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Applied Sciences
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Big Data and AI for Food and Agriculture
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Big Data and AI for Food and Agriculture

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Agricultural Science and Technology".

Deadline for manuscript submissions: 20 September 2024 | Viewed by 7260

Special Issue Editors

Prof. Dr. Xiaohui Cui

E-Mail Website
Guest Editor
School of Cyber Science & Engineering, Wuhan University, Wuhan, China
Interests: big data; food safety; deep learning; blockchain
Prof. Dr. Jin Liu

E-Mail Website
Guest Editor
School of Computer Science, Wuhan University, Wuhan, China
Interests: software engineering; big data
Prof. Dr. Wei Li

E-Mail Website
Guest Editor
School of Artificial Intelligence & Computer Science, Jiangnan University, Wuxi, China
Interests: big data; deep learning; deep generative model

Special Issue Information

Dear Colleagues,

The Big Data and AI technologies have been increasingly applied to a lot of real-world applications. In this work, food and agriculture data analysis is undoubtedly one of the most challenging applications. The various and non-standard data on food and agriculture renders that it is a non-trivial task to apply Big Data and AI to the two domains. To address these problems, some attempts would be made by applying the Big Data and AI technologies to food and agriculture domains. Therefore, the journal of Applied Sciences release a Special Issue on topic “Big Data and AI for Food and Agriculture”.

Topic of interest includes, but are not limited to:

  1. Real-world applications and case studies that utilize big data and AI to address food problems. For example, food security, consumer’s behavior on food, food parings, food flavor analysis, etc.
  2. Real-world applications and case studies that utilize big data and AI to detect crops’ diseases and to analyze crops’ growth, production and to predict price of crops.
  3. Exploring new theories/models/algorithms/methods on AI+Food or AI+Agriculture.
  4. Cloud-based platforms/systems using Big Data and AI.
  5. Deep learning/Machine learning for food and agriculture.
  6. Learning and evolutionary computing, biometrics for food and agriculture.
  7. Intelligent systems/platforms on food and agriculture.
  8. Blockchain technology for food and agriculture
  9. Automation technology for food and agriculture

Prof. Dr. Xiaohui Cui
Prof. Dr. Jin Liu
Prof. Dr. Wei Li
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. Applied Sciences 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

  • food security & Safety
  • smart agirculture
  • deep learning
  • IoT, blockchain
  • food paring & flavor
  • climate-smart farming
  • globalization
  • future foods

Published Papers (4 papers)

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Research

21 pages, 3356 KiB  
Article
A Lightweight Crop Pest Detection Method Based on Convolutional Neural Networks
by Zekai Cheng, Rongqing Huang, Rong Qian, Wei Dong, Jingbo Zhu and Meifang Liu
Appl. Sci. 2022, 12(15), 7378; https://doi.org/10.3390/app12157378 - 22 Jul 2022
Cited by 10 | Viewed by 1461
Abstract
Existing object detection methods with many parameters and computations are not suitable for deployment on devices with poor performance in agricultural environments. Therefore, this study proposes a lightweight crop pest detection method based on convolutional neural networks, named YOLOLite-CSG. The basic architecture of [...] Read more.
Existing object detection methods with many parameters and computations are not suitable for deployment on devices with poor performance in agricultural environments. Therefore, this study proposes a lightweight crop pest detection method based on convolutional neural networks, named YOLOLite-CSG. The basic architecture of the method is derived from a simplified version of YOLOv3, namely YOLOLite, and k-means++ is utilized to improve the generation process of the prior boxes. In addition, a lightweight sandglass block and coordinate attention are used to optimize the structure of residual blocks. The method was evaluated on the CP15 crop pest dataset. Its detection precision exceeds that of YOLOv3, at 82.9%, while the number of parameters is 5 million, only 8.1% of the number used by YOLOv3, and the number of computations is 9.8 GFLOPs, only 15% of that used by YOLOv3. Furthermore, the detection precision of the method is superior to all other commonly used object detection methods evaluated in this study, with a maximum improvement of 10.6%, and it still has a significant edge in the number of parameters and computation required. The method has excellent pest detection precision with extremely few parameters and computations. It is well-suited to be deployed on equipment for detecting crop pests in agricultural environments. Full article
(This article belongs to the Special Issue Big Data and AI for Food and Agriculture)
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14 pages, 3310 KiB  
Article
Towards Optimizing Garlic Combine Harvester Design with Logistic Regression
by Zhengbo Zhu, Wei Li, Fujun Wen, Liangzhe Chen and Yan Xu
Appl. Sci. 2022, 12(12), 6015; https://doi.org/10.3390/app12126015 - 13 Jun 2022
Cited by 1 | Viewed by 1584
Abstract
In this paper, a garlic combine harvester machine was designed and some influential parameters of the machine were optimized. The working parts of the machine mainly consisted of a reel, a reciprocating cutter, a seedling conveyor, a profiling depth-stop device, a digging shovel [...] Read more.
In this paper, a garlic combine harvester machine was designed and some influential parameters of the machine were optimized. The working parts of the machine mainly consisted of a reel, a reciprocating cutter, a seedling conveyor, a profiling depth-stop device, a digging shovel and a lifting chain. Each part had unique structural parameters and motion parameters, as different parameters would deeply affect the performance of the machine. A logistical regression algorithm was utilized to analyze the working speed of the reel, the digging depth of the reciprocating cutter and the lifting speed of the lifting chain. This paper also discussed the influence of these three functions on the damage rate based on the collected data when harvesting garlic. Specifically, each function was tested 60 times for collecting data. The experimental results showed that the order of influence of the three functions on the damage rate was the digging depth, working speed and lifting speed. Moreover, the lowest damage rate was 0.18% when the digging depth was 100 mm, the working speed was 1.05 km·h−1 and the lifting speed was 0.69 m·s−1. A validation test was taken out based on the three functions of the analysis results, and the damage rate was 0.83%, which was close to the analysis results, and proved that the analysis results were accurate and meaningful. The research results are beneficial to the development and application of the garlic combine harvester. Full article
(This article belongs to the Special Issue Big Data and AI for Food and Agriculture)
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12 pages, 2147 KiB  
Article
Hyperspectral Identification of Ginseng Growth Years and Spectral Importance Analysis Based on Random Forest
by Limin Zhao, Shumin Liu, Xingfeng Chen, Zengwei Wu, Rui Yang, Tingting Shi, Yunli Zhang, Kaiwen Zhou and Jiaguo Li
Appl. Sci. 2022, 12(12), 5852; https://doi.org/10.3390/app12125852 - 08 Jun 2022
Cited by 6 | Viewed by 1661
Abstract
The growth year of ginseng is very important as it affects its economic value and even defines if ginseng can be used as medicine or food. In the case of large-scale developments in the ginseng industry, a set of non-destructive, fast, and nonprofessional [...] Read more.
The growth year of ginseng is very important as it affects its economic value and even defines if ginseng can be used as medicine or food. In the case of large-scale developments in the ginseng industry, a set of non-destructive, fast, and nonprofessional operations related to the growth year identification method is needed. The characteristics of ginseng reflectance spectral data were analyzed, and the growth year recognition model was constructed by a decision-tree-based random forest machine learning method. After independent verification, the accuracy of distinguishing ginseng food and medicine can reach 92.9%, with 6-year growth as the boundary, and 100%, with 5-year growth as the boundary. The research results show that the spectral change of ginseng is the most obvious in the fifth year, which provides a reference for the key research years based on chemical analyses and other methods. For the application of growth year recognition, the NIR band (1000–2500 nm) had little contribution to the recognition of ginseng growth years, and the band with the largest contribution was 400–650 nm. The recognition model based on machine learning provides a non-destructive, fast, and simple scheme with high accuracy for ginseng year recognition, and the spectral importance analysis conclusion of ginseng growth years provides a design reference for the development of special lightweight spectral equipment for year recognition. Full article
(This article belongs to the Special Issue Big Data and AI for Food and Agriculture)
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13 pages, 7580 KiB  
Article
Food Risk Entropy Model Based on Federated Learning
by Jiaojiao Yu, Yizhou Chen, Zhenyu Wang, Jin Liu and Bo Huang
Appl. Sci. 2022, 12(10), 5174; https://doi.org/10.3390/app12105174 - 20 May 2022
Cited by 2 | Viewed by 1439
Abstract
The safety of agricultural products is a guarantee of national security. The increasing variety of pesticides used on crops has led to an increasing abundance of pesticide residues in agricultural products, making pesticide residues an important factor in threatening health. Traditional indicators for [...] Read more.
The safety of agricultural products is a guarantee of national security. The increasing variety of pesticides used on crops has led to an increasing abundance of pesticide residues in agricultural products, making pesticide residues an important factor in threatening health. Traditional indicators for evaluating the safety of agricultural products, such as pass rates and residue rates, can only qualitatively describe the level of pesticide residues. Isolated data leads to low data utilization, data is distributed between different terminals or departments and cannot be shared, while the security of private data needs to be ensured. Therefore, we propose a risk entropy model based on federated learning. The model is able to quantitatively describe the risk level of agricultural products and achieve data fusion without exposing private data in the federated learning model. In this paper, a total of 90,510 agricultural product data samples from 2015 to 2019 are collected, with each sample containing 58 indicators. The experimental results show that the developed food safety risk entropy model can quantitatively reflect the level of risk in the target region and time interval. In addition, we have developed a multidimensional data analysis tool based on federated learning, which can achieve data integration across multiple regions and departments. Full article
(This article belongs to the Special Issue Big Data and AI for Food and Agriculture)
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