Using deep belief network to construct the agricultural information system based on Internet of Things

Luo, Ji; Zhao, Chuhao; Qiao, Chen; Li, Guangqin

doi:10.1007/s11227-021-03898-y

Cited by 108 publications

(19 citation statements)

References 29 publications

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“…Deep belief network (DBN) is one of unsupervised learning algorithms [12][13][14]. It is composed of RBM, so there is no connection in the same layer.…”

Section: Deep Belief Networkmentioning

confidence: 99%

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[Retracted] Automatic Classification Method of Music Genres Based on Deep Belief Network and Sparse Representation

Pan

2022

Journal of Mathematics

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Aiming at the problems of poor classification effect, low accuracy, and long time in the current automatic classification methods of music genres, an automatic classification method of music genres based on deep belief network and sparse representation is proposed. The music signal is preprocessed by framing, pre-emphasis, and windowing, and the characteristic parameters of the music signal are extracted by Mel frequency cepstrum coefficient analysis. The restricted Boltzmann machine is trained layer by layer to obtain the connection weights between layers of the depth belief network model. According to the output classification, the connection weights in the model are fine-tuned by using the error back-propagation algorithm. Based on the deep belief network model after fine-tuning training, the structure of the music genre classification network model is designed. Combined with the classification algorithm of sparse representation, for the training samples of sparse representation music genre, the sparse solution is obtained by using the minimum norm, the sparse representation of test vector is calculated, the category of training samples is judged, and the automatic classification of music genre is realized. The experimental results show that the music genre automatic classification effect of the proposed method is better, the classification accuracy rate is higher, and the classification time can be effectively shortened.

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“…Deep belief network (DBN) is one of unsupervised learning algorithms [12][13][14]. It is composed of RBM, so there is no connection in the same layer.…”

Section: Deep Belief Networkmentioning

confidence: 99%

“…In formula (14), ‖ • ‖ 0 is described as l 0 norm. However, formula ( 14) has an NP problem, which is difficult to solve.…”

Section: Seeking Sparse Solutionmentioning

confidence: 99%

[Retracted] Automatic Classification Method of Music Genres Based on Deep Belief Network and Sparse Representation

Pan

2022

Journal of Mathematics

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“…Deep learning algorithms have been used in various fields for detection, such as 23 , surgical instruments 24 , outlier detection 25 , constructing agricultural information systems 26 and so on. The most prevalent architecture for plant disease diagnosis using deep learning (DL) algorithms is CNN, which has shown encouraging results.…”

Section: Related Workmentioning

confidence: 99%

A novel smartphone application for early detection of habanero disease

Babatunde,

Ogundokun

et al. 2024

Sci Rep

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Habanero plant diseases can significantly reduce crop yield and quality, making early detection and treatment crucial for farmers. In this study, we discuss the creation of a modified VGG16 (MVGG16) Deep Transfer Learning (DTL) model-based smartphone app for identifying habanero plant diseases. With the help of the smartphone application, growers can quickly diagnose the health of a habanero plant by taking a photo of one of its leaves. We trained the DTL model on a dataset of labelled images of healthy and infected habanero plants and evaluated its performance on a separate test dataset. The MVGG16 DTL algorithm had an accuracy, precision, f1-score, recall and AUC of 98.79%, 97.93%, 98.44%, 98.95 and 98.63%, respectively, on the testing dataset. The MVGG16 DTL model was then integrated into a smartphone app that enables users to upload photographs, get diagnosed, and explore a history of earlier diagnoses. We tested the software on a collection of photos of habanero plant leaves and discovered that it was highly accurate at spotting infected plants. The smartphone software can boost early identification and treatment of habanero plant diseases, resulting in higher crop output and higher-quality harvests.

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“…According to the main body of system management and implementation, the specific submanagement roles of the system mainly include the company's decision-making and management, farm technicians, farm base managers, and agricultural experts five target users to carry out real-time monitoring of growth, standardized management of planting, and remote guidance and control of melon and fruit production bases. Belonging to this system for the melon fruit planting operation process for personalized customization and specification, in the refinement of the operation of the management system, after real-time dissemination of planting-related instructions, the planting standardization plan in the specific implementation details of management optimization, so that each operation is valued and recorded, planting standards are further standardized [14]. The highest accuracy rate is 89.75%.…”

Section: Intelligent Sensor Network For Finementioning

confidence: 99%

Image Detection System Based on Smart Sensor Network and Ecological Economy in the Context of Fine Agriculture

Wang

Teo

et al. 2022

Journal of Sensors

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In this paper, an in-depth study and analysis of the ecological economy of fine agriculture are carried out using image detection methods of smart sensor networks. The analog signal output from the wireless sensor network is filtered and thresholder to convert into a digital signal to complete the sensor monitoring data preprocessing for digital information analysis. In this paper, with the objectives of good environmental adaptability, low power consumption, low cost, and standardization, the key technologies of wireless sensor networks for fine agriculture are studied, including network structure, networking method, node positioning method, data fusion method, rapid energy self-sufficiency, and energy-saving strategy, and the performance evaluation method of wireless sensor network system, IoT-oriented middleware design method, generic node software and hardware design method, and typical application system. Firstly, a convolutional layer is used instead of a fully connected layer, which makes the network more flexible in terms of input image requirements and enables the calculation of the target rice region. Not only will many complex operations be generated, but it will also limit the generalization ability of the model. Then, by introducing a flexible connection layer based on unit and optimizing the loss function of the network, a crop convolutional neural network (Crop-Net) is finally proposed for training and testing rice images at different growth stages to improve the detection accuracy. In this paper, a network quality of service goal-driven evaluation strategy and evaluation method for agricultural wireless sensor network systems is designed to provide a reference for the establishment of industry standards for wireless sensor network systems for fine agriculture.

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Using deep belief network to construct the agricultural information system based on Internet of Things

Cited by 108 publications

References 29 publications

[Retracted] Automatic Classification Method of Music Genres Based on Deep Belief Network and Sparse Representation

[Retracted] Automatic Classification Method of Music Genres Based on Deep Belief Network and Sparse Representation

A novel smartphone application for early detection of habanero disease

Image Detection System Based on Smart Sensor Network and Ecological Economy in the Context of Fine Agriculture

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