SSTNet: Spatial, Spectral, and Texture Aware Attention Network Using Hyperspectral Image for Corn Variety Identification

Zhang, Weidong; Li, Zexu; Sun, Hai-Han; Zhang, Qiang; Zhuang, Pinghui; Li, Chongyi

doi:10.1109/lgrs.2022.3225215

Cited by 29 publications

(28 citation statements)

References 34 publications

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“…We method run on a Windows 10 PC with AMD Ryzen 5 3600X Central Processing Unit (CPU) at 3.80 GHz, The dataset (CSHID) utilized in this article is sourced from SSTNet ( Zhang et al., 2022 ), encompassing ten different corn varieties cultivated in Henan Province: Baiyu 607, Baiyu 808, Baiyu 818, Baiyu 833, Baiyu 879, Baiyu 897, Baiyu 918, Baiyu 8317, Baiyu 9284, and Fengda 601. The data was collected using Surface Optics’ SOC 710 Portable Visible/Near Infrared Imaging Spectrometer.…”

Section: Methodsmentioning

confidence: 99%

“…However, traditional manual classification methods can be inefficient and require substantial human resources. In the agricultural field, hyperspectral imaging technology has found extensive application ( Zhang et al., 2022 ). Hyperspectral images offer multi-band spectral data and capture more comprehensive plant information than traditional RGB images ( Wang et al., 2019 ; Ahmad et al., 2021 ).…”

Section: Introductionmentioning

confidence: 99%

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Efficient residual network using hyperspectral images for corn variety identification

Li,

Zhai,

Zheng

et al. 2024

Front. Plant Sci.

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Corn seeds are an essential element in agricultural production, and accurate identification of their varieties and quality is crucial for planting management, variety improvement, and agricultural product quality control. However, more than traditional manual classification methods are needed to meet the needs of intelligent agriculture. With the rapid development of deep learning methods in the computer field, we propose an efficient residual network named ERNet to identify hyperspectral corn seeds. First, we use linear discriminant analysis to perform dimensionality reduction processing on hyperspectral corn seed images so that the images can be smoothly input into the network. Second, we use effective residual blocks to extract fine-grained features from images. Lastly, we detect and categorize the hyperspectral corn seed images using the classifier softmax. ERNet performs exceptionally well compared to other deep learning techniques and conventional methods. With 98.36% accuracy rate, the result is a valuable reference for classification studies, including hyperspectral corn seed pictures.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Efficient residual network using hyperspectral images for corn variety identification

Li,

Zhai,

Zheng

et al. 2024

Front. Plant Sci.

Self Cite

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“…It emphasizes the model's aptitude for precise classification while minimizing the risk of overlooking relevant data. The four model assessment indicators are calculated as shown in Equations ( 7)- (10).…”

Section: Assessment Of Indicatorsmentioning

confidence: 99%

CNN-Bi-LSTM: A Complex Environment-Oriented Cattle Behavior Classification Network Based on the Fusion of CNN and Bi-LSTM

Gao,

Wang,

Wang

et al. 2023

Sensors

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Cattle behavior classification technology holds a crucial position within the realm of smart cattle farming. Addressing the requisites of cattle behavior classification in the agricultural sector, this paper presents a novel cattle behavior classification network tailored for intricate environments. This network amalgamates the capabilities of CNN and Bi-LSTM. Initially, a data collection method is devised within an authentic farm setting, followed by the delineation of eight fundamental cattle behaviors. The foundational step involves utilizing VGG16 as the cornerstone of the CNN network, thereby extracting spatial feature vectors from each video data sequence. Subsequently, these features are channeled into a Bi-LSTM classification model, adept at unearthing semantic insights from temporal data in both directions. This process ensures precise recognition and categorization of cattle behaviors. To validate the model’s efficacy, ablation experiments, generalization effect assessments, and comparative analyses under consistent experimental conditions are performed. These investigations, involving module replacements within the classification model and comprehensive analysis of ablation experiments, affirm the model’s effectiveness. The self-constructed dataset about cattle is subjected to evaluation using cross-entropy loss, assessing the model’s generalization efficacy across diverse subjects and viewing perspectives. Classification performance accuracy is quantified through the application of a confusion matrix. Furthermore, a set of comparison experiments is conducted, involving three pertinent deep learning models: MASK-RCNN, CNN-LSTM, and EfficientNet-LSTM. The outcomes of these experiments unequivocally substantiate the superiority of the proposed model. Empirical results underscore the CNN-Bi-LSTM model’s commendable performance metrics: achieving 94.3% accuracy, 94.2% precision, and 93.4% recall while navigating challenges such as varying light conditions, occlusions, and environmental influences. The objective of this study is to employ a fusion of CNN and Bi-LSTM to autonomously extract features from multimodal data, thereby addressing the challenge of classifying cattle behaviors within intricate scenes. By surpassing the constraints imposed by conventional methodologies and the analysis of single-sensor data, this approach seeks to enhance the precision and generalizability of cattle behavior classification. The consequential practical, economic, and societal implications for the agricultural sector are of considerable significance.

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“…With the rapid development of deep learning, many research and applications have achieved breakthrough results, such as Oracle Bone Inscription detection [8] [9], corn variety identification [10]. The knowledge base still has its advantages in the era of deep learning.…”

Section: No Mistake Charactersmentioning

confidence: 99%

Typos Correction in Overseas Chinese Learning Based on Chinese Character Semantic Knowledge Graph

Xiong¹,

Zhang²,

Zhang³

et al. 2023

JDAIP

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In recent years, more and more foreigners begin to learn Chinese characters, but they often make typos when using Chinese. The fundamental reason is that they mainly learn Chinese characters from the glyph and pronunciation, but do not master the semantics of Chinese characters. If they can understand the meaning of Chinese characters and form knowledge groups of the characters with relevant meanings, it can effectively improve learning efficiency. We achieve this goal by building a Chinese character semantic knowledge graph (CCSKG). In the process of building the knowledge graph, the semantic computing capacity of HowNet was utilized, and 104,187 associated edges were finally established for 6752 Chinese characters. Thanks to the development of deep learning, OpenHowNet releases the core data of HowNet and provides useful APIs for calculating the similarity between two words based on sememes. Therefore our method combines the advantages of data-driven and knowledge-driven. The proposed method treats Chinese sentences as subgraphs of the CCSKG and uses graph algorithms to correct Chinese typos and achieve good results. The experimental results show that compared with keras-bert and pycorrector + ernie, our method reduces the false acceptance rate by 38.28% and improves the recall rate by 40.91% in the field of learning Chinese as a foreign language. The CCSKG can help to promote Chinese overseas communication and international education.

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SSTNet: Spatial, Spectral, and Texture Aware Attention Network Using Hyperspectral Image for Corn Variety Identification

Cited by 29 publications

References 34 publications

Efficient residual network using hyperspectral images for corn variety identification

Efficient residual network using hyperspectral images for corn variety identification

CNN-Bi-LSTM: A Complex Environment-Oriented Cattle Behavior Classification Network Based on the Fusion of CNN and Bi-LSTM

Typos Correction in Overseas Chinese Learning Based on Chinese Character Semantic Knowledge Graph

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