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Applications of Image Processing Technology in Agriculture
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Applications of Image Processing Technology in 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 2025 | Viewed by 1887

Special Issue Editors

Prof. Dr. José Miguel Molina Martínez

E-Mail Website
Guest Editor
Department of Agricultural Engineering, Technical University of Cartagena, 30202 Cartagena, Spain
Interests: water resource management; irrigation; energy efficiency; smart agriculture; agriculture automation and control; computers and electronics in agriculture
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Antonio Madueño Luna

E-Mail Website
Guest Editor
Department of Aerospace Engineering and Fluid Mechanics, University of Seville, 41092 Seville, Spain
Interests: artificial intelligence and machine learning applications; computer vision in horticulture
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue, "Applications of Image Processing Technology in Agriculture", aims to showcase the latest advancements and research into using image processing technologies within the agricultural sector. It focuses on a broad spectrum of topics, including, but not limited to, the use of computers and electronics in agriculture, computer vision, smart agriculture innovations, agricultural automation and control systems, big data analytics, neural network applications, Internet of Things (IoT) integrations, and the applications of industrial design in agriculture. Additionally, it covers water resource management, irrigation solutions, and energy efficiency improvements. Through a rigorous peer-review process, this Special Issue will gather and publish innovative studies and methodologies that demonstrate how image processing and related technologies can optimize agricultural practices, enhance crop monitoring and management, and contribute to the sustainability and efficiency of the agricultural industry.

Prof. Dr. José Miguel Molina Martínez
Prof. Dr. Antonio Madueño Luna
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

  • image processing in agriculture
  • computer vision
  • smart agriculture
  • agricultural automation
  • big data in agriculture
  • neural networks
  • Internet of Things (IoT)
  • industrial design in agriculture
  • water resource management
  • irrigation technology
  • energy efficiency

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Published Papers (2 papers)

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Research

15 pages, 3326 KiB  
Article
Comparison of Image Preprocessing Strategies for Convolutional Neural Network-Based Growth Stage Classification of Butterhead Lettuce in Industrial Plant Factories
by Jung-Sun Gloria Kim, Soo Chung, Myungjin Ko, Jihoon Song and Soo Hyun Shin
Appl. Sci. 2025, 15(11), 6278; https://doi.org/10.3390/app15116278 - 3 Jun 2025
Viewed by 658
Abstract
The increasing need for scalable and efficient crop monitoring systems in industrial plant factories calls for image-based deep learning models that are both accurate and robust to domain variability. This study investigates the feasibility of CNN-based growth stage classification of butterhead lettuce ( [...] Read more.
The increasing need for scalable and efficient crop monitoring systems in industrial plant factories calls for image-based deep learning models that are both accurate and robust to domain variability. This study investigates the feasibility of CNN-based growth stage classification of butterhead lettuce (Lactuca sativa L.) using two data types: raw images and images processed through GrabCut–Watershed segmentation. A ResNet50-based transfer learning model was trained and evaluated on each dataset, and cross-domain performance was assessed to understand generalization capability. Models trained and tested within the same domain achieved high accuracy (Model 1: 99.65%; Model 2: 97.75%). However, cross-domain evaluations revealed asymmetric performance degradation—Model 1-CDE (trained on raw images, tested on preprocessed images) achieved 82.77% accuracy, while Model 2-CDE (trained on preprocessed images, tested on raw images) dropped to 34.15%. Although GrabCut–Watershed offered clearer visual inputs, it limited the model’s ability to generalize due to reduced contextual richness and oversimplification. In terms of inference efficiency, Model 2 recorded the fastest model-only inference time (0.037 s/image), but this excluded the segmentation step. In contrast, Model 1 achieved 0.055 s/image without any additional preprocessing, making it more viable for real-time deployment. Notably, Model 1-CDE combined the fastest inference speed (0.040 s/image) with stable cross-domain performance, while Model 2-CDE was both the slowest (0.053 s/image) and least accurate. Grad-CAM visualizations further confirmed that raw image-trained models consistently attended to meaningful plant structures, whereas segmentation-trained models often failed to localize correctly in cross-domain tests. These findings demonstrate that training with raw images yields more robust, generalizable, and deployable models. The study highlights the importance of domain consistency and preprocessing trade-offs in vision-based agricultural systems and lays the groundwork for lightweight, real-time AI applications in smart farming. Full article
(This article belongs to the Special Issue Applications of Image Processing Technology in Agriculture)
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19 pages, 2079 KiB  
Article
Evaluation of Feature Selection and Regression Models to Predict Biomass of Sweet Basil by Using Drone and Satellite Imagery
by Luana Centorame, Nicolò La Porta, Michela Papandrea, Adriano Mancini and Ester Foppa Pedretti
Appl. Sci. 2025, 15(11), 6227; https://doi.org/10.3390/app15116227 - 31 May 2025
Viewed by 910
Abstract
The integration of precision agriculture technologies, such as remote sensing through drones and satellites, has significantly enhanced real-time crop monitoring. This study is among the first to combine multispectral data from both a drone equipped with Altum-PT camera and PlanetScope satellite imagery to [...] Read more.
The integration of precision agriculture technologies, such as remote sensing through drones and satellites, has significantly enhanced real-time crop monitoring. This study is among the first to combine multispectral data from both a drone equipped with Altum-PT camera and PlanetScope satellite imagery to predict fresh biomass in sweet basil grown in an open field, demonstrating the added value of integrating different spatial scales. A dataset of 40 sampling points was built by combining remote sensing data with field measurements, and seven vegetation indices were calculated for each point. Feature selection was performed using three different methods (F-score, Recursive Feature Elimination, and model-based selection), and the most informative features were then processed through Principal Component Analysis. Eight regression models were trained and evaluated using leave-one-out cross-validation. The best-performing models were Random Forest (R2 = 0.96 in training, R2 = 0.65 in testing) and k-Nearest Neighbours (R2 = 0.74 in training, R2 = 0.94 in testing), with kNN demonstrating superior generalization capability on unseen data. These findings highlight the potential of combining drone and satellite imagery for modelling basil agronomic traits, offering valuable insights for optimizing crop management strategies. Full article
(This article belongs to the Special Issue Applications of Image Processing Technology in Agriculture)
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