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AgriEngineering
Special Issues
Sensors and Computer Vision for Quality Assessment of Agricultural Products
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Sensors and Computer Vision for Quality Assessment of Agricultural Products

A special issue of AgriEngineering (ISSN 2624-7402). This special issue belongs to the section "Pre and Post-Harvest Engineering in Agriculture".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 1804

Editors

Dr. Yongqiang Li

E-Mail Website
Guest Editor
School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, China
Interests: machine vision; environmental perception; unmanned systems
Dr. Xiaofei An

E-Mail Website
Guest Editor
National Agricultural Intelligent Equipment Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
Interests: yield monitoring; precision agriculture; grain elevators; fertilizer spreaders; applicators; spreaders; soil; rotary tillage; planters

Special Issue Information

Dear Colleagues,

The global demand for high-quality, safe, and sustainably produced agricultural products continues to rise, driven by population growth, evolving consumer expectations, and stringent regulatory standards. Recent advances in sensor technologies and computer vision have opened new frontiers for real-time monitoring, grading, and traceability of agricultural commodities. The integration of imaging systems, hyperspectral/multispectral sensors, deep learning models, and edge-computing platforms is transforming how we evaluate freshness, ripeness, defects, nutritional content, and authenticity of fruits, vegetables, grains, meat, and other agricultural goods.

We are pleased to invite you to contribute to a Special Issue of AgriEngineering, titled “Sensors and Computer Vision for Quality Assessment of Agricultural Products.”

This Special Issue aims to showcase cutting-edge research at the intersection of sensing technologies, computer vision, and agricultural product quality evaluation. It seeks to compile original studies and comprehensive reviews that demonstrate novel methodologies, system integrations, field or laboratory validations, and practical applications aligned with sustainable and efficient agri-food systems.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Non-destructive sensing techniques (e.g., NIR, hyperspectral, thermal, Raman, fluorescence imaging) for quality traits;
  • Deep learning and machine learning approaches for image-based classification, segmentation, and defect detection;
  • Real-time computer vision systems for on-line sorting and grading in processing facilities;
  • Sensor fusion strategies combining multiple modalities for enhanced accuracy;
  • Portable or low-cost sensing devices for smallholder or on-farm use;
  • Benchmark datasets and open-source tools for agricultural product analysis;
  • Case studies on specific commodities (e.g., apples, rice, coffee, dairy, poultry);
  • Integration of quality assessment systems with robotics, IoT, or blockchain for traceability.

We look forward to receiving your contributions.

Dr. Yongqiang Li
Dr. Xiaofei An
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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 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

  • computer vision
  • sensors
  • non-destructive quality assessment
  • deep learning
  • agricultural products

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

Published Papers (3 papers)

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Research

21 pages, 5977 KB  
Article
Lightweight Astra-YOLO Astragalus Slices Defect Detection Method Based on Feature-Space Weight Reconstruction
by Jun You, Xin Du, Qixin Sun, Shufa Chen, Yue Jiang and Ziming Lu
AgriEngineering 2026, 8(7), 265; https://doi.org/10.3390/agriengineering8070265 - 26 Jun 2026
Viewed by 161
Abstract
To address the low efficiency and high subjectivity of manual inspection of Astragalus slices, as well as the limited fine-grained detection accuracy caused by the visual similarity between the characteristic radial “chrysanthemum heart” texture and minor defects such as insect damage and mold, [...] Read more.
To address the low efficiency and high subjectivity of manual inspection of Astragalus slices, as well as the limited fine-grained detection accuracy caused by the visual similarity between the characteristic radial “chrysanthemum heart” texture and minor defects such as insect damage and mold, this study proposes a lightweight intelligent detection model named Astra-YOLO. A dataset consisting of 622 original Astragalus slice images from four categories was divided into training, validation, and test sets at a ratio of 8:1:1. Data augmentation was applied exclusively to the training set, resulting in a total of 3110 images. Based on YOLOv11n, three targeted improvements were introduced: GhostConv lightweight convolution was employed to reduce model parameters and computational cost; the parameter-free SimAM attention mechanism was integrated to suppress interference from complex textures and enhance defect feature representation; and Wise-IoU v3 was adopted to improve bounding box regression for precise localization of small defects. The experimental results demonstrate that Astra-YOLO achieves superior performance with only 2.53 million parameters and 6.20 GFLOPs. The model attains an mAP@0.5 of 92.7%, an mAP@0.5:0.95 of 73.8%, a precision of 92.4%, and a recall of 92.1%. These results indicate that Astra-YOLO effectively balances lightweight design and detection accuracy, outperforming the baseline model and other improved variants, thereby providing reliable technical support for industrial online inspection and automated quality grading of Astragalus slices. Full article
(This article belongs to the Special Issue Sensors and Computer Vision for Quality Assessment of Agricultural Products)
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19 pages, 19256 KB  
Article
YOLOv11-LicoSeg: A Method for Measuring the Radicle Length of Licorice
by Ruxiao Bai, Haixiu He, Zhibo Zhong, Limin Yu, Xiuqing Fu and Qifeng Wu
AgriEngineering 2026, 8(6), 234; https://doi.org/10.3390/agriengineering8060234 - 9 Jun 2026
Viewed by 242
Abstract
Global climate change and soil salinization pose challenges to licorice cultivation. Evaluating seed vigor based on the dynamic changes in radicle morphology is crucial for screening and cultivating licorice varieties that are tolerant to low temperatures and salts. Traditional manual measurement of licorice [...] Read more.
Global climate change and soil salinization pose challenges to licorice cultivation. Evaluating seed vigor based on the dynamic changes in radicle morphology is crucial for screening and cultivating licorice varieties that are tolerant to low temperatures and salts. Traditional manual measurement of licorice radicle characteristics suffers from issues such as high cost, long time consumption, and large errors. The YOLOv11 instance segmentation model in the field of deep learning offers advantages including a simple architecture, strong lightweight properties, and a unified detection-segmentation framework. Therefore, this study selected the YOLOv11 model to build a deep learning framework and used the continuous time-series crop growth vitality monitoring system to collect full-time-series images of 18 groups of licorice seeds germinating under different temperature and salt stress conditions. The YOLOv11-seg model was improved by adding a Spatial Strip Attention mechanism (SSA) to enhance the spatial correlation of radicle features, replacing ordinary convolutions with a Multi-scale Edge Detail Enhancement Module (MEEM) to optimize multi-scale feature extraction capabilities, and embedding a Normalized Weighted Distance (NWD) loss function to strengthen the segmentation ability for tiny targets. The YOLOv11-LicoSeg model was constructed for segmenting and extracting licorice radicle features and calculating root length. The experimental results showed that the mAP50 of the model’s detection reached 97.4%, mAP50–95 reached 81.7%, the mAP50 of the segmentation mask reached 97.0%, and mAP50–95 reached 78.2%. Compared with the unimproved YOLOv11-seg, the mAP50 of detection increased by 0.7%, mAP50–95 increased by 1.3%, the mAP50 of segmentation increased by 0.7%, and mAP50–95 increased by 0.8%. The linear regression coefficient between manual measurement and machine-vision measurement was 0.94218, and the goodness of fit R2 was 0.94408. Using this model and the monitoring system, the morphological evolution of the licorice radicle contour characteristics over the germination time was obtained. The study indicated that the growth of licorice radicles was optimal under salt stress of 1200 µs/cm and 1800 µs/cm. YOLOv11-LicoSeg accurately segmented licorice radicles and calculated radicle length, with the performance to segment 100 licorice radicle images within 7 s. After deployment, it significantly reduced the labor cost and time consumption for acquiring licorice radicle phenotypes. In conclusion, YOLOv11-LicoSeg provides a rapid and accurate method for variety screening in licorice breeding and cultivation. Full article
(This article belongs to the Special Issue Sensors and Computer Vision for Quality Assessment of Agricultural Products)
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26 pages, 3199 KB  
Article
XGBoost Ensemble Algorithm for Classifying Tomato Leaf Diseases Based on Texture Descriptors
by Alpamis Kutlimuratov, Baxodir Achilov, Kuanishbay Seitnazarov, Piratdin Allayarov, Islambek Saymanov, Rashid Oteniyazov and Jamshid Khamzaev
AgriEngineering 2026, 8(3), 98; https://doi.org/10.3390/agriengineering8030098 - 5 Mar 2026
Cited by 1 | Viewed by 865
Abstract
This article presents a simple and understandable approach to the automatic assessment of the severity of late blight on tomato leaves. We collect our own dataset of 5245 RGB images of healthy and diseased tomato leaves and determine five ordinal classes: healthy (0%) [...] Read more.
This article presents a simple and understandable approach to the automatic assessment of the severity of late blight on tomato leaves. We collect our own dataset of 5245 RGB images of healthy and diseased tomato leaves and determine five ordinal classes: healthy (0%) and four infection levels (0.1–10%, 11–25%, 26–50%, and ≥51% of the affected area). Each image is segmented using the global definition of the Otsu threshold, followed by morphological purification, after which seven textural and geometric characteristics are extracted from the contours of the lesion: contrast, number of contours, average and standard deviation of the contour area, average and standard deviation of the contour perimeter, and average area-to-perimeter ratio. All characteristics are normalized and used as input data for the XGBoost classifier. The dataset is randomly split into 80% training and 20% test images, resulting in an independent test set of 1049 images. In this test set, the proposed model provides an overall accuracy of 0.93 and an F1 macro score of 0.93 points, while for each F1 class, it varies from 0.90 to 0.97. The confusion matrix shows a stable difference between neighboring severity levels, while the analysis of the importance of the features confirms the relevance of contour descriptors for characterizing the size and shape of the lesion. This method only runs on a central processor, requires a small amount of memory, and outputs interpretable output data, making it suitable for use in greenhouses and farms with limited computing resources. We also discuss the limitations associated with the boundaries between neighboring classes and the potential shift in the subject area, and we outline directions for expanding the approach to multi-sheet scenes and explicit ordinal loss functions. Full article
(This article belongs to the Special Issue Sensors and Computer Vision for Quality Assessment of Agricultural Products)
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