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Electronics
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Artificial Intelligence and Sensors with Agricultural Applications
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Artificial Intelligence and Sensors with Agricultural Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (15 August 2023) | Viewed by 9272

Special Issue Editors

Dr. Yuan Rao

E-Mail Website
Guest Editor
Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, College of Information and Computer Science, Anhui Agricultural University, Hefei 230036, China
Interests: electronic sensor and system; deep learning; agricultural internet of things; machine-learning-based photonics system; LIDAR; NIR spectroscopy; fluorescent imaging system; crop phenotype monitoring; agricultural digital twins
Dr. Lei Chen

E-Mail Website
Guest Editor
Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
Interests: machine learning; pattern recognition; cross media computing; biometrics; smart agriculture; crop phenotype monitoring
Dr. Xiu Jin

E-Mail Website
Guest Editor
Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, College of Information and Computer Science, Anhui Agricultural University, Hefei 230036, China
Interests: NIR spectroscopy; deep learning; agricultural internet of things; machine-learning-based photonics system; LIDAR; fluorescent imaging system; crop phenotype monitoring; agricultural digital twins
Dr. Xiaoping Huang

E-Mail Website
Guest Editor
National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, School of Internet, Anhui University, Hefei 230039, China
Interests: agricultural internet of things; animal husbandry informatization; deep learning; computer vision; digital signal processing; wearable sensors and system; Kalman filter; particle filter

Special Issue Information

Dear Colleagues,

Recent technological advances in Micro Electro Mechanical System (MEMS) enable the agricultural Internet of Things (IoT) to offer a large quantity of data through the use of large-scale sensors. Consequently, modern, intelligent agricultural development increasingly requires the combination of intelligent algorithms and sensors to tackle the collection, management, analysis and application of data, especially in agricultural crop phenotypes, food safety and animal identification. In order to achieve such a goal, the combination of intelligent algorithms and sensors is further discussed, which can better assist the intelligent development of agriculture. In terms of crop phenotype, the changes of leaf size and physicochemical information can be detected by sensors in real-time and accurately, so as to precisely analyze crop yield and quality. In terms of food safety, nondestructive electronics and optical inspection methods can assist food safety, especially when combined with intelligent algorithms, which can conduct further research on food quality and other aspects. In the recognition of animals and plants, deep learning technology has been widely used, but it is still difficult to use sensors for 3D reconstruction, especially in real-time. This topic hopes to combine artificial intelligence with sensors, exploring its agricultural application in respect of both theoretical and practical areas.

Scope：

  • Agricultural Internet of Things;
  • Electronics and smart sensors;
  • Spectroscopy and its application;
  • Remote sensing technologies;
  • Hyperspectral imaging system;
  • Multispectral imaging system;
  • Three-dimensional reconstruction;
  • Machine learning and deep learning;
  • Expert system with agricultural application.

Dr. Yuan Rao
Dr. Lei Chen
Dr. Xiu Jin
Dr. Xiaoping Huang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Internet of Things
  • sensors
  • instrumentation and measurements
  • hyperspectral image
  • multispectral image
  • near-infrared spectroscopy
  • Raman spectroscopy
  • 3D
  • agriculture
  • machine learning
  • deep learning

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Published Papers (4 papers)

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Research

17 pages, 2791 KiB  
Article
S2Former: Parallel Spectral–Spatial Transformer for Hyperspectral Image Classification
by Dong Yuan, Dabing Yu, Yixi Qian, Yongbing Xu and Yan Liu
Electronics 2023, 12(18), 3937; https://doi.org/10.3390/electronics12183937 - 18 Sep 2023
Cited by 5 | Viewed by 1683
Abstract
Due to their excellent representation talent in local features, the convolutional neural network (CNN) has achieved favourable performance in hyperspectral image (HSI) classification tasks. Nevertheless, current CNN models exhibit a marked flaw: they are hard to model the dependencies in long-range distanced positions. [...] Read more.
Due to their excellent representation talent in local features, the convolutional neural network (CNN) has achieved favourable performance in hyperspectral image (HSI) classification tasks. Nevertheless, current CNN models exhibit a marked flaw: they are hard to model the dependencies in long-range distanced positions. This flaw becomes more problematic for the HSI classification task, which targets extracting more discriminative features in local and global dimensions from limited samples. In this paper, we introduce a spatial–spectral transformer (S2Former), which explores spatial and spectral feature extraction in a dual-stream framework for HSI Classification. S2Former, which consists of a spatial transformer and a spectral transformer in parallel branches, extracts the discriminative feature in spatial and spectral dimensions. More specifically, we propose multi-head spatial self-attention to capture the long-range spatial dependency of non-adjacent HSI pixels in a spatial transformer. In the spectral transformer, we propose multi-head covariance spectral attention to mine and represent spectral signatures by computing covariance-based channel maps. Meanwhile, the local activation feed-forward network is developed to complement local details. Extensive experiments conducted on four publicly available datasets indicate that our S2Former achieves state-of-the-art performance for the HSI classification task. Full article
(This article belongs to the Special Issue Artificial Intelligence and Sensors with Agricultural Applications)
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21 pages, 4068 KiB  
Article
Flood Disaster Assessment Method Based on a Stacked Denoising Autoencoder
by Yanping Chen, Yilun Wang, Zhize Wu, Le Zou and Wenbo Li
Electronics 2023, 12(18), 3839; https://doi.org/10.3390/electronics12183839 - 11 Sep 2023
Cited by 1 | Viewed by 1146
Abstract
In recent years, extreme weather has occurred frequently, and the risk of heavy rainfall and flooding faced by the people has risen. It is therefore an urgent requirement to carry out applied research on heavy rainfall and flooding risk assessment. We took Henan [...] Read more.
In recent years, extreme weather has occurred frequently, and the risk of heavy rainfall and flooding faced by the people has risen. It is therefore an urgent requirement to carry out applied research on heavy rainfall and flooding risk assessment. We took Henan Province, where a major flood disaster occurred in 2021, as an example to analyze the impact factors of urban flooding and conduct a risk assessment. Indicators were first selected from population, housing, and the economy, and correlation analysis was used to optimize the indicator system. Then, a deep clustering network model based on a stacked denoising autoencoder (SDAE) was constructed, the feature information implied in the disaster indicators was abstracted into potential features through the coding and decoding of the network, and a small number of potential features were used to express the complex relationship between the disaster indicators. The results of the study show that the high-risk areas of flood damage in Henan Province in 2021 account for 2.3%, the medium-risk areas account for 9.4%, and the low-risk areas account for 80.3%. These evaluation results are in line with the actual situation in Henan Province, and the division of the grade in some areas is more reasonable compared with the entropy weighting method, which is a commonly used method of disaster assessment. The new model does not need to calculate weights to cope with changes in indicators and disaster conditions. The research results can provide scientific reference for urban flood risk management, disaster prevention and mitigation, and regional planning. Full article
(This article belongs to the Special Issue Artificial Intelligence and Sensors with Agricultural Applications)
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19 pages, 5777 KiB  
Article
Multimodal Fine-Grained Transformer Model for Pest Recognition
by Yinshuo Zhang, Lei Chen and Yuan Yuan
Electronics 2023, 12(12), 2620; https://doi.org/10.3390/electronics12122620 - 10 Jun 2023
Cited by 10 | Viewed by 2412
Abstract
Deep learning has shown great potential in smart agriculture, especially in the field of pest recognition. However, existing methods require large datasets and do not exploit the semantic associations between multimodal data. To address these problems, this paper proposes a multimodal fine-grained transformer [...] Read more.
Deep learning has shown great potential in smart agriculture, especially in the field of pest recognition. However, existing methods require large datasets and do not exploit the semantic associations between multimodal data. To address these problems, this paper proposes a multimodal fine-grained transformer (MMFGT) model, a novel pest recognition method that improves three aspects of transformer architecture to meet the needs of few-shot pest recognition. On the one hand, the MMFGT uses self-supervised learning to extend the transformer structure to extract target features using contrastive learning to reduce the reliance on data volume. On the other hand, fine-grained recognition is integrated into the MMFGT to focus attention on finely differentiated areas of pest images to improve recognition accuracy. In addition, the MMFGT further improves the performance in pest recognition by using the joint multimodal information from the pest’s image and natural language description. Extensive experimental results demonstrate that the MMFGT obtains more competitive results compared to other excellent models, such as ResNet, ViT, SwinT, DINO, and EsViT, in pest recognition tasks, with recognition accuracy up to 98.12% and achieving 5.92% higher accuracy compared to the state-of-the-art DINO method for the baseline. Full article
(This article belongs to the Special Issue Artificial Intelligence and Sensors with Agricultural Applications)
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14 pages, 6099 KiB  
Article
Recognition and Detection of Persimmon in a Natural Environment Based on an Improved YOLOv5 Model
by Ziang Cao, Fangfang Mei, Dashan Zhang, Bingyou Liu, Yuwei Wang and Wenhui Hou
Electronics 2023, 12(4), 785; https://doi.org/10.3390/electronics12040785 - 4 Feb 2023
Cited by 11 | Viewed by 2892
Abstract
Accurate and rapid recognition of fruit is the guarantee of intelligent persimmon picking. Given the changes in the light and occlusion conditions in a natural environment, this study developed a detection method based on the improved YOLOv5 model. This approach has several critical [...] Read more.
Accurate and rapid recognition of fruit is the guarantee of intelligent persimmon picking. Given the changes in the light and occlusion conditions in a natural environment, this study developed a detection method based on the improved YOLOv5 model. This approach has several critical steps, including optimizing the loss function based on the traditional YOLOv5, combining the centralized feature pyramid (CFP), integrating the convolutional block attention module (CBAM), and adding a small target detection layer. Images of ripe and unripe persimmons were collected from fruit trees. These images were preprocessed to enhance the contrast, and they were then extended by means of image enhancement to increase the robustness of the network. To test the proposed method, several experiments, including detection and comparative experiments, were conducted. From the detection experiments, persimmons in a natural environment could be detected successfully using the proposed model, with the accuracy rate reaching 92.69%, the recall rate reaching 94.05%, and the average accuracy rate reaching 95.53%. Furthermore, from the comparison experiments, the proposed model performed better than the traditional YOLOv5 and single-shot multibox detector (SSD) models, improving the detection accuracy while reducing the leak detection and false detection rate. These findings provide some references for the automatic picking of persimmons. Full article
(This article belongs to the Special Issue Artificial Intelligence and Sensors with Agricultural Applications)
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