A Spatial Feature-Enhanced Attention Neural Network with High-Order Pooling Representation for Application in Pest and Disease Recognition

Agriculture, Journal Year: 2022, Volume and Issue: 12(4), P. 500 - 500

Published: March 31, 2022

With the development of advanced information and intelligence technologies, precision agriculture has become an effective solution to monitor prevent crop pests diseases. However, pest disease recognition in applications is essentially fine-grained image classification task, which aims learn discriminative features that can identify subtle differences among similar visual samples. It still challenging solve for existing standard models troubled by oversized parameters low accuracy performance. Therefore, this paper, we propose a feature-enhanced attention neural network (Fe-Net) handle diseases innovative agronomy practices. This model established based on improved CSP-stage backbone network, offers massive channel-shuffled various dimensions sizes. Then, spatial module added exploit interrelationship between different semantic regions. Finally, proposed Fe-Net employs higher-order pooling mine more highly representative computing square root covariance matrix elements. The whole architecture efficiently trained end-to-end way without additional manipulation. comparative experiments CropDP-181 Dataset, achieves Top-1 Accuracy up 85.29% with average time only 71 ms, outperforming other methods. More experimental evidence demonstrates our approach obtains balance model’s performance parameters, suitable its practical deployment art applications.

Language: Английский

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A Reversible Automatic Selection Normalization (RASN) Deep Network for Predicting in the Smart Agriculture System

Agronomy, Journal Year: 2022, Volume and Issue: 12(3), P. 591 - 591

Published: Feb. 27, 2022

Due to the nonlinear modeling capabilities, deep learning prediction networks have become widely used for smart agriculture. Because sensing data has noise and complex nonlinearity, it is still an open topic improve its performance. This paper proposes a Reversible Automatic Selection Normalization (RASN) network, integrating normalization renormalization layer evaluate select module of model. The accuracy been improved effectively by scaling translating input with learnable parameters. application results show that model good ability adaptability greenhouse in Smart Agriculture System.

Language: Английский

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Deep Spatio-Temporal Graph Network with Self-Optimization for Air Quality Prediction

Entropy, Journal Year: 2023, Volume and Issue: 25(2), P. 247 - 247

Published: Jan. 30, 2023

The environment and development are major issues of general concern. After much suffering from the harm environmental pollution, human beings began to pay attention protection started carry out pollutant prediction research. A large number air predictions have tried predict pollutants by revealing their evolution patterns, emphasizing fitting analysis time series but ignoring spatial transmission effect adjacent areas, leading low accuracy. To solve this problem, we propose a network with self-optimization ability spatio-temporal graph neural (BGGRU) mine changing pattern propagation effect. proposed includes temporal modules. module uses sampling aggregation (GraphSAGE) in order extract information data. Bayesian gated recurrent unit (BGraphGRU), which applies (GRU) so as fit data's information. In addition, study used optimization problem model's inaccuracy caused inappropriate hyperparameters model. high accuracy method was verified actual PM2.5 data Beijing, China, provided an effective for predicting concentration.

Language: Английский

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A review on big data based on deep neural network approaches

Artificial Intelligence Review, Journal Year: 2023, Volume and Issue: 56(12), P. 14765 - 14801

Published: June 7, 2023

Language: Английский

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Variational Bayesian Network with Information Interpretability Filtering for Air Quality Forecasting

Mathematics, Journal Year: 2023, Volume and Issue: 11(4), P. 837 - 837

Published: Feb. 7, 2023

Air quality plays a vital role in people’s health, and air forecasting can assist decision making for government planning sustainable development. In contrast, it is challenging to multi-step forecast accurately due its complex nonlinear caused by both temporal spatial dimensions. Deep models, with their ability model strong nonlinearities, have become the primary methods forecasting. However, because of lack mechanism-based analysis, uninterpretability makes decisions risky, especially when decisions. This paper proposes an interpretable variational Bayesian deep learning information self-screening PM2.5 Firstly, based on factors related concentration, e.g., temperature, humidity, wind speed, distribution, etc., multivariate data screening structure was established catch as much helpful possible. Secondly, layer implanted network optimize selection input variables. Further, following implantation layer, gated recurrent unit (GRU) constructed overcome distribution achieve accurate The high accuracy proposed method verified Beijing, China, which provides effective way, multiple determined using technology.

Language: Английский

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Ada-STGMAT: An adaptive spatio-temporal graph multi-attention network for intelligent time series forecasting in smart cities

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: 269, P. 126428 - 126428

Published: Jan. 10, 2025

Language: Английский

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Overall recursive least squares and overall stochastic gradient algorithms and their convergence for feedback nonlinear controlled autoregressive systems

International Journal of Robust and Nonlinear Control, Journal Year: 2022, Volume and Issue: 32(9), P. 5534 - 5554

Published: April 4, 2022

Abstract This article deals with the problems of parameter estimation for feedback nonlinear controlled autoregressive systems (i.e., equation‐error systems). The bilinear‐in‐parameter identification model is formulated to describe system. An overall recursive least squares algorithm developed handle difficulty bilinear‐in‐parameter. For purpose avoiding heavy computational burden, an stochastic gradient deduced and forgetting factor introduced improve convergence rate. Furthermore, analysis proposed algorithms are established by means process theory. effectiveness illustrated simulation example.

Language: Английский

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A novel multi-innovation gradient support vector machine regression method

ISA Transactions, Journal Year: 2022, Volume and Issue: 130, P. 343 - 359

Published: March 17, 2022

Language: Английский

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An Effective Pyramid Neural Network Based on Graph-Related Attentions Structure for Fine-Grained Disease and Pest Identification in Intelligent Agriculture

Agriculture, Journal Year: 2023, Volume and Issue: 13(3), P. 567 - 567

Published: Feb. 26, 2023

In modern agriculture and environmental protection, effective identification of crop diseases pests is very important for intelligent management systems mobile computing application. However, the existing mainly relies on machine learning deep networks to carry out coarse-grained classification large-scale parameters complex structure fitting, which lacks ability in identifying fine-grained features inherent correlation mine pests. To solve problems, a pest method based graph pyramid attention, convolutional neural network (GPA-Net) proposed promote agricultural production efficiency. Firstly, CSP backbone constructed obtain rich feature maps. Then, cross-stage trilinear attention module extract abundant discrimination portions objects as much possible. Moreover, multilevel designed learn multiscale spatial graphic relations enhance recognize diseases. Finally, comparative experiments executed cassava leaf, AI Challenger, IP102 datasets demonstrates that GPA-Net achieves better performance than models, with accuracy up 99.0%, 97.0%, 56.9%, respectively, more conducive distinguish applications practical smart protection.

Language: Английский

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A Variational Bayesian Inference-Based En-Decoder Framework for Traffic Flow Prediction

IEEE Transactions on Intelligent Transportation Systems, Journal Year: 2023, Volume and Issue: 25(3), P. 2966 - 2975

Published: May 24, 2023

Accurate traffic flow prediction, a hotspot for intelligent transportation research, is the prerequisite prediction making travel plans. The speed of can be affected by roads condition, weather, holidays, etc. Moreover, sensors to catch information about will interfered with environmental factors such as illumination, collection time, occlusion, Therefore, in practical system complicated, uncertain, and challenging predict accurately. Motivated from aforementioned issues challenges, this paper, we propose deep encoder-decoder framework based on variational Bayesian inference. A neural network designed combining inference Gated Recurrent Units (GRU) which used unit mine intrinsic dynamics flow. Then, introduced into multi-head attention mechanism avoid noise-induced deterioration accuracy. proposed model achieves superior performance Guangzhou urban dataset over benchmarks, particularly when long-term prediction.

Language: Английский

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