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Agronomy
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Intelligent Detection and Classification of External Traits in Crop Plants,...
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Intelligent Detection and Classification of External Traits in Crop Plants, Fruits, and Vegetables

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 5349

Special Issue Editors

Dr. Baohua Zhang

E-Mail Website
Guest Editor
College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210095, China
Interests: quality and safety assessment of agricultural products; harvesting robots; robot vision; robotic grasping; spectral analysis and modeling; robotic systems and their applications in agriculture
Special Issues, Collections and Topics in MDPI journals
Dr. Yuxing Han

E-Mail Website
Guest Editor
1. College of Artificial Intelligence, South China Agricultural University, Guangzhou 510642, China
2. Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen 518000, China
Interests: computer vision; deep learning; brain-inspired computing; edge computing; remote sensing; agricultural engineering; smart agriculture; precision agriculture; agricultural aviation; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Piotr Rybacki

E-Mail Website
Guest Editor
Department of Agronomy, Poznań University of Life Sciences, Dojazd 11, 60-632 Poznań, Poland
Interests: digital agriculture; agriculture 4.0; computer image analysis; digital classification of agricultural and horticultural products; remote sensing and telematics in agriculture and horticulture; precision technologies in agriculture and horticulture; autonomous robots and drones in agriculture; smart greenhouses; internet of things in agriculture
Prof. Dr. Janetta Niemann

E-Mail Website
Guest Editor
Department of Genetics and Plant Breeding, Poznań University of Life Sciences, Dojazd 11, 60-632 Poznań, Poland
Interests: agricultural biotechnology; plant genetics; 3D plant phenotyping; resistance breeding; application of molecular tools and convolutional neural network for variety classification and seed quality assessment

Special Issue Information

Dear Colleagues,

A technological revolution is currently taking place in agriculture, termed 'Agriculture 4.0'. Modern, intelligent solutions are being introduced, mainly based on the digitalization of production processes and the classification and qualitative assessment of agricultural crops, fruit, and vegetables. The basis of these intelligent solutions is artificial intelligence (AI), which allows for the modeling, simulation, and prediction of complex agricultural processes, especially in the case of complex relationships between variables related to weather and agrotechnical conditions. Intelligent solutions are extremely helpful for extracting quality characteristics of agricultural products based on shape, color, texture, and light spectrum. Digital techniques and methods provide new knowledge that can be applied to control the quality of food and agricultural products with high accuracy. Texture, shape, and color characteristics of agricultural products are used to detect damaged apple or orange areas, weeds, and pests. Computer image analysis has become one of the main techniques used in agriculture to assess seeds and grains in terms of quality losses, quantifying their degree of mechanical damage, maturity stage, disease infestation, or contamination with other plant species.

In this Special Issue, we aim to exchange knowledge on precision agriculture, the use of computer systems in agricultural production, the application of artificial neural networks and image analysis for qualitative and quantitative classification of field crops, vegetables, and fruit, and the use of genetic algorithms to manage machinery and evaluate its efficiency.

Dr. Baohua Zhang
Dr. Yuxing Han
Prof. Dr. Piotr Rybacki
Prof. Dr. Janetta Niemann
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. Agronomy 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 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

  • artificial intelligence
  • neural networks
  • image analysis
  • qualitative classification

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Published Papers (6 papers)

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Research

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23 pages, 6358 KiB  
Article
Optimization of Sorghum Spike Recognition Algorithm and Yield Estimation
by Mengyao Han, Jian Gao, Cuiqing Wu, Qingliang Cui, Xiangyang Yuan and Shujin Qiu
Agronomy 2025, 15(7), 1526; https://doi.org/10.3390/agronomy15071526 - 23 Jun 2025
Viewed by 305
Abstract
In the natural field environment, the high planting density of sorghum and severe occlusion among spikes substantially increases the difficulty of sorghum spike recognition, resulting in frequent false positives and false negatives. The target detection model suitable for this environment requires high computational [...] Read more.
In the natural field environment, the high planting density of sorghum and severe occlusion among spikes substantially increases the difficulty of sorghum spike recognition, resulting in frequent false positives and false negatives. The target detection model suitable for this environment requires high computational power, and it is difficult to realize real-time detection of sorghum spikes on mobile devices. This study proposes a detection-tracking scheme based on improved YOLOv8s-GOLD-LSKA with optimized DeepSort, aiming to enhance yield estimation accuracy in complex agricultural field scenarios. By integrating the GOLD module’s dual-branch multi-scale feature fusion and the LSKA attention mechanism, a lightweight detection model is developed. The improved DeepSort algorithm enhances tracking robustness in occlusion scenarios by optimizing the confidence threshold filtering (0.46), frame-skipping count, and cascading matching strategy (n = 3, max_age = 40). Combined with the five-point sampling method, the average dry weight of sorghum spikes (0.12 kg) was used to enable rapid yield estimation. The results demonstrate that the improved model achieved a mAP of 85.86% (a 6.63% increase over the original YOLOv8), an F1 score of 81.19%, and a model size reduced to 7.48 MB, with a detection speed of 0.0168 s per frame. The optimized tracking system attained a MOTA of 67.96% and ran at 42 FPS. Image- and video-based yield estimation accuracies reached 89–96% and 75–93%, respectively, with single-frame latency as low as 0.047 s. By optimizing the full detection–tracking–yield pipeline, this solution overcomes challenges in small object missed detections, ID switches under occlusion, and real-time processing in complex scenarios. Its lightweight, high-efficiency design is well suited for deployment on UAVs and mobile terminals, providing robust technical support for intelligent sorghum monitoring and precision agriculture management, and thereby playing a crucial role in driving agricultural digital transformation. Full article
(This article belongs to the Special Issue Intelligent Detection and Classification of External Traits in Crop Plants, Fruits, and Vegetables)
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26 pages, 11251 KiB  
Article
Design and Testing of a Four-Arm Multi-Joint Apple Harvesting Robot Based on Singularity Analysis
by Xiaojie Lei, Jizhan Liu, Houkang Jiang, Baocheng Xu, Yucheng Jin and Jianan Gao
Agronomy 2025, 15(6), 1446; https://doi.org/10.3390/agronomy15061446 - 13 Jun 2025
Viewed by 505
Abstract
The use of multi-joint arms in a high-spindle environment can solve complex problems, but the singularity problem of the manipulator related to the structure of the serial manipulator is prominent. Therefore, based on the general mathematical model of fruit spatial distribution in high-spindle [...] Read more.
The use of multi-joint arms in a high-spindle environment can solve complex problems, but the singularity problem of the manipulator related to the structure of the serial manipulator is prominent. Therefore, based on the general mathematical model of fruit spatial distribution in high-spindle apple orchards, this study proposes two harvesting system architecture schemes that can meet the constraints of fruit spatial distribution and reduce the singularity of harvesting robot operation, which are four-arm dual-module independent moving scheme (Scheme A) and four-arm single-module parallel moving scheme (Scheme B). Based on the link-joint method, the analytical expression of the singular configuration of the redundant degree of freedom arm group system under the two schemes is obtained. Then, the inverse kinematics solution method of the redundant arm group and the singularity avoidance picking trajectory planning strategy are proposed to realize the judgment and solution of the singular configuration in the complex working environment of the high-spindle. The singularity rate of Scheme A in the simulation environment is 17.098%, and the singularity rate of Scheme B is only 6.74%. In the field experiment, the singularity rate of Scheme A is 26.18%, while the singularity rate of Scheme B is 13.22%. The success rate of Schemes A and B are 80.49% and 72.33%, respectively. Through experimental comparison and analysis, Scheme B is more prominent in solving singular problems but still needs to improve the success rate in future research. This paper can provide a reference for solving the singular problems in the complex working environment of high spindles. Full article
(This article belongs to the Special Issue Intelligent Detection and Classification of External Traits in Crop Plants, Fruits, and Vegetables)
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26 pages, 10969 KiB  
Article
TQVGModel: Tomato Quality Visual Grading and Instance Segmentation Deep Learning Model for Complex Scenarios
by Peichao Cong, Kun Wang, Ji Liang, Yutao Xu, Tianheng Li and Bin Xue
Agronomy 2025, 15(6), 1273; https://doi.org/10.3390/agronomy15061273 - 22 May 2025
Viewed by 544
Abstract
To address the challenges of poor instance segmentation accuracy, real-time performance trade-offs, high miss rates, and imprecise edge localization in tomato grading and harvesting robots operating in complex scenarios (e.g., dense growth, occluded fruits, and dynamic viewing conditions), an accurate, efficient, and robust [...] Read more.
To address the challenges of poor instance segmentation accuracy, real-time performance trade-offs, high miss rates, and imprecise edge localization in tomato grading and harvesting robots operating in complex scenarios (e.g., dense growth, occluded fruits, and dynamic viewing conditions), an accurate, efficient, and robust visual instance segmentation network is urgently needed. This paper proposes TQVGModel (Tomato Quality Visual Grading Model), a Mask RCNN-based instance segmentation network for tomato quality grading. First, TQVGModel employs a multi-branch IncepConvV2 backbone, reconstructed via ConvNeXt architecture and large-kernel convolution decomposition, to enhance instance segmentation accuracy while maintaining real-time performance. Second, the Class Balanced Focal Loss is adopted in the classification branch to prioritize sparse or challenging classes, reducing the miss rates in complex scenes. Third, an Enhanced Sobel (E-Sobel) operator integrates boundary prediction with an edge loss function, improving edge localization precision for quality assessment. Additionally, a quality grading subsystem is designed to automate tomato evaluation, supporting subsequent harvesting and growth monitoring. A high-quality benchmark dataset, Tomato-Seg, is constructed for complex-scene tomato instance segmentation. Experiments show that the TQVGModel-Tiny variant achieves an 80.05% mAP (7.04% higher than Mask R-CNN), with 33.98 M parameters (10.2 M fewer) and 53.38 ms inference speed (16.6 ms faster). These results demonstrate TQVGModel’s high accuracy, real-time capability, reduced miss rates, and precise edge localization, providing a theoretical foundation for tomato grading and harvesting in complex environments. Full article
(This article belongs to the Special Issue Intelligent Detection and Classification of External Traits in Crop Plants, Fruits, and Vegetables)
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15 pages, 13649 KiB  
Article
Point Cloud Completion of Occluded Corn with a 3D Positional Gated Multilayer Perceptron and Prior Shape Encoder
by Yuliang Gao, Zhen Li, Tao Liu, Bin Li and Lifeng Zhang
Agronomy 2025, 15(5), 1155; https://doi.org/10.3390/agronomy15051155 - 9 May 2025
Viewed by 489
Abstract
To obtain the complete shape and pose of corn under occlusion, this study proposes a point cloud completion algorithm for completing the fragmented corn point cloud after segmentation. Considering that this work focuses on a single-class crop—corn—the proposals mainly focus on the deep [...] Read more.
To obtain the complete shape and pose of corn under occlusion, this study proposes a point cloud completion algorithm for completing the fragmented corn point cloud after segmentation. Considering that this work focuses on a single-class crop—corn—the proposals mainly focus on the deep learning model size and the completion of the overall shape of the corn. In this work, the 3D corn models derived from segmentation are employed to systematically output the fragmented point cloud data in batches. The Shape Coding PointAttN (SCPAN) algorithm is also proposed, which is based on PointAttN. The model’s structure is simplified to output sparse point clouds and minimize computational complexity, and a gated multilayer perceptron (MLP) containing 3D position coding is introduced to enhance the model’s spatial awareness. In addition, the prior shape encoder module is initially trained and subsequently integrated into the model to enhance its focus on shape characteristics. Compared to the original model, PointAttN, SCPAN achieves a 34.2% reduction in the number of parameters, and the inference time is reduced by 30 ms while maintaining comparable accuracy. The experimental results show that the proposed method can complete the corn point cloud more effectively, using a small model to help estimate the pose and dimensions of corn accurately. This work supports the precise phenotypic analysis of corn and similar crops, such as citrus and tomatoes, and promotes the development of smart agricultural technology. Full article
(This article belongs to the Special Issue Intelligent Detection and Classification of External Traits in Crop Plants, Fruits, and Vegetables)
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17 pages, 8318 KiB  
Article
Vegetable Fields Mapping in Northeast China Based on Phenological Features
by Jialin Hu, Huimin Lu, Kaishan Song and Bingxue Zhu
Agronomy 2025, 15(2), 307; https://doi.org/10.3390/agronomy15020307 - 26 Jan 2025
Cited by 2 | Viewed by 1095
Abstract
Developing vegetable agriculture is crucial for ensuring a balanced dietary structure and promoting nutritional health. However, remote sensing extraction in open-field vegetable planting areas faces several challenges, such as the mixing of target crops with natural vegetation caused by differences in climate conditions [...] Read more.
Developing vegetable agriculture is crucial for ensuring a balanced dietary structure and promoting nutritional health. However, remote sensing extraction in open-field vegetable planting areas faces several challenges, such as the mixing of target crops with natural vegetation caused by differences in climate conditions and planting practices, which hinders the development of large-scale vegetable field mapping. This paper proposes a classification method based on vegetable phenological characteristics (VPC), which takes into account the spatiotemporal heterogeneity of vegetable cultivation in Northeast China. We used a two-step strategy. First, Sentinel-2 satellite images and land use data were utilized to identify the optimal time and key indicators for vegetable detection based on the phenological differences in crop growth. Second, spectral analysis was integrated with three machine learning classifiers, which leveraged phenological and spectral features extracted from satellite images to accurately identify vegetable-growing areas. This combined approach enabled the generation of a high-precision vegetable planting map. The research findings reveal a consistent year-by-year increase in the planting area of vegetables from 2019 to 2023. The overall accuracy (OA) of the results ranges from 0.81 to 0.93, with a Kappa coefficient of 0.83. Notably, this is the first 10 m resolution regional vegetable map in China, marking a significant advancement in economic vegetable crop mapping. Full article
(This article belongs to the Special Issue Intelligent Detection and Classification of External Traits in Crop Plants, Fruits, and Vegetables)
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Review

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21 pages, 8602 KiB  
Review
From Outside to Inside: The Subtle Probing of Globular Fruits and Solanaceous Vegetables Using Machine Vision and Near-Infrared Methods
by Junhua Lu, Mei Zhang, Yongsong Hu, Wei Ma, Zhiwei Tian, Hongsen Liao, Jiawei Chen and Yuxin Yang
Agronomy 2024, 14(10), 2395; https://doi.org/10.3390/agronomy14102395 - 16 Oct 2024
Viewed by 1771
Abstract
Machine vision and near-infrared light technology are widely used in fruits and vegetable grading, as an important means of agricultural non-destructive testing. The characteristics of fruits and vegetables can easily be automatically distinguished by these two technologies, such as appearance, shape, color and [...] Read more.
Machine vision and near-infrared light technology are widely used in fruits and vegetable grading, as an important means of agricultural non-destructive testing. The characteristics of fruits and vegetables can easily be automatically distinguished by these two technologies, such as appearance, shape, color and texture. Nondestructive testing is reasonably used for image processing and pattern recognition, and can meet the identification and grading of single features and fusion features in production. Through the summary and analysis of the fruits and vegetable grading technology in the past five years, the results show that the accuracy of machine vision for fruits and vegetable size grading is 70–99.8%, the accuracy of external defect grading is 88–95%, and the accuracy of NIR and hyperspectral internal detection grading is 80.56–100%. Comprehensive research on multi-feature fusion technology in the future can provide comprehensive guidance for the construction of automatic integrated grading of fruits and vegetables, which is the main research direction of fruits and vegetable grading in the future. Full article
(This article belongs to the Special Issue Intelligent Detection and Classification of External Traits in Crop Plants, Fruits, and Vegetables)
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