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Sustainable Development of Intelligent Agriculture
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Sustainable Development of Intelligent Agriculture

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Agriculture".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 3560

Special Issue Editors

Prof. Dr. Helong Yu

E-Mail Website
Guest Editor
Institute for the Smart Agriculture, Jilin Agricultural University, Changchun, China
Interests: intelligent agriculture; digital agriculture
Prof. Dr. Jian Zhang

E-Mail Website1 Website2
Guest Editor
1. Department of Biology, University of British Columbia, Okanagan, Kelowna, BC V1V 1V7, Canada
2. Faculty of Agronomy, Jilin Agricultural University, Changchun 131018, China
Interests: digital agriculture; bioinformatics; genomics; plant phenomics; indoor breeding
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yuntao Ma

E-Mail Website
Guest Editor
College of Land Science and Technology, China Agricultural University, Beijing 100193, China
Interests: 3D phenotyping; precision agriculture; crop modeling; UAV proximity; image analysis; multi-source data fusion
Special Issues, Collections and Topics in MDPI journals
Dr. Jing Zhou

E-Mail Website
Guest Editor
College of Information Technology, Jilin Agricultural University, Changchun, China
Interests: crop phenotype monitoring; intelligent tillage equipment
Dr. Li Ma

E-Mail Website
Guest Editor
College of Information Technology, Jilin Agricultural University, Changchun, China
Interests: deep learning; computer agricultural applications; image processing

Special Issue Information

Dear Colleagues,

Today, mankind is facing global problems, including food shortages, population growth, energy crises, environmental pollution, and climate change. Agriculture is the basis of human survival; however, traditional agricultural production methods lead to a waste of resources, environmental damage, and other issues that are not conducive to the sustainable agricultural development necessary to alleviate and solve these problems. Intelligent agriculture optimizes production decisions, continuously improves production processes, increases agricultural production efficiency, and achieves sustainable economic, social, and environmental development through digital technologies such as artificial intelligence, big data, blockchain, cloud computing, the Internet of Things, and 5G.

We are pleased to invite you to contribute to this Special Issue, entitled “Sustainable Development of Intelligent Agriculture”, which aims to promote high-quality research on recent advances in this field and inspiring related research efforts.

This Special Issue aims to cover all recent advancements in experimental and theoretical aspects related to this field. We are particularly looking for innovative papers that address all levels of the sustainable development of intelligent agriculture, from requirements, development paths, and solutions to specific key technologies, theories, and algorithms, providing new ways to handle these problems or address them in a more systematic manner.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Soil informatics;
  • Seed informatics;
  • Smart fungi;
  • Agricultural intelligent equipment;
  • Research on optical agriculture.

Prof. Dr. Helong Yu
Prof. Dr. Jian Zhang
Prof. Dr. Yuntao Ma
Dr. Jing Zhou
Dr. Li Ma
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. Sustainability 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

  • intelligent agriculture
  • precision agriculture
  • circular agriculture
  • ecological agriculture
  • unmanned farm
  • digital agriculture
  • sustainable agriculture
  • optical agriculture
  • intelligent agricultural machinery
  • artificial intelligence

Published Papers (2 papers)

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Research

23 pages, 8728 KiB  
Article
Sample Expansion and Classification Model of Maize Leaf Diseases Based on the Self-Attention CycleGAN
by Hongliang Guo, Mingyang Li, Ruizheng Hou, Hanbo Liu, Xudan Zhou, Chunli Zhao, Xiao Chen and Lianxing Gao
Sustainability 2023, 15(18), 13420; https://doi.org/10.3390/su151813420 - 07 Sep 2023
Cited by 2 | Viewed by 942
Abstract
In order to address the limited scale and insufficient diversity of research datasets for maize leaf diseases, this study proposes a maize disease image generation algorithm based on the cycle generative adversarial network (CycleGAN). With the disease image transfer method, healthy maize images [...] Read more.
In order to address the limited scale and insufficient diversity of research datasets for maize leaf diseases, this study proposes a maize disease image generation algorithm based on the cycle generative adversarial network (CycleGAN). With the disease image transfer method, healthy maize images can be transformed into diseased crop images. To improve the accuracy of the generated data, the category activation mapping attention mechanism is integrated into the original CycleGAN generator and discriminator, and a feature recombination loss function is constructed in the discriminator. In addition, the minimum absolute error is used to calculate the differences between the hidden layer feature representations, and backpropagation is employed to enhance the contour information of the generated images. To demonstrate the effectiveness of this method, the improved CycleGAN algorithm is used to transform healthy maize leaf images. Evaluation metrics, such as peak signal-to-noise ratio (PSNR), structural similarity (SSIM), Fréchet inception distance (FID), and grayscale histogram can prove that the obtained maize leaf disease images perform better in terms of background and detail preservation. Furthermore, using this method, the original CycleGAN method, and the Pix2Pix method, the dataset is expanded, and a recognition network is used to perform classification tasks on different datasets. The dataset generated by this method achieves the best performance in the classification tasks, with an average accuracy rate of over 91%. These experiments indicate the feasibility of this model in generating high-quality maize disease leaf images. It not only addresses the limitation of existing maize disease datasets but also improves the accuracy of maize disease recognition in small-sample maize leaf disease classification tasks. Full article
(This article belongs to the Special Issue Sustainable Development of Intelligent Agriculture)
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13 pages, 3221 KiB  
Article
Analysis and Research on Rice Disease Identification Method Based on Deep Learning
by He Liu, Yuduo Cui, Jiamu Wang and Helong Yu
Sustainability 2023, 15(12), 9321; https://doi.org/10.3390/su15129321 - 09 Jun 2023
Cited by 4 | Viewed by 1735
Abstract
Rice is one of the most important food crops in China and around the world. However, with the continuous transformation of human activities, the quality of climate, soil, and water sources has also changed, and disease affecting rice has become increasingly serious. Traditional [...] Read more.
Rice is one of the most important food crops in China and around the world. However, with the continuous transformation of human activities, the quality of climate, soil, and water sources has also changed, and disease affecting rice has become increasingly serious. Traditional artificial pest identification methods have been unable to adapt to the occurrence of a large number of diseases, and artificial naked eye identification also increases the uncertainty of the identification results, and cannot “suit the remedy to the case”, which will not cure the disease, or even achieve half the result with half the effort. In the incidence range of rice diseases, rice blast, rice false smut, and bacterial blight have the highest incidence rate, the greatest harm, and are the most representative. Therefore, this paper mainly focuses on the above three categories. In this paper, the identification of rice diseases is further studied. First, sample pictures of rice blast, rice false smut, and bacterial leaf blight diseases are collected. Due to the differences in the distance and light of the sample photos, their size and angle is biased. Therefore, some means are needed to unify the specifications of these images, so as to improve the efficiency of network model recognition. Neural network recognition needs to absorb many sample images to classify and learn features. The main research objects of this paper are rice blast, rice false smut, and bacterial wilt. Therefore, this paper also expands the data set for this kind of disease, and unifies the specifications through size cutting, angle change, and vertical symmetrical mirror image processing. Then, we built a new network model based on deep learning to realize the parameter initialization design. The accuracy of the rice disease identification model built at the beginning does not satisfy the practical requirements. In order to upgrade the model in depth, this experiment increases the entry point of analysis and research, and integrates four parameters: iteration times, batch size, learning rate, and optimization algorithm in order to strive for the optimization of the experimental results. In this study, the confusion matrix is selected as the evaluation standard, and experimental results with more objectivity and reference value are obtained through the horizontal comparison of visual graphics generator (VGG) and residual network (ResNet), two highly referential network models. The results show that the recognition accuracy of the optimized model is 98.64%, which achieves the goal of accurately identifying diseases. Full article
(This article belongs to the Special Issue Sustainable Development of Intelligent Agriculture)
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