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AgriEngineering
Special Issues
Information and Communications Technology in Agriculture
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Information and Communications Technology in Agriculture

A special issue of AgriEngineering (ISSN 2624-7402). This special issue belongs to the section "Computer Applications and Artificial Intelligence in Agriculture".

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 6097

Special Issue Editors

Prof. Dr. Ray E. Sheriff

E-Mail Website1 Website2
Guest Editor
Department of Computer Science, Edge Hill University, Ormskirk L39 4QP, UK
Interests: telecommunications; satellite communications; mobile communications; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Prof. Linbo Qing

E-Mail Website
Guest Editor
College of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China
Interests: artificial intelligence; computer vision; image/video coding; remote sensing

Special Issue Information

Dear Colleagues,

In recent years, the employment of information and communications technology (ICT) for precision agriculture has rapidly gathered pace throughout the world. The advantages of such an approach can be seen through more informed decision making, improved farming efficiency, better quality of harvests and greater crop yields. Such advances in farming techniques can be attributed to the growth in the capabilities of enabling technologies and the interpretation of the data that they provide.

There are now numerous ways in which ICT can be applied for precision agriculture. Wireless sensors can be used to give information on the quality of soil, and the level of hydration, while 5G and the Internet of things provide the ability to communicate with low-power, in situ devices to retrieve data and communicate this information for analysis. The data retrieved from wireless sensors can be interpreted using artificial intelligence techniques to determine the health and quality of plants and crops, while unmanned aerial vehicles and earth observation satellites can provide detailed geographical imagery to allow for optimum use of the land for planting and harvesting crops. These are just some examples of how innovation in the use of ICT is influencing agricultural practice.

This Special Issue is focused on showcasing original research on the employment of information and communications technology for precision agriculture. Contributions could include but are not limited to:

  • The use of 5G and the Internet of things for precision agriculture.
  • The interpretation of data on crops using artificial intelligence techniques and other means of data analysis.
  • The application of high-resolution satellite earth observation imagery for management of the land.
  • The deployment of wireless sensor technology for more efficient farming practice.
  • The employment of unmanned aerial vehicles (UAVs) or drone technology for monitoring crops.
  • Case studies that demonstrate the effectiveness of ICT on precision agriculture in practical situations.

Prof. Ray E. Sheriff
Prof. Linbo Qing
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. AgriEngineering is an international peer-reviewed open access quarterly 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 1600 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

  • artificial intelligence
  • data analytics
  • fifth-generation mobile
  • Internet of things
  • mobile communications
  • precision agriculture
  • satellite earth imagery
  • smart farming
  • unmanned aerial vehicles
  • wireless communications
  • wireless sensors

Published Papers (1 paper)

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Research

17 pages, 3628 KiB  
Article
Tomato Leaf Diseases Classification Based on Leaf Images: A Comparison between Classical Machine Learning and Deep Learning Methods
by Lijuan Tan, Jinzhu Lu and Huanyu Jiang
AgriEngineering 2021, 3(3), 542-558; https://0-doi-org.brum.beds.ac.uk/10.3390/agriengineering3030035 - 09 Jul 2021
Cited by 56 | Viewed by 5405
Abstract
Tomato production can be greatly reduced due to various diseases, such as bacterial spot, early blight, and leaf mold. Rapid recognition and timely treatment of diseases can minimize tomato production loss. Nowadays, a large number of researchers (including different institutes, laboratories, and universities) [...] Read more.
Tomato production can be greatly reduced due to various diseases, such as bacterial spot, early blight, and leaf mold. Rapid recognition and timely treatment of diseases can minimize tomato production loss. Nowadays, a large number of researchers (including different institutes, laboratories, and universities) have developed and examined various traditional machine learning (ML) and deep learning (DL) algorithms for plant disease classification. However, through pass survey analysis, we found that there are no studies comparing the classification performance of ML and DL for the tomato disease classification problem. The performance and outcomes of different traditional ML and DL (a subset of ML) methods may vary depending on the datasets used and the tasks to be solved. This study generally aimed to identify the most suitable ML/DL models for the PlantVillage tomato dataset and the tomato disease classification problem. For machine learning algorithm implementation, we used different methods to extract disease features manually. In our study, we extracted a total of 52 texture features using local binary pattern (LBP) and gray level co-occurrence matrix (GLCM) methods and 105 color features using color moment and color histogram methods. Among all the feature extraction methods, the COLOR+GLCM method obtained the best result. By comparing the different methods, we found that the metrics (accuracy, precision, recall, F1 score) of the tested deep learning networks (AlexNet, VGG16, ResNet34, EfficientNet-b0, and MobileNetV2) were all better than those of the measured machine learning algorithms (support vector machine (SVM), k-nearest neighbor (kNN), and random forest (RF)). Furthermore, we found that, for our dataset and classification task, among the tested ML/DL algorithms, the ResNet34 network obtained the best results, with accuracy of 99.7%, precision of 99.6%, recall of 99.7%, and F1 score of 99.7%. Full article
(This article belongs to the Special Issue Information and Communications Technology in Agriculture)
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