. The results have been implemented and display the expected outcomes. It is clear from the performance of the three subsystems. The outcomes of the tool system's data evaluation provide precise statistical values. Then, Machine Learning analysis generates accurate soil PH prediction models. It has been demonstrated that the monitoring system is applicable and has a favorable impact on data soil PH factor. The implication for the future is that this monitoring system should be added with Nitrogen-Phosphorus-Potassium sensors to measure soil nutrients. Also, the system added edge-analysis to be integrated in monitoring and analyzing soil nutrients.

show abstract

Section: F the Implementation Of The Monitoring Tool Systemmentioning

confidence: 75%

The Monitoring System of Soil PH Factor Using IoT-Webserver-Android and Machine Learning: A Case Study

Sumarsono,

Farida Afiatna,

Muflihah

2024

Int. J. Adv. Sci. Eng. Inf. Technol.

View full text Add to dashboard Cite

show abstract

“…For instance, Ref. [179] employed the Arduino Portenta H7 board in their investigation of a smart sensor for energy-saving in IoT PA. Similarly, studies by [133,[180][181][182][183][184][185] utilized Raspberry Pi and [95,107,136,160,186,187] employed Arduino.…”

Section: Computation Componentsmentioning

confidence: 99%

“…The Kalman filter algorithm [94] has a strong ability to handle the noisy environment with uncertainty and enable the monitoring of nodes with distinct physical characteristics. TinyML [179] deployed a machine learning model capable of detecting fruit presence with capabilities as an energy-efficient model.…”

Section: Deep Learningmentioning

confidence: 99%

See 1 more Smart Citation

IoT Solutions with Artificial Intelligence Technologies for Precision Agriculture: Definitions, Applications, Challenges, and Opportunities

Senoo,

Anggraini,

Kumi

et al. 2024

Electronics

View full text Add to dashboard Cite

The global agricultural sector confronts significant obstacles such as population growth, climate change, and natural disasters, which negatively impact food production and pose a threat to food security. In response to these challenges, the integration of IoT and AI technologies emerges as a promising solution, facilitating data-driven decision-making, optimizing resource allocation, and enhancing monitoring and control systems in agricultural operations to address these challenges and promote sustainable farming practices. This study examines the intersection of IoT and AI in precision agriculture (PA), aiming to provide a comprehensive understanding of their combined impact and mutually reinforcing relationship. Employing a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, we explore the synergies and transformative potential of integrating IoT and AI in agricultural systems. The review also aims to identify present trends, challenges, and opportunities in utilizing IoT and AI in agricultural systems. Diverse forms of agricultural practices are scrutinized to discern the applications of IoT and AI systems. Through a critical analysis of existing literature, this study contributes to a deeper understanding of how the integration of IoT and AI technologies can revolutionize PA, resulting in improved efficiency, sustainability, and productivity in the agricultural sector.

show abstract

“…To really gauge results, the choice tree strategy, a strong AI method, is utilized to information gathered from the field. As per [4] exhibit the capability of such advances in the farming area, an energy-productive model fit for natural product ID was proposed utilizing TinyML and LoRaWAN. The consequences of our model's correlation with a cloud-based model for a similar application demonstrated that it was multiple times more energy-proficient and had a high precision level, preparing for another arrangement of PC vision applications in brilliant cultivating that depend on battery-controlled sensors.…”

Section: IImentioning

confidence: 99%

Soil Quality Monitoring and Crop Prediction Using IOT and Machine Learning

2023

IJRASET

View full text Add to dashboard Cite

People are pressured for time in today's fast-paced environment, and technology assists them in a variety of ways. Agriculture is the primary industry in India. As a result, in the current environment, increasing output or production is critical. When it comes to determining certain chemical features of soil that have an immediate impact on plant growth, traditional farming faces various obstacles. Another critical factor to consider is the effect of timely water supply on plant output. The soil alkalinity and electrical conductivity are measured with PH electrodes. This study also looked into the real-time detection of soil variables and the location of chemical properties like pH, humidity, temperature, and soil moisture using a Raspberry Pi as a controller. Semi-supervised learning will be carried out in a methodical manner using the Q-learning algorithm. We used a range of samples in the experiment to show how to apply the indicated performance.

show abstract

TinyML Smart Sensor for Energy Saving in Internet of Things Precision Agriculture platform

Cited by 23 publications

References 10 publications

The Monitoring System of Soil PH Factor Using IoT-Webserver-Android and Machine Learning: A Case Study

The Monitoring System of Soil PH Factor Using IoT-Webserver-Android and Machine Learning: A Case Study

IoT Solutions with Artificial Intelligence Technologies for Precision Agriculture: Definitions, Applications, Challenges, and Opportunities

Soil Quality Monitoring and Crop Prediction Using IOT and Machine Learning

Contact Info

Product

Resources

About