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

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

Deadline for manuscript submissions: 15 August 2026 | Viewed by 19053

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Special Issue Editors

Dr. Baohua Zhang

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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

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Guest Editor

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

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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

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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

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Keywords

artificial intelligence
neural networks
image analysis
qualitative classification

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Published Papers (12 papers)

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Research

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33 pages, 7834 KB

Open AccessArticle

Frequency-Domain Decoupling and Multi-Dimensional Spatial Feature Reconstruction for Occlusion-Aware Apple Detection in Complex Semi-Structured Orchard Environments

by Long Gao, Pengfei Wang, Lixing Liu, Hongjie Liu, Jianping Li and Xin Yang

Agronomy 2026, 16(8), 790; https://doi.org/10.3390/agronomy16080790 - 12 Apr 2026

Viewed by 514

Abstract

Apple detection is a core perception task for harvesting robots operating in complex orchard environments. Targets are frequently affected by branch–foliage occlusion, alternating front/side/back lighting, and strong local illumination fluctuations, which blur object boundaries against background textures and substantially increase detection difficulty. To improve target perception under these conditions, we propose an improved detector, YOLOv11-CBMES. First, based on YOLOv11, we replace the original neck with a weighted BiFPN to enhance cross-scale feature fusion under occlusion. Second, we introduce a Contrast-Driven Feature Aggregation (CDFA) module at the P5 stage, using Haar wavelet decomposition to decouple low-frequency illumination components from high-frequency structural components. Third, we reconstruct spatial feature learning and the upsampling pathway using CSP-based multi-scale blocks and efficient upsampling blocks, and embed a zero-parameter Shift-Context strategy to strengthen local neighbourhood interaction. Finally, we formulate apple detection as a three-class occlusion classification task (No Occlusion, Soft Occlusion, and Hard Occlusion) to support occlusion-aware target recognition. On the apple occlusion dataset, YOLOv11-CBMES achieves

{m A P}_{N O}

= 83.50%,

{m A P}_{S O}

= 67.36%, and

{m A P}_{H O}

= 51.90% at IoU = 0.5. Compared with YOLOv11n under the same training protocol, the gains are +2.16 pp (NO), +3.68 pp (SO), and +5.31 pp (HO), with the largest improvement observed in Hard Occlusion (HO). The results indicate that introducing frequency-domain structural processing into the detection framework improves apple occlusion classification and object detection performance, and provides a theoretical basis for designing perception modules for end-effector operations in apple harvesting robots. 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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30 pages, 8651 KB

Open AccessEditor’s ChoiceArticle

Disease-Seg: A Lightweight and Real-Time Segmentation Framework for Fruit Leaf Diseases

by Liying Cao, Donghui Jiang, Yunxi Wang, Jiankun Cao, Zhihan Liu, Jiaru Li, Xiuli Si and Wen Du

Agronomy 2026, 16(3), 311; https://doi.org/10.3390/agronomy16030311 - 26 Jan 2026

Viewed by 906

Abstract

Accurate segmentation of fruit tree leaf diseases is critical for yield protection and precision crop management, yet it is challenging due to complex field conditions, irregular leaf morphology, and diverse lesion patterns. To address these issues, Disease-Seg, a lightweight real-time segmentation framework, is proposed. It integrates CNN and Transformer with a parallel fusion architecture to capture local texture and global semantic context. The Extended Feature Module (EFM) enlarges the receptive field while retaining fine details. A Deep Multi-scale Attention mechanism (DM-Attention) allocates channel weights across scales to reduce redundancy, and a Feature-weighted Fusion Module (FWFM) optimizes integration of heterogeneous feature maps, enhancing multi-scale representation. Experiments show that Disease-Seg achieves 90.32% mIoU and 99.52% accuracy, outperforming representative CNN, Transformer, and hybrid-based methods. Compared with HRNetV2, it improves mIoU by 6.87% and FPS by 31, while using only 4.78 M parameters. It maintains 69 FPS on 512 × 512 crops and requires approximately 49 ms per image on edge devices, demonstrating strong deployment feasibility. On two grape leaf diseases from the PlantVillage dataset, it achieves 91.19% mIoU, confirming robust generalization. These results indicate that Disease-Seg provides an accurate, efficient, and practical solution for fruit leaf disease segmentation, enabling real-time monitoring and smart agriculture applications. 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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22 pages, 20100 KB

Open AccessArticle

Real-Time Detection and Validation of a Target-Oriented Model for Spindle-Shaped Tree Trunks Leveraging Deep Learning

by Kang Zheng, Shuo Yang, Zhichong Wang, Hao Fu, Xiu Wang, Wei Zou, Changyuan Zhai and Liping Chen

Agronomy 2026, 16(2), 210; https://doi.org/10.3390/agronomy16020210 - 15 Jan 2026

Viewed by 567

Abstract

To enhance the automation and intelligence of trenching fertilization operations, this research proposes a real-time trunk detection model (Trunk-Seek) designed for spindle-shaped orchards. The model employs a customized data augmentation strategy and integrates the YOLO deep learning framework to effectively address visual challenges such as lighting variation, occlusion, and motion blur. Multiple object tracking algorithms were evaluated, and ByteTrack was selected for its superior performance in dynamic trunk tracking. In addition, a Positioning and Triggering Algorithm (PTA) was developed to enable precise localization and triggering for target-oriented fertilization. The system was deployed on an edge device, a test bench was established, and both laboratory and field experiments were conducted to validate its performance. Experimental results demonstrated that the detection model achieved an mAP50 of 98.9% and maintained a stable 32.53 FPS on the edge device, fulfilling real-time detection requirements. Test bench analysis revealed that variations in trunk diameter and operation speed affected triggering accuracy, with an average dynamic localization error of ±1.78 cm. An empirical model (T) was developed to describe the time-delay behavior associated with positioning errors. Field verification in orchards confirmed that Trunk-Seek achieved a triggering accuracy of 91.08%, representing a 24.08% improvement over conventional training methods. Combining high accuracy with robust real-time performance, Trunk-Seek and the proposed PTA provide essential technical support for the development of a visual target-oriented fertilization system in modern orchards. 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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29 pages, 7854 KB

Open AccessArticle

Flower Thinning Strategy of Flat Peach Inflorescence Based on RBCN-YOLO

by Yongchuang Xiong, Benxue Ma, Yanxing Chen, Ying Xu and Jincheng Chen

Agronomy 2025, 15(12), 2715; https://doi.org/10.3390/agronomy15122715 - 25 Nov 2025

Viewed by 717

Abstract

Accurately identifying the morphology and spatial distribution of flat peach inflorescence is crucial for guiding precise flower thinning operations. In this study, based on the YOLOv8 framework, a flat peach inflorescence detection model (RBCN-YOLO) was developed for detecting all growth stages, from bud to initial flowering and full flowering. The model optimized the neck network architecture by incorporating RepBlock and BiFusion modules, integrating the CAFM module into the backbone network, and combining the NWD loss function with the CIoU loss function. The improved model showed better detection performance in remote viewing angles, backlight conditions, and complex scenarios. Moreover, it demonstrated good real-time performance on edge devices. Based on this model, a flower thinning strategy was designed by combining the density classification algorithm, inflorescence membership categorization, and interval flower-thinning requirements. The results showed that the RBCN-YOLO model achieved a mAP@0.5 of 82.9% and an F1 score of 78.9%. These scores represented improvements of 3.0% and 2.4%, respectively, compared to YOLOv8. Notably, the model performance in the initial flowering stage showed the most significant improvement, with the mAP@0.5 increasing from 65.1% to 70.7%. Additionally, the flower thinning strategy based on RBCN-YOLO achieved a flower thinning ratio of 54.55%, with a thinning accuracy of 78.84%. To further enhance the application of the research, a visualization system with integrated object detection and flower thinning functions was designed. This study provides a valuable reference for flower-thinning operations in flat peach orchards. 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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24 pages, 4427 KB

Open AccessEditor’s ChoiceArticle

Three-Dimensional Convolutional Neural Networks (3D-CNN) in the Classification of Varieties and Quality Assessment of Soybean Seeds (Glycine max L. Merrill)

by Piotr Rybacki, Kiril Bahcevandziev, Diego Jarquin, Ireneusz Kowalik, Andrzej Osuch, Ewa Osuch and Janetta Niemann

Agronomy 2025, 15(9), 2074; https://doi.org/10.3390/agronomy15092074 - 28 Aug 2025

Cited by 1 | Viewed by 1996

Abstract

The precise identification, classification, sorting, and rapid and accurate quality assessment of soybean seeds are extremely important in terms of the continuity of agricultural production, varietal purity, seed processing, protein extraction, and food safety. Currently, commonly used methods for the identification and quality assessment of soybean seeds include morphological analysis, chemical analysis, protein electrophoresis, liquid chromatography, spectral analysis, and image analysis. The use of image analysis and artificial intelligence is the aim of the presented research, in which a method for the automatic classification of soybean varieties, the assessment of the degree of damage, and the identification of geometric features of soybean seeds based on numerical models obtained using a 3D scanner has been proposed. Unlike traditional two-dimensional images, which only represent height and width, 3D imaging adds a third dimension, allowing for a more realistic representation of the shape of the seeds. The research was conducted on soybean seeds with a moisture content of 13%, and the seeds were stored in a room with a temperature of 20–23 °C and air humidity of 60%. Individual soybean seeds were scanned to create 3D models, allowing for the measurement of their geometric parameters, assessment of texture, evaluation of damage, and identification of characteristic varietal features. The developed 3D-CNN network model comprised an architecture consisting of an input layer, three hidden layers, and one output layer with a single neuron. The aim of the conducted research is to design a new, three-dimensional 3D-CNN architecture, the main task of which is the classification of soybean seeds. For the purposes of network analysis and testing, 22 input criteria were defined, with a hierarchy of their importance. The training, testing, and validation database of the SB3D-NET network consisted of 3D models obtained as a result of scanning individual soybean seeds, 100 for each variety. The accuracy of the training process of the proposed SB3D-NET model for the qualitative classification of 3D models of soybean seeds, based on the adopted criteria, was 95.54%, and the accuracy of its validation was 90.74%. The relative loss value during the training process of the SB3D-NET model was 18.53%, and during its validation process, it was 37.76%. The proposed SB3D-NET neural network model for all twenty-two criteria achieves values of global error (GE) of prediction and classification of seeds at the level of 0.0992. 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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23 pages, 6358 KB

Open AccessArticle

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 1014

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 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 KB

Open AccessArticle

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

Cited by 5 | Viewed by 1874

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 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 KB

Open AccessArticle

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

Cited by 2 | Viewed by 2406

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 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 KB

Open AccessEditor’s ChoiceArticle

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

Cited by 2 | Viewed by 1326

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 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 KB

Open AccessArticle

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 3 | Viewed by 2320

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 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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33 pages, 5104 KB

Open AccessReview

Precision Agriculture Through a Real-Time Systems Perspective: A Narrative Review

by Mansub Haseeb Bhat, Rickiel Franklin da Silva, Sameer Bhat, Aeshna Sinha and Kenneth J. Moore

Agronomy 2026, 16(5), 552; https://doi.org/10.3390/agronomy16050552 - 28 Feb 2026

Viewed by 1142

Abstract

Precision agriculture employs state-of-the-art technologies to improve the economic viability, sustainability, and efficiency of agricultural practices. This paper offers a thorough review of precision agriculture, with an emphasis on real-time systems as a foundation for understanding the integration and impact of major technologies. We examine technologies such as digital twins, mobile applications, autonomous systems, location-aware technologies, edge computing, and Wireless Sensor Networks (WSN) that are revolutionizing agricultural processes. We also discuss the potential of other sensing techniques to enhance precision farming, including image analysis, sensory and chemical analysis, and physical state detection. Additionally, the roles that data transmission protocols, artificial intelligence (AI), and machine learning play in maximizing real-time data processing and decision-making are examined. We emphasize the main challenges and limitations in precision agriculture, such as data interoperability, scalability, and system integration. With a focus on market trends and local issues, we examine how AI, real-time systems, sensor technologies, and financial constraints impact the growth of precision agriculture. These advancements have an impact on precise monitoring, post-harvest management, and human health. Lastly, we provide suggestions for successful integration and future developments in precision agriculture, emphasizing design, engineering, and creative approaches to assist the field’s ongoing development. 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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21 pages, 8602 KB

Open AccessReview

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

Cited by 2 | Viewed by 2860

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 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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