Rice Disease Identification Method Based on Attention Mechanism and Deep Dense Network

Jiang, Minlan; Feng, Changguang; Fang, Xiaosheng; Huang, Qi; Zhang, Changjiang; Shi, Xiaowei

doi:10.3390/electronics12030508

Cited by 11 publications

(7 citation statements)

References 35 publications

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“…The attention mechanism has emerged as a pivotal technique in fine-grained image recognition, enhancing the nuances and subtleties of images ( Lee et al, 2022 ; Hu et al., 2023 ; Jiang et al., 2023 ). The attention mechanism rapidly scans the global context, pinpoints the pertinent target regions, and dampens unrelated information.…”

Section: Methodsmentioning

confidence: 99%

TRiP: a transfer learning based rice disease phenotype recognition platform using SENet and microservices

Yuan,

Xia,

Tian

et al. 2024

Front. Plant Sci.

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Classification of rice disease is one significant research topics in rice phenotyping. Recognition of rice diseases such as Bacterialblight, Blast, Brownspot, Leaf smut, and Tungro are a critical research field in rice phenotyping. However, accurately identifying these diseases is a challenging issue due to their high phenotypic similarity. To address this challenge, we propose a rice disease phenotype identification framework which utilizing the transfer learning and SENet with attention mechanism on the cloud platform. The pre-trained parameters are transferred to the SENet network for parameters optimization. To capture distinctive features of rice diseases, the attention mechanism is applied for feature extracting. Experiment test and comparative analysis are conducted on the real rice disease datasets. The experimental results show that the accuracy of our method reaches 0.9573. Furthermore, we implemented a rice disease phenotype recognition platform based microservices architecture and deployed it on the cloud, which can provide rice disease phenotype recognition task as a service for easy usage.

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Section: Methodsmentioning

confidence: 99%

TRiP: a transfer learning based rice disease phenotype recognition platform using SENet and microservices

Yuan,

Xia,

Tian

et al. 2024

Front. Plant Sci.

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“…Attention model has been widely used in various fields of deep learning in recent years. Whether in the field of image processing, speech recognition, or natural language processing tasks, we can see the practical application of attention mechanism [9] and get good results. In current object detection technologies, there are already many visual attention models used to focus attention on a certain area in the image, using different weight parameters to adjust the importance of attention information.…”

Section: B a Cbam Attention Modulementioning

confidence: 99%

Image Tampering Recognition Algorithm Based on Improved YOLOv5s

Liu

2023

IEEE Access

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In order to improve the extraction effect of the features of the tampered region, the model usually relies on a specific feature defined by hand to optimize the model. However, the effect of the model is not ideal when the tampered mode is unknown. In this paper, the Neck layer of the YOLOv5s model is reasonably embedded into the CBAM attention module, so that the model can more accurately capture the features of the tampered region. In addition, the boundary frame loss is optimized by using EIOU loss function, which separately dissolves the impact factors of aspect ratio to calculate the model. The experimental results show that the image tampering recognition algorithm proposed in this paper based on the improved YOLOv5s can not only effectively identify a variety of tampering modes, but also increase the average accuracy of recognition by 1.57% compared with the benchmark algorithm. The recognition speed is higher than that of LSTM and U-Net algorithms, and the recognition speed also reaches 13.89 images per second, maintaining a high level.

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“…It was also the lightest and fastest option for diagnosing rice illnesses. Atalla et al [19] use the realistic WSN (Wireless Sensor Network) simulator COOJA (Contiki OS Java) to lowpower and lossy networks (RPL) in the two agricultural scenarios. The mobility of nodes is the primary distinguishing factor between the simulation settings for stationary and mobile nodes.…”

Section: Related Workmentioning

confidence: 99%

Whale Optimization based Deep Residual Learning Network for Early Rice Disease Prediction in IoT

Lakshmi,

Kashyap,

Fazal Khan

et al. 2023

ICST Transactions on Scalable Information Systems

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Disease detection on a farm requires laborious and time-consuming observation of individual plants, which is made more difficult when the farm is large and many different plants are farmed. To address these problems, cutting-edge technologies, AI, and Deep Learning (DL) are employed to provide more accurate illness predictions. When it comes to smart farming and precision agriculture, IoT opens up exciting new possibilities. To a certain extent, the goal-mouth of "smart farming" is to upsurge productivity and efficiency in agricultural processes. Smart farming is an approach to agriculture in which Internet of Things devices are interconnected and new technologies are used to optimize existing methods. Utilizing Internet of Things (IoT) devices, smart farming aids in more informed decision making. In many parts of the world, rice is the staple diet. This means that early detection of rice plant diseases using automated techniques and IoT devices is essential. Growing rice yields and profits may be helped along by DL model creation and deployment in agriculture. Here we introduce DRL, a deep residual learning framework that has been trained using photos of rice leaves to recognize one of four classes. The suggested model is called WO-DRL, and the hyper-parameter tuning procedure of DRL is executed with the help of the Whale Optimization algorithm. The outcomes demonstrate the efficacy of our suggested approach in directing the WO-DRL model to learn important characteristics. The findings of this study will pave the way for the agriculture sector to more quickly diagnose and treat plant diseases using AI.

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Rice Disease Identification Method Based on Attention Mechanism and Deep Dense Network

Cited by 11 publications

References 35 publications

TRiP: a transfer learning based rice disease phenotype recognition platform using SENet and microservices

TRiP: a transfer learning based rice disease phenotype recognition platform using SENet and microservices

Image Tampering Recognition Algorithm Based on Improved YOLOv5s

Whale Optimization based Deep Residual Learning Network for Early Rice Disease Prediction in IoT

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