scholarly journals Automated Pest Detection with DNN on the Edge for Precision Agriculture

2021 ◽  
Author(s):  
Andrea Albanese ◽  
matteo nardello ◽  
Davide Brunelli
Keyword(s):  
Embedded System ◽  
Low Power ◽  
Precision Agriculture ◽  
Research Direction ◽  
Disease Diagnosis ◽  
Yield Reduction ◽  
Battery Life ◽  
Monitoring And Control ◽  
Embedded Sensing ◽  
Pest Infestation

Artificial intelligence has smoothly penetrated several economic activities, especially monitoring and control applications, including the agriculture sector. However, research efforts toward low-power sensing devices with fully functional machine learning (ML) on-board are still fragmented and limited in smart farming. Biotic stress is one of the primary causes of crop yield reduction. With the development of deep learning in computer vision technology, autonomous detection of pest infestation through images has become an important research direction for timely crop disease diagnosis. This paper presents an embedded system enhanced with ML functionalities, ensuring continuous detection of pest infestation inside fruit orchards. The embedded solution is based on a low-power embedded sensing system along with a Neural Accelerator able to capture and process images inside common pheromone-based traps. Three different ML algorithms have been trained and deployed, highlighting the capabilities of the platform. Moreover, the proposed approach guarantees an extended battery life thanks to the integration of energy harvesting functionalities. Results show how it is possible to automate the task of pest infestation for unlimited time without the farmer's intervention.

scholarly journals Automated Pest Detection with DNN on the Edge for Precision Agriculture

2021 ◽  
Author(s):  
Andrea Albanese ◽  
matteo nardello ◽  
Davide Brunelli
Keyword(s):  
Embedded System ◽  
Low Power ◽  
Precision Agriculture ◽  
Research Direction ◽  
Disease Diagnosis ◽  
Yield Reduction ◽  
Battery Life ◽  
Monitoring And Control ◽  
Embedded Sensing ◽  
Pest Infestation

Artificial intelligence has smoothly penetrated several economic activities, especially monitoring and control applications, including the agriculture sector. However, research efforts toward low-power sensing devices with fully functional machine learning (ML) on-board are still fragmented and limited in smart farming. Biotic stress is one of the primary causes of crop yield reduction. With the development of deep learning in computer vision technology, autonomous detection of pest infestation through images has become an important research direction for timely crop disease diagnosis. This paper presents an embedded system enhanced with ML functionalities, ensuring continuous detection of pest infestation inside fruit orchards. The embedded solution is based on a low-power embedded sensing system along with a Neural Accelerator able to capture and process images inside common pheromone-based traps. Three different ML algorithms have been trained and deployed, highlighting the capabilities of the platform. Moreover, the proposed approach guarantees an extended battery life thanks to the integration of energy harvesting functionalities. Results show how it is possible to automate the task of pest infestation for unlimited time without the farmer's intervention.

Designing Future Precision Agriculture: Detection of Seeds Germination Using Artificial Intelligence on a Low-Power Embedded System

2019 ◽  
Vol 19 (23) ◽  
pp. 11573-11582 ◽  
Author(s):  
Dmitrii Shadrin ◽  
Alexander Menshchikov ◽  
Dmitry Ermilov ◽  
Andrey Somov
Keyword(s):  
Artificial Intelligence ◽  
Embedded System ◽  
Low Power ◽  
Precision Agriculture ◽  
Seeds Germination

scholarly journals Rural Healthcare IoT Architecture Based on Low-Energy LoRa

2021 ◽  
Vol 18 (14) ◽  
pp. 7660
Author(s):  
Ace Dimitrievski ◽  
Sonja Filiposka ◽  
Francisco José Melero ◽  
Eftim Zdravevski ◽  
Petre Lameski ◽  
...  
Keyword(s):  
Low Power ◽  
Rural Areas ◽  
Urban Areas ◽  
Well Being ◽  
Battery Life ◽  
Ultra Low Power ◽  
Term Operation ◽  
Doctor Patient Communication ◽  
Connected Health ◽  
Iot Devices

Connected health is expected to introduce an improvement in providing healthcare and doctor-patient communication while at the same time reducing cost. Connected health would introduce an even more significant gap between healthcare quality for urban areas with physical proximity and better communication to providers and the portion of rural areas with numerous connectivity issues. We identify these challenges using user scenarios and propose LoRa based architecture for addressing these challenges. We focus on the energy management of battery-powered, affordable IoT devices for long-term operation, providing important information about the care receivers’ well-being. Using an external ultra-low-power timer, we extended the battery life in the order of tens of times, compared to relying on low power modes of the microcontroller.

scholarly journals Tomato Leaf Disease Diagnosis Based on Improved Convolution Neural Network by Attention Module

Agriculture ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 651
Author(s):  
Shengyi Zhao ◽  
Yun Peng ◽  
Jizhan Liu ◽  
Shuo Wu
Keyword(s):  
Neural Network ◽  
High Performance ◽  
Model Comparison ◽  
Research Direction ◽  
Disease Diagnosis ◽  
Tomato Leaf ◽  
Identification Accuracy ◽  
Main Research ◽  
Proposed Model ◽  
Complex Features

Crop disease diagnosis is of great significance to crop yield and agricultural production. Deep learning methods have become the main research direction to solve the diagnosis of crop diseases. This paper proposed a deep convolutional neural network that integrates an attention mechanism, which can better adapt to the diagnosis of a variety of tomato leaf diseases. The network structure mainly includes residual blocks and attention extraction modules. The model can accurately extract complex features of various diseases. Extensive comparative experiment results show that the proposed model achieves the average identification accuracy of 96.81% on the tomato leaf diseases dataset. It proves that the model has significant advantages in terms of network complexity and real-time performance compared with other models. Moreover, through the model comparison experiment on the grape leaf diseases public dataset, the proposed model also achieves better results, and the average identification accuracy of 99.24%. It is certified that add the attention module can more accurately extract the complex features of a variety of diseases and has fewer parameters. The proposed model provides a high-performance solution for crop diagnosis under the real agricultural environment.

scholarly journals The Demonstration of S2P (Serial-to-Parallel) Converter with Address Allocation Method Using 28 nm CMOS Technology

2021 ◽  
Vol 11 (1) ◽  
pp. 429
Author(s):  
Min-Su Kim ◽  
Youngoo Yang ◽  
Hyungmo Koo ◽  
Hansik Oh
Keyword(s):  
Integrated Circuits ◽  
Embedded System ◽  
Low Power ◽  
High Speed ◽  
Cmos Technology ◽  
Clock Frequency ◽  
Clock Generation ◽  
Allocation Method ◽  
Evaluation Board ◽  
28 Nm

To improve the performance of analog, RF, and digital integrated circuits, the cutting-edge advanced CMOS technology has been widely utilized. We successfully designed and implemented a high-speed and low-power serial-to-parallel (S2P) converter for 5G applications based on the 28 nm CMOS technology. It can update data easily and quickly using the proposed address allocation method. To verify the performances, an embedded system (NI-FPGA) for fast clock generation on the evaluation board level was also used. The proposed S2P converter circuit shows extremely low power consumption of 28.1 uW at 0.91 V with a core die area of 60 × 60 μm2 and operates successfully over a wide clock frequency range from 5 M to 40 MHz.

The Development of Embedded System for Energy Saving in Electro-Hydraulic System

2013 ◽  
Vol 418 ◽  
pp. 63-69
Author(s):  
Sema Patchim ◽  
Watcharin Po-Ngaen
Keyword(s):  
Embedded System ◽  
Low Power ◽  
Hydraulic System ◽  
Fuzzy Controller ◽  
Low Cost ◽  
Operational Performance ◽  
Functional Design ◽  
Power Utility ◽  
Load Sensing ◽  
Oil Flow

In last decade, energy efficiency of hydraulic actuators systems has been especially important in industrial machinery applications [1-. And an advanced electronics world most of the applications are developed by microcontroller based embedded system. Energy processor based variable oil flow of hydraulic controller was presented to improve the efficiency of the motor by maintaining with the load sensing. These PIC processor combined with fuzzy controller were help to design efficient optimal power hydraulic machine controller. A functional design of processor and in this system was completed by using load sensing signal to control oil flow. The advantage of the proposed system was optimized operational performance and low power utility. Without having the architectural concept of any motor we can control it by using this method. This is a low cost low power controller and easy to use. The experiment results verified its validity.

scholarly journals Adaptive Data Synchronization Algorithm for IoT-Oriented Low-Power Wide-Area Networks

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 4053 ◽  
Author(s):  
Andrea Petroni ◽  
Francesca Cuomo ◽  
Leonisio Schepis ◽  
Mauro Biagi ◽  
Marco Listanti ◽  
...  
Keyword(s):  
Low Power ◽  
Mobile Communications ◽  
Data Exchange ◽  
Energy Savings ◽  
Wide Area ◽  
Wide Area Networks ◽  
Battery Life ◽  
File Synchronization ◽  
The Internet Of Things

The Internet of Things (IoT) is by now very close to be realized, leading the world towards a new technological era where people’s lives and habits will be definitively revolutionized. Furthermore, the incoming 5G technology promises significant enhancements concerning the Quality of Service (QoS) in mobile communications. Having billions of devices simultaneously connected has opened new challenges about network management and data exchange rules that need to be tailored to the characteristics of the considered scenario. A large part of the IoT market is pointing to Low-Power Wide-Area Networks (LPWANs) representing the infrastructure for several applications having energy saving as a mandatory goal besides other aspects of QoS. In this context, we propose a low-power IoT-oriented file synchronization protocol that, by dynamically optimizing the amount of data to be transferred, limits the device level of interaction within the network, therefore extending the battery life. This protocol can be adopted with different Layer 2 technologies and provides energy savings at the IoT device level that can be exploited by different applications.

Low power aware pulse triggered flip flops using modified clock gating approaches

2018 ◽  
Vol 15 (6) ◽  
pp. 792-803
Author(s):  
Sudhakar Jyothula
Keyword(s):  
Power Consumption ◽  
Low Power ◽  
Average Power ◽  
Essential Element ◽  
Battery Life ◽  
Portable Devices ◽  
Clock Gating ◽  
Flip Flop ◽  
Content Type ◽  
Low Leakage

PurposeThe purpose of this paper is to design a low power clock gating technique using Galeor approach by assimilated with replica path pulse triggered flip flop (RP-PTFF).Design/methodology/approachIn the present scenario, the inclination of battery for portable devices has been increasing tremendously. Therefore, battery life has become an essential element for portable devices. To increase the battery life of portable devices such as communication devices, these have to be made with low power requirements. Hence, power consumption is one of the main issues in CMOS design. To reap a low-power battery with optimum delay constraints, a new methodology is proposed by using the advantages of a low leakage GALEOR approach. By integrating the proposed GALEOR technique with conventional PTFFs, a reduction in power consumption is achieved.FindingsThe design was implemented in mentor graphics EDA tools with 130 nm technology, and the proposed technique is compared with existing conventional PTFFs in terms of power consumption. The average power consumed by the proposed technique (RP-PTFF clock gating with the GALEOR technique) is reduced to 47 per cent compared to conventional PTFF for 100 per cent switching activity.Originality/valueThe study demonstrates that RP-PTFF with clock gating using the GALEOR approach is a design that is superior to the conventional PTFFs.

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