Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.7
3.0
Journals
AgriEngineering
Special Issues
Exploring the Application of Artificial Intelligence and Image Processing in...
share announcement

Exploring the Application of Artificial Intelligence and Image Processing 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: 31 May 2025 | Viewed by 9791

Special Issue Editors

Dr. Yee Siang Gan

E-Mail Website
Guest Editor
School of Architecture, Feng Chia University, Taichung 40724, Taiwan
Interests: image processing; robotics in indoor navigation; deep learning; AI vision computing; image object detection and recognition system; DNA computing; discrete mathematics
Dr. Sze-Teng Liong

E-Mail Website
Guest Editor
Department of Electronic Engineering, Feng Chia University, Taichung 40724, Taiwan
Interests: image processing; computer vision; data analytics

Special Issue Information

Dear Colleagues,

In recent years, the utilization of artificial intelligence (AI) technology has gained popularity across various sectors, including, but not limited to, robotics, education, banking, and agriculture. Advancements in sensing technologies, such as RGB-D, multi- and hyper-spectral, and 3D technologies, in conjunction with the proliferation of the Internet of Things (IoT), have enabled the retrieval of information across a wide range of spatial, spectral, and temporal domains. This, coupled with the integration of AI approaches, has led to the emergence of new insights and analysis. In particular, AI-powered computer vision technologies have become crucial in the development of intelligent and automated solutions. Within the agricultural sector, the implementation of AI has led to significant improvements in crop production and real-time monitoring, harvesting, processing, and marketing. Various hi-tech computer-based systems have been developed to determine important parameters such as weed detection, yield detection, and crop quality. However, it is essential to note that understanding and addressing the challenges related to safety and quality assessment for food production using AI technologies is a necessary step in realizing the full potential of these technologies within the agricultural sector. As such, this journal welcomes both fundamental science and applied research that describes the practical applications of AI methods in the fields of agriculture, food and bio-system engineering, and related areas.

The journal welcomes original research articles, review articles, perspective papers and short communications on the following topics of interest:

  • AI-based precision agriculture;
  • Smart sensors and Internet of Things;
  • Agricultural robotics and automation equipment;
  • Computational intelligence in agriculture;
  • AI in agricultural optimization management;
  • Intelligent systems for agriculture;
  • Precision agricultural aviation;
  • Expert systems in agriculture;
  • Remote sensing in agriculture;
  • AI technology in aquiculture;
  • AI in food engineering;
  • Automatic navigation and self-driving technology;
  • Intelligent interfaces and human–machine interaction;
  • Machine vision and image/signal processing;
  • Machine learning and pattern recognition.

Dr. Yee Siang Gan
Dr. Sze-Teng Liong
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 monthly 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

  • agriculture
  • AIOT
  • artificial intelligence
  • image processing
  • robotics

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (6 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

16 pages, 7431 KiB  
Article
Deep Learning-Based Model for Effective Classification of Ziziphus jujuba Using RGB Images
by Yu-Jin Jeon, So Jin Park, Hyein Lee, Ho-Youn Kim and Dae-Hyun Jung
AgriEngineering 2024, 6(4), 4604-4619; https://doi.org/10.3390/agriengineering6040263 - 3 Dec 2024
Viewed by 575
Abstract
Ensuring the quality of medicinal herbs in the herbal market is crucial. However, the genetic and physical similarities among medicinal materials have led to issues of mixing and counterfeit distribution, posing significant challenges to quality assurance. Recent advancements in deep learning technology, widely [...] Read more.
Ensuring the quality of medicinal herbs in the herbal market is crucial. However, the genetic and physical similarities among medicinal materials have led to issues of mixing and counterfeit distribution, posing significant challenges to quality assurance. Recent advancements in deep learning technology, widely applied in the field of computer vision, have demonstrated the potential to classify images quickly and accurately, even those that can only be distinguished by experts. This study aimed to develop a classification model based on deep learning technology to distinguish RGB images of seeds from Ziziphus jujuba Mill. var. spinosa, Ziziphus mauritiana Lam., and Hovenia dulcis Thunb. Using three advanced convolutional neural network (CNN) architectures—ResNet-50, Inception-v3, and DenseNet-121—all models demonstrated a classification performance above 98% on the test set, with classification times as low as 23 ms. These results validate that the models and methods developed in this study can effectively distinguish Z. jujuba seeds from morphologically similar species. Furthermore, the strong performance and speed of these models make them suitable for practical use in quality inspection settings. Full article
(This article belongs to the Special Issue Exploring the Application of Artificial Intelligence and Image Processing in Agriculture)
►▼ Show Figures

Figure 1

17 pages, 5119 KiB  
Article
Application of a Real-Time Field-Programmable Gate Array-Based Image-Processing System for Crop Monitoring in Precision Agriculture
by Sabiha Shahid Antora, Mohammad Ashik Alahe, Young K. Chang, Tri Nguyen-Quang and Brandon Heung
AgriEngineering 2024, 6(3), 3345-3361; https://doi.org/10.3390/agriengineering6030191 - 14 Sep 2024
Viewed by 1167
Abstract
Precision agriculture (PA) technologies combined with remote sensors, GPS, and GIS are transforming the agricultural industry while promoting sustainable farming practices with the ability to optimize resource utilization and minimize environmental impact. However, their implementation faces challenges such as high computational costs, complexity, [...] Read more.
Precision agriculture (PA) technologies combined with remote sensors, GPS, and GIS are transforming the agricultural industry while promoting sustainable farming practices with the ability to optimize resource utilization and minimize environmental impact. However, their implementation faces challenges such as high computational costs, complexity, low image resolution, and limited GPS accuracy. These issues hinder timely delivery of prescription maps and impede farmers’ ability to make effective, on-the-spot decisions regarding farm management, especially in stress-sensitive crops. Therefore, this study proposes field programmable gate array (FPGA)-based hardware solutions and real-time kinematic GPS (RTK-GPS) to develop a real-time crop-monitoring system that can address the limitations of current PA technologies. Our proposed system uses high-accuracy RTK and real-time FPGA-based image-processing (RFIP) devices for data collection, geotagging real-time field data via Python and a camera. The acquired images are processed to extract metadata then visualized as a heat map on Google Maps, indicating green area intensity based on romaine lettuce leafage. The RFIP system showed a strong correlation (R2 = 0.9566) with a reference system and performed well in field tests, providing a Lin’s concordance correlation coefficient (CCC) of 0.8292. This study demonstrates the potential of the developed system to address current PA limitations by providing real-time, accurate data for immediate decision making. In the future, this proposed system will be integrated with autonomous farm equipment to further enhance sustainable farming practices, including real-time crop health monitoring, yield assessment, and crop disease detection. Full article
(This article belongs to the Special Issue Exploring the Application of Artificial Intelligence and Image Processing in Agriculture)
►▼ Show Figures

Figure 1

24 pages, 3934 KiB  
Article
Computational Techniques for Analysis of Thermal Images of Pigs and Characterization of Heat Stress in the Rearing Environment
by Maria de Fátima Araújo Alves, Héliton Pandorfi, Rodrigo Gabriel Ferreira Soares, Gledson Luiz Pontes de Almeida, Taize Calvacante Santana and Marcos Vinícius da Silva
AgriEngineering 2024, 6(3), 3203-3226; https://doi.org/10.3390/agriengineering6030183 - 6 Sep 2024
Viewed by 1048
Abstract
Heat stress stands out as one of the main elements linked to concerns related to animal thermal comfort. This research aims to develop a sequential methodology for the extraction of automatic characteristics from thermal images and the classification of heat stress in pigs [...] Read more.
Heat stress stands out as one of the main elements linked to concerns related to animal thermal comfort. This research aims to develop a sequential methodology for the extraction of automatic characteristics from thermal images and the classification of heat stress in pigs by means of machine learning. Infrared images were obtained from 18 pigs housed in air-conditioned and non-air-conditioned pens. The image analysis consisted of its pre-processing, followed by color segmentation to isolate the region of interest and later the extraction of the animal’s surface temperatures, from a developed algorithm and later the recognition of the comfort pattern through machine learning. The results indicated that the automated color segmentation method was able to identify the region of interest with an average accuracy of 88% and the temperature extraction differed from the Therma Cam program by 0.82 °C. Using a Vector Support Machine (SVM), the research achieved an accuracy rate of 80% in the automatic classification of pigs in comfort and thermal discomfort, with an accuracy of 91%, indicating that the proposal has the potential to monitor and evaluate the thermal comfort of pigs effectively. Full article
(This article belongs to the Special Issue Exploring the Application of Artificial Intelligence and Image Processing in Agriculture)
►▼ Show Figures

Figure 1

13 pages, 2643 KiB  
Article
Model Development for Identifying Aromatic Herbs Using Object Detection Algorithm
by Samira Nascimento Antunes, Marcelo Tsuguio Okano, Irenilza de Alencar Nääs, William Aparecido Celestino Lopes, Fernanda Pereira Leite Aguiar, Oduvaldo Vendrametto, João Carlos Lopes Fernandes and Marcelo Eloy Fernandes
AgriEngineering 2024, 6(3), 1924-1936; https://doi.org/10.3390/agriengineering6030112 - 21 Jun 2024
Cited by 3 | Viewed by 1575
Abstract
The rapid evolution of digital technology and the increasing integration of artificial intelligence in agriculture have paved the way for groundbreaking solutions in plant identification. This research pioneers the development and training of a deep learning model to identify three aromatic plants—rosemary, mint, [...] Read more.
The rapid evolution of digital technology and the increasing integration of artificial intelligence in agriculture have paved the way for groundbreaking solutions in plant identification. This research pioneers the development and training of a deep learning model to identify three aromatic plants—rosemary, mint, and bay leaf—using advanced computer-aided detection within the You Only Look Once (YOLO) framework. Employing the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology, the study meticulously covers data understanding, preparation, modeling, evaluation, and deployment phases. The dataset, consisting of images from diverse devices and annotated with bounding boxes, was instrumental in the training process. The model’s performance was evaluated using the mean average precision at a 50% intersection over union (mAP50), a metric that combines precision and recall. The results demonstrated that the model achieved a precision of 0.7 or higher for each herb, though recall values indicated potential over-detection, suggesting the need for database expansion and methodological enhancements. This research underscores the innovative potential of deep learning in aromatic plant identification and addresses both the challenges and advantages of this technique. The findings significantly advance the integration of artificial intelligence in agriculture, promoting greater efficiency and accuracy in plant identification. Full article
(This article belongs to the Special Issue Exploring the Application of Artificial Intelligence and Image Processing in Agriculture)
►▼ Show Figures

Figure 1

14 pages, 5138 KiB  
Article
YOLO Network with a Circular Bounding Box to Classify the Flowering Degree of Chrysanthemum
by Hee-Mun Park and Jin-Hyun Park
AgriEngineering 2023, 5(3), 1530-1543; https://doi.org/10.3390/agriengineering5030094 - 31 Aug 2023
Cited by 6 | Viewed by 2913
Abstract
Detecting objects in digital images is challenging in computer vision, traditionally requiring manual threshold selection. However, object detection has improved significantly with convolutional neural networks (CNNs), and other advanced algorithms, like region-based convolutional neural networks (R-CNNs) and you only look once (YOLO). Deep [...] Read more.
Detecting objects in digital images is challenging in computer vision, traditionally requiring manual threshold selection. However, object detection has improved significantly with convolutional neural networks (CNNs), and other advanced algorithms, like region-based convolutional neural networks (R-CNNs) and you only look once (YOLO). Deep learning methods have various applications in agriculture, including detecting pests, diseases, and fruit quality. We propose a lightweight YOLOv4-Tiny-based object detection system with a circular bounding box to accurately determine chrysanthemum flower harvest time. The proposed network in this study uses a circular bounding box to accurately classify the degree of chrysanthemums blooming and detect circular objects effectively, showing better results than the network with the traditional rectangular bounding box. The proposed network has excellent scalability and can be applied to recognize general objects in a circular form. Full article
(This article belongs to the Special Issue Exploring the Application of Artificial Intelligence and Image Processing in Agriculture)
►▼ Show Figures

Figure 1

Review

Jump to: Research

15 pages, 2954 KiB  
Review
Rapid Analysis of Soil Organic Carbon in Agricultural Lands: Potential of Integrated Image Processing and Infrared Spectroscopy
by Nelundeniyage Sumuduni L. Senevirathne and Tofael Ahamed
AgriEngineering 2024, 6(3), 3001-3015; https://doi.org/10.3390/agriengineering6030172 - 20 Aug 2024
Viewed by 1370
Abstract
The significance of soil in the agricultural industry is profound, with healthy soil representing an important role in ensuring food security. In addition, soil is the largest terrestrial carbon sink on earth. The soil carbon pool is composed of both inorganic and organic [...] Read more.
The significance of soil in the agricultural industry is profound, with healthy soil representing an important role in ensuring food security. In addition, soil is the largest terrestrial carbon sink on earth. The soil carbon pool is composed of both inorganic and organic forms. The equilibrium of the soil carbon pool directly impacts the carbon cycle via all of the other processes on the planet. With the development of agricultural systems from traditional to conventional ones, and with the current era of precision agriculture, which involves making decisions based on information, the importance of understanding soil is becoming increasingly clear. The control of microenvironment conditions and soil fertility represents a key factor in achieving higher productivity in these systems. Furthermore, agriculture represents a significant contributor to carbon emissions, a topic that has become timely given the necessity for carbon neutrality. In addition to these concerns, updating soil-related data, including information on macro and micronutrient conditions, is important. Carbon represents one of the major nutrients for crops and plays a key role in the retention and release of other nutrients and the management of soil physical properties. Despite the importance of carbon, existing analytical methods are complex and expensive. This discourages frequent analyses, which results in a lack of soil carbon-related data for agricultural fields. From this perspective, in situ soil organic carbon (SOC) analysis can provide timely management information for calibrating fertilizer applications based on the soil–carbon relationship to increase soil productivity. In addition, the available data need frequent updates due to rapid changes in ecosystem services and the use of extensive fertilizers and pesticides. Despite the importance of this topic, few studies have investigated the potential of image analysis based on image processing and spectral data recording. The use of spectroscopy and visual color matching to develop SOC predictions has been considered, and the use of spectroscopic instruments has led to increased precision. Our extensive literature review shows that color models, especially Munsell color charts, are better for qualitative purposes and that Cartesian-type color models are appropriate for quantification. Even for the color model, spectroscopy data could be used, and these data have the potential to improve the precision of measurements. On the other hand, mid-infrared radiation (MIR) and near-infrared radiation (NIR) diffuse reflection has been reported to have a greater ability to predict SOC. Finally, this article reports the availability of inexpensive portable instruments that can enable the development of in situ SOC analysis from reflection and emission information with the integration of images and spectroscopy. This integration refers to machine learning algorithms with a reflection-oriented spectrophotometer and emission-based thermal images which have the potential to predict SOC without the need for expensive instruments and are easy to use in farm applications. Full article
(This article belongs to the Special Issue Exploring the Application of Artificial Intelligence and Image Processing in Agriculture)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-6
Back to TopTop