An efficient framework for data aggregation in smart agriculture

Yuan, Jiangjun; Liu, Weinan; Wang, Jie; Shi, Jiawen; Miao, Ling

doi:10.1002/cpe.6160

Cited by 12 publications

(7 citation statements)

References 34 publications

(25 reference statements)

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“…Artificial intelligence industry refers to an industry that provides intelligent products and technical services to the society based on artificial intelligence technology. It has derived various new intelligent applications through enabling manufacturing, agriculture, medical, and other industries, such as subtopic detection [1], intelligent agriculture [2], and smart grid [3]. These applications have become a new engine to promote high-quality economic and social development.…”

Section: Introductionmentioning

confidence: 99%

A Lightweight Face Verification Based on Adaptive Cascade Network and Triplet Loss Function

Lin

Zhao-yang²,

Liu

et al. 2022

Wireless Communications and Mobile Computing

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In the past few years, with the continuous breakthrough of technology in various fields, artificial intelligence has been considered as a revolutionary technology. One of the most important and useful applications of artificial intelligence is face detection. The outbreak of COVID-19 has promoted the development of the noncontact identity authentication system. Face detection is also one of the key techniques in this kind of authentication system. However, the current real-time face detection is computationally expensive which hinders the application of face recognition. To address this issue, we propose a face verification framework based on adaptive cascade network and triplet loss. The framework is simple in network architecture and has light-weighted parameters. The training network is made of three stages with an adaptive cascade network and utilizes a novel image pyramid based on scales with different sizes. We train the face verification model and complete the verification within 0.15 second for processing one image which shows the computation efficiency of our proposed framework. In addition, the experimental results also show the competitive accuracy of our proposed framework which is around 98.6%. Using dynamic semihard triplet strategy for training, our network achieves a classification accuracy of 99.2% on the dataset of Labeled Faces in the Wild.

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Section: Introductionmentioning

confidence: 99%

A Lightweight Face Verification Based on Adaptive Cascade Network and Triplet Loss Function

Lin

Zhao-yang²,

Liu

et al. 2022

Wireless Communications and Mobile Computing

Self Cite

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“…The proposed system is compared with three existing crop field monitoring systems such as crop breeding and monitoring system (CBMS) (Shakoor et al, 2017), ML-based enhanced agriculture system (MEAS) (Balducci et al, 2018), and WSN based intelligent farm precision system (WIFPS) (Mekonnen et al, 2019). All of the existing techniques are using crop field disease monitoring tasks and crop yield precision tasks (Ahmad & Nabi, 2021;Maddikunta et al, 2021;Yuan et al, 2021). However, these techniques are using generic ML techniques to classify the crop field data.…”

Section: Implementation Details and Resultsmentioning

confidence: 99%

Internet of things‐based deeply proficient monitoring and protection system for crop field

et al. 2021

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The production rate of crops is significantly declining due to natural disasters, animal interventions and plant diseases. Internet of things (IoT) and wireless sensor networks are widely applied in crop field monitoring systems to observe the quality of each plant and the field. This work proposes IoT based crop field protection system (ICFPS) that monitors and protects the crop fields from animal intrusions. This proposed system uses ultrasonic sensors, hyperspectral cameras, voice recorded buzzers and other agriculture sensors to protect the entire crop field. This system uses numerous sensor nodes and cameras for gathering field objects (images and environmental objects). The proposed ICFPS creates deep learning techniques such as recurrent convolutional neural networks (RCNN) and recurrent generative adversarial neural networks (RGAN) for feature extraction, disease detection and field data monitoring practices. This proposed work develops a smart city-based agriculture system using cognitive learning approaches. This proposed system analyses crop field data and provide automatic alerts regarding animal interferences and crop diseases. Moreover, the cognitive smart crop field system observes various field conditions which support for good production rate. In this system, sensors and camera-enabled agriculture drones are coordinated with each other to collect the field data regularly. At the same time, the proposed work trains the RCNN and RGAN units using effective crop field datasets to attain realistic decisions within minimal time intervals. The experiment details and results show the proposed ICFPS works with 8%-10% of more classification accuracy than existing systems.cognitive smart systems, crop field, deep learning and data analysis, internet of things, smart cities, wireless sensor networks | INTRODUCTIONCrop field monitoring systems and smart agriculture systems are the emerging technologies for developing automatic field control frameworks.Researchers develop smart agriculture systems for monitoring the crop field environment (soil, weather, water level, irrigation and disease) using various sensors. Recently, wireless sensor networks (WSN) frameworks and internet of things (IoT) platforms control the operations of smart field management models. On the other view, research works contributed to use the field sensor data and learning models for increasing the accuracy of decision-making systems.

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“…In the context of achieving a controlled or regulated environment, aggregate micro-climate (relative humidity and temperature) are required as inputs to dedicated control systems for appropriate control actions within the cultivation systems (Yeon Lee et al, 2019). Data aggregation is the process of fusing information from different or multiple sensors together in order to derive a single reference measurement that is sent to a base station or controller depending on the intended application (Alkahtani and Karim, 2018;Kaur and Munjal, 2020;Yuan et al, 2021). Generally, in controlled cultivation systems the aim is to ensure that the micro-climate are controlled to support plant life and growth.…”

Section: Data Aggregationmentioning

confidence: 99%

A genetic programming-based optimal sensor placement for greenhouse monitoring and control

Ajani,

Aboyeji,

Mallipeddi

et al. 2023

Front. Plant Sci.

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Optimal sensor location methods are crucial to realize a sensor profile that achieves pre-defined performance criteria as well as minimum cost. In recent times, indoor cultivation systems have leveraged on optimal sensor location schemes for effective monitoring at minimum cost. Although the goal of monitoring in indoor cultivation system is to facilitate efficient control, most of the previously proposed methods are ill-posed as they do not approach optimal sensor location from a control perspective. Therefore in this work, a genetic programming-based optimal sensor placement for greenhouse monitoring and control is presented from a control perspective. Starting with a reference micro-climate condition (temperature and relative humidity) obtained by aggregating measurements from 56 dual sensors distributed within a greenhouse, we show that genetic programming can be used to select a minimum number of sensor locations as well as a symbolic representation of how to aggregate them to efficiently estimate the reference measurements from the 56 sensors. The results presented in terms of Pearson’s correlation coefficient (r) and three error-related metrics demonstrate that the proposed model achieves an average r of 0.999 for both temperature and humidity and an average RMSE value of 0.0822 and 0.2534 for temperate and relative humidity respectively. Conclusively, the resulting models make use of only eight (8) sensors, indicating that only eight (8) are required to facilitate the efficient monitoring and control of the greenhouse facility.

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An efficient framework for data aggregation in smart agriculture

Cited by 12 publications

References 34 publications

A Lightweight Face Verification Based on Adaptive Cascade Network and Triplet Loss Function

A Lightweight Face Verification Based on Adaptive Cascade Network and Triplet Loss Function

Internet of things‐based deeply proficient monitoring and protection system for crop field

A genetic programming-based optimal sensor placement for greenhouse monitoring and control

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