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AgriEngineering
Special Issues
Computer Vision for Smart Agriculture
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Computer Vision for Smart Agriculture

A Special Issue of AgriEngineering (ISSN 2624-7402) belonging to the section "Computer Applications and Artificial Intelligence in Agriculture".

Deadline for manuscript submissions: 26 March 2027 | Viewed by 1294

Editors

Dr. Shaojun Hu

E-Mail Website
Guest Editor
College of Information Engineering, Northwest A&F University, Xianyang 712100, China
Interests: image and graphics; human–robot interaction; smart agriculture
Prof. Dr. Huijun Yang

E-Mail Website
Guest Editor
College of Information Engineering, Northwest A&F University, Xianyang 712100, China
Interests: image and graphics; smart agriculture

Special Issue Information

Dear Colleagues,

This Special Issue invites submissions that showcase innovative research on computer vision technologies that are transforming agricultural engineering. We seek contributions that address key challenges in smart agriculture, including automated crop and disease monitoring, yield estimation, robotics for harvesting and weeding, livestock behavior analysis, and quality assessment of produce. Submissions should emphasize practical applications of computer vision, image processing, and sensor fusion to enhance automation in agricultural systems. We welcome studies on robust computer vision algorithms for uncontrolled field environments, edge computing solutions, and scalable systems that bridge the gap between experimental validation and real-world implementation, driving forward the future of intelligent, sustainable agriculture.

Topics include, but are not limited to, the following:

  • Image-based phenotyping and stress detection;
  • Computer vision for agricultural robotics and navigation;
  • UAV and satellite imagery analysis;
  • IoT and vision sensor networks for farm management;
  • Vision-based crop quality inspection and sorting;
  • Datasets and benchmarks for agricultural vision tasks.

Dr. Shaojun Hu
Prof. Dr. Huijun Yang
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
  • image processing
  • smart agriculture
  • phenotyping
  • vision sensors
  • robotics

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

Published Papers (2 papers)

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Research

24 pages, 19020 KB  
Article
C-MorphYOLO: A Circular Morphological Convolution Network for Robust Sichuan Pepper Maturity Detection in Natural Environments
by Chaoyi Zhang, Fei Pan, Ying Ruan, Yangtan Xiao, Chengkai Yu, Pengjun Xiang and Xuliang Duan
AgriEngineering 2026, 8(9), 362; https://doi.org/10.3390/agriengineering8090362 - 31 Aug 2026
Viewed by 164
Abstract
Reliable maturity detection of Sichuan pepper is critical for intelligent harvesting, precision agriculture, and automated quality assessment. However, existing vision-based methods often suffer from performance degradation in natural orchard conditions due to illumination variation, fruit occlusion, background interference, and substantial appearance diversity. In [...] Read more.
Reliable maturity detection of Sichuan pepper is critical for intelligent harvesting, precision agriculture, and automated quality assessment. However, existing vision-based methods often suffer from performance degradation in natural orchard conditions due to illumination variation, fruit occlusion, background interference, and substantial appearance diversity. In addition, the lack of dedicated Sichuan pepper maturity datasets limits the development and reliable evaluation of deep learning-based detection models. To address these challenges, this study proposes C-MorphYOLO (Circular-Morphological YO-LO), a morphology-aware detection framework for Sichuan pepper maturity recognition under field conditions. By incorporating circular morphological information into feature representation learning, the proposed framework enhances the perception of fruit structural characteristics and improves fine-grained maturity discrimination. Specifically, a Circular Depthwise Convolution Module (CDCM) is developed to strengthen boundary and shape-aware feature extraction, while a Feature Enhancement-based Upsampling Module (FEUM) and a Multi-Scale Adaptive Spatial Attention Gate (MASAG) are introduced to improve small-object representation and detection robustness. Furthermore, a natural-scene Sichuan pepper maturity dataset containing 2763 images and 2962 annotated instances was established for model training and comprehensive evaluation. Experimental results on the held-out validation set demonstrate that C-MorphYOLO achieves a Precision of 96.1% and an mAP50:95 of 89.1%, representing absolute improvements of 2.2% and 2.7%, respectively, compared with the YOLOv12s baseline. These results demonstrate that the proposed framework effectively improves maturity recognition and localization performance, providing a practical vision-based solution for intelligent Sichuan pepper harvesting and automated agricultural management. Full article
(This article belongs to the Special Issue Computer Vision for Smart Agriculture)
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28 pages, 2200 KB  
Article
Deep Learning Models for Defect Identification in Oryza sativa Rice Grains: A Comparative Study
by Yasiel Pérez Vera, Melissa Kristel Chambi Flores, Santiago Alonso Avilés Córdova, Irvin Estuardo Cazorla Macedo, Percy Aarón Luján Biamonte and Edgardo Alfredo Rivero Callohuanca
AgriEngineering 2026, 8(6), 252; https://doi.org/10.3390/agriengineering8060252 - 19 Jun 2026
Viewed by 626
Abstract
Manual classification of rice grain defects remains a persistent challenge in the Peruvian rice industry, as it relies heavily on human inspection, leading to variability, inconsistency, and reduced efficiency when processing large volumes of product. This study evaluates the effectiveness of transfer learning [...] Read more.
Manual classification of rice grain defects remains a persistent challenge in the Peruvian rice industry, as it relies heavily on human inspection, leading to variability, inconsistency, and reduced efficiency when processing large volumes of product. This study evaluates the effectiveness of transfer learning and convolutional neural networks (CNNs) for the automatic classification of four rice grain categories relevant to quality assessment: Whole, Stained, Broken, and Chalky. A dataset comprising 6599 RGB images was employed. To ensure a reliable evaluation protocol, the dataset was first partitioned into training (70%), validation (15%), and test (15%) subsets, after which data augmentation was independently applied within each partition to balance class distributions. Five pretrained CNN architectures were evaluated: MobileNetV2, EfficientNetB0, ResNet50, DenseNet121, and InceptionV3, all of which share a common classification head. Models were trained using transfer learning and early stopping based on validation loss. Performance was assessed using accuracy, precision, recall, F1-score, confusion matrices, 95% confidence intervals, and pairwise McNemar statistical tests. The results showed that ResNet50 achieved the highest classification accuracy (84.71%), followed by EfficientNetB0 (83.60%) and DenseNet121 (83.20%). Statistical analysis indicated that performance differences among the top-performing architectures were relatively small, with significant differences observed only for selected model pairs. Across all evaluated models, the discrimination between Whole and Chalky grains remained the most challenging classification task due to their high visual similarity. Overall, the findings demonstrate that transfer learning-based CNNs provide an effective and scalable approach for automated rice grain defect identification and quality assessment in agricultural environments. Full article
(This article belongs to the Special Issue Computer Vision for Smart Agriculture)
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