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Agriculture
Special Issues
Image Analysis Techniques in Quality Assessment of Agricultural Products
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Image Analysis Techniques in Quality Assessment of Agricultural Products

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Artificial Intelligence and Digital Agriculture".

Deadline for manuscript submissions: 20 March 2026 | Viewed by 829

Special Issue Editors

Prof. Dr. Maciej Zaborowicz

E-Mail Website
Guest Editor
Department of Biosystems Engineering, Poznan University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, Poland
Interests: computer image analysis; artificial neural networks; neural modeling; machine learning; deep learning; computer science in agriculture
Special Issues, Collections and Topics in MDPI journals
Dr. Jakub Frankowski

E-Mail Website
Guest Editor
Department of Bioeconomy, Institute of Natural Fibres and Medicinal Plants—National Research Institute, Wojska Polskiego 71B, 60-630 Poznań, Poland
Interests: environmental science; waste management; agriculture; bioengineering; chemical engineering; bioprocess engineering; fermentation technology; biofuel production; green technology; bioeconomy; circular economy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Computer image analysis and artificial intelligence methods are increasingly being used in the quality assessment of agricultural and food products. This assessment begins as early as the sowing or planting period and can continue all the way to the delivery of the finished product to the consumer's table.

The special issue enables the publication of research works, including in the implementation aspect, related to the application of digital technologies in the broad issues of the agricultural and food industries, with a particular focus on AI technologies and innovative algorithms for data processing and analysis, including data contained in digital images.

The purpose of this special issue is to present current research work on the application of modern computer techniques in quality assessment and classification of agricultural products, which includes both plants and animals, as well as control of the entire food production process.

We look forward to the publication of innovative research on the application of digital technologies in terms of the development of production systems and quality control in agriculture.

If you have an interesting research problem, a unique research methodology and an innovative solution to a scientific problem, then this issue of Agricluture is for you!

Prof. Dr. Maciej Zaborowicz
Dr. Jakub Frankowski
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. Agriculture 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 2600 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 analysis
  • Artificial Intelligence
  • quality assessment
  • agricultural products
  • digital technologies

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Published Papers (2 papers)

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Research

13 pages, 6111 KB  
Article
Automated Crop Measurements with UAVs: Evaluation of an AI-Driven Platform for Counting and Biometric Analysis
by João Victor da Silva Martins, Marcelo Rodrigues Barbosa Júnior, Lucas de Azevedo Sales, Regimar Garcia dos Santos, Wellington Souto Ribeiro and Luan Pereira de Oliveira
Agriculture 2025, 15(21), 2213; https://doi.org/10.3390/agriculture15212213 - 24 Oct 2025
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Abstract
Unmanned aerial vehicles (UAVs) are transforming agriculture through enhanced data acquisition, improved monitoring efficiency, and support for data-driven decision-making. Complementing this, AI-driven platforms provide intuitive and reliable tools for advanced UAV analytics. However, their integration remains underexplored, particularly in specialty crops. Therefore, in [...] Read more.
Unmanned aerial vehicles (UAVs) are transforming agriculture through enhanced data acquisition, improved monitoring efficiency, and support for data-driven decision-making. Complementing this, AI-driven platforms provide intuitive and reliable tools for advanced UAV analytics. However, their integration remains underexplored, particularly in specialty crops. Therefore, in this study, we evaluated the performance of an AI-driven web platform (Solvi) for automated plant counting and biometric trait estimation in two contrasting systems: pecan, a perennial nut crop, and onion, an annual vegetable. Ground-truth measurements included pecan tree number, tree height, and canopy area, as well as onion bulb number and diameter, the latter used for market class classification. Counting performance was assessed using precision, recall, and F1 score, while trait estimation was evaluated with linear regression analysis. UAV-based counts showed strong agreement with ground-truth data, achieving precision, recall, and F1 scores above 97% for both crops. For pecans, UAV-derived estimates of tree height (R2 = 0.98, error = 11.48%) and canopy area (R2 = 0.99, error = 23.16%) demonstrated high accuracy, while errors were larger in young trees compared with mature trees. For onions, UAV-derived bulb diameters achieved an R2 of 0.78 with a 6.29% error, and market class classification (medium, jumbo, colossal) was predicted with <10% error. These findings demonstrate that UAV imagery integrated with a user-friendly AI platform can deliver accurate, scalable solutions for biometric monitoring in both perennial and annual specialty crops, supporting applications in harvest planning, orchard management, and market supply forecasting. Full article
(This article belongs to the Special Issue Image Analysis Techniques in Quality Assessment of Agricultural Products)
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15 pages, 5006 KB  
Article
Phenotypic Descriptors and Image-Based Assessment of Viola cornuta L. Quality Under Photoselective Shade Nets Using a Naive Bayes Classifier
by Fátima Alejandrina Hagg-Torrijos, José Alfredo Carrillo-Salazar, Juan Manuel González-Camacho and Víctor Arturo González-Hernández
Agriculture 2025, 15(21), 2187; https://doi.org/10.3390/agriculture15212187 - 23 Oct 2025
Viewed by 211
Abstract
Light quality affects the visual and morphological traits of ornamental species, and its effects on plant quality can be quantitatively assessed using image analysis combined with machine-learning classifiers. The objective was to characterize the morphological, reproductive, and color-related traits of Viola cornuta L. [...] Read more.
Light quality affects the visual and morphological traits of ornamental species, and its effects on plant quality can be quantitatively assessed using image analysis combined with machine-learning classifiers. The objective was to characterize the morphological, reproductive, and color-related traits of Viola cornuta L. cv. Sorbeth® Coconut® Swirl® grown under red, black, green, and blue shade nets in open-field conditions in Montecillo, Mexico, based on the combined use of traditional measurements and image-based analysis. Measurements were taken 69 days after transplanting. Image analysis with a multiclass Naive Bayes classifier (98.9% accuracy) quantified flower area and the three color classes (yellow, cream, and purple). Leaf area and ground cover were measured through color-based segmentation and adaptative thresholding. Open-field plants showed the largest ground cover, with flowers (19.4%), compact canopy (37% smaller than under the black net), and the highest number of flowers (33 flowers/plant). The yellow floral area was also the largest (0.3 cm2/flower). Black, green, and blue nets promoted larger leaf areas (10 to 11 cm2/leaf), while the black net produced the largest flowers (18.6 cm2). Blue and red nets reduced cream (4.3 cm2) and purple (7.3 cm2) areas, respectively. Photoselective nets differentially modulated viola morphology and pigmentation, while open-field conditions yielded compact plants with large flower areas of the highest visual quality. Full article
(This article belongs to the Special Issue Image Analysis Techniques in Quality Assessment of Agricultural Products)
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