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J. Imaging
Special Issues
Imaging Applications in Agriculture
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Imaging Applications in Agriculture

A special issue of Journal of Imaging (ISSN 2313-433X).

Deadline for manuscript submissions: 31 August 2025 | Viewed by 10195

Special Issue Editors

Prof. Dr. Pierre Gouton

E-Mail Website
Guest Editor
Laboratoire ImVia, UFR Sciences et Techniques, Université de Bourgogne, 21078 Dijon, France
Interests: computer vision; robot vision; security access and monitoring; multispectral imaging; medical image processing; agriculture applications
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Saeid Minaei

E-Mail Website
Guest Editor
Biosystems Engineering Department, Faculty of Agriculture, Tarbiat Modares University, Tehran P. O. Box 14115-336, Iran
Interests: agricultural automation and mechatronics; precision agriculture; non-destructive testing of agricultural materials; quality assessment; loss and waste management; agricultural machine design
Dr. Vahid Mohammadi

E-Mail Website
Guest Editor
Laboratoire CREATIS, INSA, Université Lyon 1, Bâtiment Léonard de Vinci, 69100 Villeurbanne, France
Interests: image processing; multispectral/hyperspectral imaging; 3D imaging; artificial intelligence; precision agriculture

Special Issue Information

Dear Colleagues,

Agriculture has witnessed a transformative evolution through the integration of advanced imaging technologies. The intersection of agriculture and imaging applications has paved the way for innovative solutions, offering unprecedented insights and efficiency in various aspects of the agricultural domain. This Special Issue aims to explore and showcase the diverse range of imaging applications that have revolutionized modern agriculture. 

Topics of Interest:

We invite the submission of contributions covering a wide array of topics related to imaging applications in agriculture, including but not limited to:

  1. Precision Agriculture:
    • Remote sensing technologies for crop monitoring.
    • Satellite- and drone-based imaging for precision farming.
    • Applications of GIS and GPS in precision agriculture.
  2. Crop Health Monitoring:
    • Imaging techniques for the early detection of plant diseases.
    • Use of infrared and hyperspectral imaging in assessing crop health.
    • Image-based analysis of nutrient deficiencies in crops.
  3. Smart Farming and Automation:
    • Computer vision applications in autonomous farming.
    • Robotics and imaging for automated harvesting and cultivation.
    • Intelligent sensor networks for real-time monitoring.
  4. Imaging for Soil Analysis:
    • Use of imaging technologies to assess soil quality.
    • Monitoring of soil erosion and degradation through imaging.
    • Image-based soil nutrient mapping.
  5. Emerging Technologies:
    • Applications of machine learning and AI in agricultural imaging.
    • Augmented reality (AR) and virtual reality (VR) in farm training and simulation.
    • Blockchain and imaging for traceability in agriculture.
  6. Post-Harvest Quality Monitoring
    • Food quality assessment.
    • Processing/storage monitoring.
    • Detection of adulteration.
  7. Livestock/poultry production
    • Growth monitoring.
    • Welfare/Health assessment.
    • Disease detection.

Prof. Dr. Pierre Gouton
Prof. Dr. Saeid Minaei
Dr. Vahid Mohammadi
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. Journal of Imaging 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 1800 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

  • precision agriculture
  • crop health monitoring
  • smart farming and automation
  • imaging for soil analysis
  • emerging technologies

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

Published Papers (5 papers)

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Research

16 pages, 9878 KiB  
Article
An Enhanced Deep Learning Model for Effective Crop Pest and Disease Detection
by Yongqi Yuan, Jinhua Sun and Qian Zhang
J. Imaging 2024, 10(11), 279; https://doi.org/10.3390/jimaging10110279 - 2 Nov 2024
Viewed by 1133
Abstract
Traditional machine learning methods struggle with plant pest and disease image recognition, particularly when dealing with small sample sizes, indistinct features, and numerous categories. This paper proposes an improved ResNet34 model (ESA-ResNet34) for crop pest and disease detection. The model employs ResNet34 as [...] Read more.
Traditional machine learning methods struggle with plant pest and disease image recognition, particularly when dealing with small sample sizes, indistinct features, and numerous categories. This paper proposes an improved ResNet34 model (ESA-ResNet34) for crop pest and disease detection. The model employs ResNet34 as its backbone and introduces an efficient spatial attention mechanism (effective spatial attention, ESA) to focus on key regions of the images. By replacing the standard convolutions in ResNet34 with depthwise separable convolutions, the model reduces its parameter count by 85.37% and its computational load by 84.51%. Additionally, Dropout is used to mitigate overfitting, and data augmentation techniques such as center cropping and horizontal flipping are employed to enhance the model’s robustness. The experimental results show that the improved algorithm achieves an accuracy, precision, and F1 score of 87.09%, 87.14%, and 86.91%, respectively, outperforming several benchmark models (including AlexNet, VGG16, MobileNet, DenseNet, and various ResNet variants). These findings demonstrate that the proposed ESA-ResNet34 model significantly enhances crop pest and disease detection. Full article
(This article belongs to the Special Issue Imaging Applications in Agriculture)
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12 pages, 2150 KiB  
Article
Optimized Crop Disease Identification in Bangladesh: A Deep Learning and SVM Hybrid Model for Rice, Potato, and Corn
by Shohag Barman, Fahmid Al Farid, Jaohar Raihan, Niaz Ashraf Khan, Md. Ferdous Bin Hafiz, Aditi Bhattacharya, Zaeed Mahmud, Sadia Afrin Ridita, Md Tanjil Sarker, Hezerul Abdul Karim and Sarina Mansor
J. Imaging 2024, 10(8), 183; https://doi.org/10.3390/jimaging10080183 - 30 Jul 2024
Viewed by 1594
Abstract
Agriculture plays a vital role in Bangladesh’s economy. It is essential to ensure the proper growth and health of crops for the development of the agricultural sector. In the context of Bangladesh, crop diseases pose a significant threat to agricultural output and, consequently, [...] Read more.
Agriculture plays a vital role in Bangladesh’s economy. It is essential to ensure the proper growth and health of crops for the development of the agricultural sector. In the context of Bangladesh, crop diseases pose a significant threat to agricultural output and, consequently, food security. This necessitates the timely and precise identification of such diseases to ensure the sustainability of food production. This study focuses on building a hybrid deep learning model for the identification of three specific diseases affecting three major crops: late blight in potatoes, brown spot in rice, and common rust in corn. The proposed model leverages EfficientNetB0′s feature extraction capabilities, known for achieving rapid high learning rates, coupled with the classification proficiency of SVMs, a well-established machine learning algorithm. This unified approach streamlines data processing and feature extraction, potentially improving model generalizability across diverse crops and diseases. It also aims to address the challenges of computational efficiency and accuracy that are often encountered in precision agriculture applications. The proposed hybrid model achieved 97.29% accuracy. A comparative analysis with other models, CNN, VGG16, ResNet50, Xception, Mobilenet V2, Autoencoders, Inception v3, and EfficientNetB0 each achieving an accuracy of 86.57%, 83.29%, 68.79%, 94.07%, 90.71%, 87.90%, 94.14%, and 96.14% respectively, demonstrated the superior performance of our proposed model. Full article
(This article belongs to the Special Issue Imaging Applications in Agriculture)
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13 pages, 38675 KiB  
Article
Efficient Wheat Head Segmentation with Minimal Annotation: A Generative Approach
by Jaden Myers, Keyhan Najafian, Farhad Maleki and Katie Ovens
J. Imaging 2024, 10(7), 152; https://doi.org/10.3390/jimaging10070152 - 21 Jun 2024
Viewed by 1123
Abstract
Deep learning models have been used for a variety of image processing tasks. However, most of these models are developed through supervised learning approaches, which rely heavily on the availability of large-scale annotated datasets. Developing such datasets is tedious and expensive. In the [...] Read more.
Deep learning models have been used for a variety of image processing tasks. However, most of these models are developed through supervised learning approaches, which rely heavily on the availability of large-scale annotated datasets. Developing such datasets is tedious and expensive. In the absence of an annotated dataset, synthetic data can be used for model development; however, due to the substantial differences between simulated and real data, a phenomenon referred to as domain gap, the resulting models often underperform when applied to real data. In this research, we aim to address this challenge by first computationally simulating a large-scale annotated dataset and then using a generative adversarial network (GAN) to fill the gap between simulated and real images. This approach results in a synthetic dataset that can be effectively utilized to train a deep-learning model. Using this approach, we developed a realistic annotated synthetic dataset for wheat head segmentation. This dataset was then used to develop a deep-learning model for semantic segmentation. The resulting model achieved a Dice score of 83.4% on an internal dataset and Dice scores of 79.6% and 83.6% on two external datasets from the Global Wheat Head Detection datasets. While we proposed this approach in the context of wheat head segmentation, it can be generalized to other crop types or, more broadly, to images with dense, repeated patterns such as those found in cellular imagery. Full article
(This article belongs to the Special Issue Imaging Applications in Agriculture)
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15 pages, 3252 KiB  
Article
Precision Agriculture: Computer Vision-Enabled Sugarcane Plant Counting in the Tillering Phase
by Muhammad Talha Ubaid and Sameena Javaid
J. Imaging 2024, 10(5), 102; https://doi.org/10.3390/jimaging10050102 - 26 Apr 2024
Cited by 1 | Viewed by 2593
Abstract
The world’s most significant yield by production quantity is sugarcane. It is the primary source for sugar, ethanol, chipboards, paper, barrages, and confectionery. Many people are affiliated with sugarcane production and their products around the globe. The sugarcane industries make an agreement with [...] Read more.
The world’s most significant yield by production quantity is sugarcane. It is the primary source for sugar, ethanol, chipboards, paper, barrages, and confectionery. Many people are affiliated with sugarcane production and their products around the globe. The sugarcane industries make an agreement with farmers before the tillering phase of plants. Industries are keen on knowing the sugarcane field’s pre-harvest estimation for planning their production and purchases. The proposed research contribution is twofold: by publishing our newly developed dataset, we also present a methodology to estimate the number of sugarcane plants in the tillering phase. The dataset has been obtained from sugarcane fields in the fall season. In this work, a modified architecture of Faster R-CNN with feature extraction using VGG-16 with Inception-v3 modules and sigmoid threshold function has been proposed for the detection and classification of sugarcane plants. Significantly promising results with 82.10% accuracy have been obtained with the proposed architecture, showing the viability of the developed methodology. Full article
(This article belongs to the Special Issue Imaging Applications in Agriculture)
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12 pages, 1622 KiB  
Article
A Mobile App for Detecting Potato Crop Diseases
by Dunia Pineda Medina, Ileana Miranda Cabrera, Rolisbel Alfonso de la Cruz, Lizandra Guerra Arzuaga, Sandra Cuello Portal and Monica Bianchini
J. Imaging 2024, 10(2), 47; https://doi.org/10.3390/jimaging10020047 - 13 Feb 2024
Cited by 1 | Viewed by 2815
Abstract
Artificial intelligence techniques are now widely used in various agricultural applications, including the detection of devastating diseases such as late blight (Phytophthora infestans) and early blight (Alternaria solani) affecting potato (Solanum tuberorsum L.) crops. In this paper, we [...] Read more.
Artificial intelligence techniques are now widely used in various agricultural applications, including the detection of devastating diseases such as late blight (Phytophthora infestans) and early blight (Alternaria solani) affecting potato (Solanum tuberorsum L.) crops. In this paper, we present a mobile application for detecting potato crop diseases based on deep neural networks. The images were taken from the PlantVillage dataset with a batch of 1000 images for each of the three identified classes (healthy, early blight-diseased, late blight-diseased). An exploratory analysis of the architectures used for early and late blight diagnosis in potatoes was performed, achieving an accuracy of 98.7%, with MobileNetv2. Based on the results obtained, an offline mobile application was developed, supported on devices with Android 4.1 or later, also featuring an information section on the 27 diseases affecting potato crops and a gallery of symptoms. For future work, segmentation techniques will be used to highlight the damaged region in the potato leaf by evaluating its extent and possibly identifying different types of diseases affecting the same plant. Full article
(This article belongs to the Special Issue Imaging Applications in Agriculture)
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