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Agronomy
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Computer Vision and Deep Learning Technology in Agriculture
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Computer Vision and Deep Learning Technology in Agriculture

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

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 21698

Special Issue Editor

Dr. Juncheng Ma

E-Mail Website
Guest Editor
College of Water Resources and Civil Engineering, China Agricultural University, Beijing, China
Interests: crop traits estimation; crop growth monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Agriculture is complex and hard to predict. Computer vision offers us opportunities to better understand agriculture by processing data in a visible manner. Deep learning, which belongs to the machine learning field, is a modern technique for image processing and data analysis, with promising results and great potential. Recent advances in deep learning have significantly promoted computer vision applications in agriculture, providing solutions to many long-lasting challenges.

This Special Issue of Agronomy aims to provide coverage of advances in the development and application of computer vision, machine learning, and deep learning techniques for solving problems in agriculture. Papers on new techniques for processing high-resolution images collected with RGB, multispectral and hyperspectral sensors from the air (with UAVs, for instance) or from the ground are also welcome. We encourage you to share your research on state-of-the-art applications of computer vision and deep learning in agriculture and to submit your paper to this Special Issue.

Dr. Juncheng Ma
Guest Editor

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

  • precision agriculture
  • computer vision
  • deep learning
  • machine learning
  • image processing

Published Papers (15 papers)

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Research

20 pages, 10327 KiB  
Article
Improved Feature Fusion in YOLOv5 for Accurate Detection and Counting of Chinese Flowering Cabbage (Brassica campestris L. ssp. chinensis var. utilis Tsen et Lee) Buds
by Kai Yuan, Qian Wang, Yalong Mi, Yangfan Luo and Zuoxi Zhao
Agronomy 2024, 14(1), 42; https://doi.org/10.3390/agronomy14010042 - 22 Dec 2023
Viewed by 835
Abstract
Chinese flowering cabbage (Brassica campestris L. ssp. chinensis var. utilis Tsen et Lee) is an important leaf vegetable originating from southern China. Its planting area is expanding year by year. Accurately judging its maturity and determining the appropriate harvest time are crucial [...] Read more.
Chinese flowering cabbage (Brassica campestris L. ssp. chinensis var. utilis Tsen et Lee) is an important leaf vegetable originating from southern China. Its planting area is expanding year by year. Accurately judging its maturity and determining the appropriate harvest time are crucial for production. The open state of Chinese flowering cabbage buds serves as a crucial maturity indicator. To address the challenge of accurately identifying Chinese flowering cabbage buds, we introduced improvements to the feature fusion approach of the YOLOv5 (You Only Look Once version 5) algorithm, resulting in an innovative algorithm with a dynamically adjustable detection head, named FPNDyH-YOLOv5 (Feature Pyramid Network with Dynamic Head-You Only Look Once version 5). Firstly, a P2 detection layer was added to enhance the model’s detection ability of small objects. Secondly, the spatial-aware attention mechanism from DyHead (Dynamic Head) for feature fusion was added, enabling the adaptive fusion of semantic information across different scales. Furthermore, a center-region counting method based on the Bytetrack object tracking algorithm was devised for real-time quantification of various categories. The experimental results demonstrate that the improved model achieved a mean average precision ([email protected]) of 93.9%, representing a 2.5% improvement compared to the baseline model. The average precision (AP) for buds at different maturity levels was 96.1%, 86.9%, and 98.7%, respectively. When applying the trained model in conjunction with Bytetrack for video detection, the average counting accuracy, relative to manual counting, was 88.5%, with class-specific accuracies of 90.4%, 80.0%, and 95.1%. In conclusion, this method facilitates relatively accurate classification and counting of Chinese flowering cabbage buds in natural environments. Full article
(This article belongs to the Special Issue Computer Vision and Deep Learning Technology in Agriculture)
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24 pages, 5751 KiB  
Article
HMFN-FSL: Heterogeneous Metric Fusion Network-Based Few-Shot Learning for Crop Disease Recognition
by Wenbo Yan, Quan Feng, Sen Yang, Jianhua Zhang and Wanxia Yang
Agronomy 2023, 13(12), 2876; https://doi.org/10.3390/agronomy13122876 - 23 Nov 2023
Viewed by 823
Abstract
The high performance of deep learning networks relies mainly on massive data. However, collecting enough samples of crop disease is impractical, which significantly limits the intelligent diagnosis of diseases. In this study, we propose Heterogeneous Metric Fusion Network-based Few-Shot Learning (HMFN-FSL), which aims [...] Read more.
The high performance of deep learning networks relies mainly on massive data. However, collecting enough samples of crop disease is impractical, which significantly limits the intelligent diagnosis of diseases. In this study, we propose Heterogeneous Metric Fusion Network-based Few-Shot Learning (HMFN-FSL), which aims to recognize crop diseases with unseen categories using only a small number of labeled samples. Specifically, CBAM (Convolutional Block Attention Module) was embedded in the feature encoders to improve the feature representation capability. Second, an improved few-shot learning network, namely HMFN-FSL, was built by fusing three metric networks (Prototypical Network, Matching Network, and DeepEMD (Differentiable Earth Mover’s Distance)) under the framework of meta-learning, which solves the problem of the insufficient accuracy of a single metric model. Finally, pre-training and meta-training strategies were optimized to improve the ability to generalize to new tasks in meta-testing. In this study, two datasets named Plantvillage and Field-PV (covering 38 categories of 14 crops and containing 50,403 and 665 images, respectively) are used for extensive comparison and ablation experiments. The results show that the HMFN-FSL proposed in this study outperforms the original metric networks and other state-of-the-art FSL methods. HMFN-FSL achieves 91.21% and 98.29% accuracy for crop disease recognition on 5way-1shot, 5way-5shot tasks on the Plantvillage dataset. The accuracy is improved by 14.86% and 3.96%, respectively, compared to the state-of-the-art method (DeepEMD) in past work. Furthermore, HMFN-FSL was still robust on the field scenes dataset (Field-PV), with average recognition accuracies of 73.80% and 85.86% on 5way-1shot, 5way-5shot tasks, respectively. In addition, domain variation and fine granularity directly affect the performance of the model. In conclusion, the few-shot method proposed in this study for crop disease recognition not only has superior performance in laboratory scenes but is also still effective in field scenes. Our results outperform the existing related works. This study provided technical references for subsequent few-shot disease recognition in complex environments in field environments. Full article
(This article belongs to the Special Issue Computer Vision and Deep Learning Technology in Agriculture)
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14 pages, 1950 KiB  
Article
Semantic Segmentation of Portuguese Agri-Forestry Using High-Resolution Orthophotos
by Tiago G. Morais, Tiago Domingos and Ricardo F. M. Teixeira
Agronomy 2023, 13(11), 2741; https://doi.org/10.3390/agronomy13112741 - 30 Oct 2023
Viewed by 1581
Abstract
The Montado ecosystem is an important agri-forestry system in Portugal, occupying about 8% of the total area of the country. However, this biodiverse ecosystem is threatened due to factors such as shrub encroachment. In this context, the development of tools for characterizing and [...] Read more.
The Montado ecosystem is an important agri-forestry system in Portugal, occupying about 8% of the total area of the country. However, this biodiverse ecosystem is threatened due to factors such as shrub encroachment. In this context, the development of tools for characterizing and monitoring Montado areas is crucial for their conservation. In this study, we developed a deep convolutional neural network algorithm based on the U-net architecture to identify regions with trees, shrubs, grass, bare soil, or other areas in Montado areas using high-resolution RGB and near-infrared orthophotos (with a spatial resolution of 25 cm) from seven experimental sites in the Alentejo region of Portugal (six used for training/validation and one for testing). To optimize the model’s performance, we performed hyperparameter tuning, which included adjusting the number of filters, dropout rate, and batch size. The best model achieved an overall classification performance of 0.88 and a mean intersection of the union of 0.81 on the test set, indicating high accuracy and reliability of the model in identifying and delineating land cover classes in the Montado ecosystem. The developed model is a powerful tool for identifying the status of the Montado ecosystem regarding shrub encroachment and facilitating better future management. Full article
(This article belongs to the Special Issue Computer Vision and Deep Learning Technology in Agriculture)
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16 pages, 4480 KiB  
Article
Win-Former: Window-Based Transformer for Maize Plant Point Cloud Semantic Segmentation
by Yu Sun, Xindong Guo and Hua Yang
Agronomy 2023, 13(11), 2723; https://doi.org/10.3390/agronomy13112723 - 29 Oct 2023
Cited by 1 | Viewed by 881
Abstract
Semantic segmentation of plant point clouds is essential for high-throughput phenotyping systems, while existing methods still struggle to balance efficiency and performance. Recently, the Transformer architecture has revolutionized the area of computer vision, and has potential for processing 3D point clouds. Applying the [...] Read more.
Semantic segmentation of plant point clouds is essential for high-throughput phenotyping systems, while existing methods still struggle to balance efficiency and performance. Recently, the Transformer architecture has revolutionized the area of computer vision, and has potential for processing 3D point clouds. Applying the Transformer for semantic segmentation of 3D plant point clouds remains a challenge. To this end, we propose a novel window-based Transformer (Win-Former) network for maize 3D organic segmentation. First, we pre-processed the Pheno4D maize point cloud dataset for training. The maize points were then projected onto a sphere surface, and a window partition mechanism was proposed to construct windows into which points were distributed evenly. After that, we employed local self-attention within windows for computing the relationship of points. To strengthen the windows’ connection, we introduced a Cross-Window self-attention (C-SA) module to gather the cross-window features by moving entire windows along the sphere. The results demonstrate that Win-Former outperforms the famous networks and obtains 83.45% mIoU with the lowest latency of 31 s on maize organ segmentation. We perform extensive experiments on ShapeNet to evaluate stability and robustness, and our proposed model achieves competitive results on part segmentation tasks. Thus, our Win-Former model effectively and efficiently segments the maize point cloud and provides technical support for automated plant phenotyping analysis. Full article
(This article belongs to the Special Issue Computer Vision and Deep Learning Technology in Agriculture)
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20 pages, 67319 KiB  
Article
Multi-Plant Disease Identification Based on Lightweight ResNet18 Model
by Li Ma, Yuanhui Hu, Yao Meng, Zhiyi Li and Guifen Chen
Agronomy 2023, 13(11), 2702; https://doi.org/10.3390/agronomy13112702 - 27 Oct 2023
Cited by 1 | Viewed by 1174
Abstract
Deep-learning-based methods for plant disease recognition pose challenges due to their high number of network parameters, extensive computational requirements, and overall complexity. To address this issue, we propose an improved residual-network-based multi-plant disease recognition method that combines the characteristics of plant diseases. Our [...] Read more.
Deep-learning-based methods for plant disease recognition pose challenges due to their high number of network parameters, extensive computational requirements, and overall complexity. To address this issue, we propose an improved residual-network-based multi-plant disease recognition method that combines the characteristics of plant diseases. Our approach introduces a lightweight technique called maximum grouping convolution to the ResNet18 model. We made three enhancements to adapt this method to the characteristics of plant diseases and ultimately reduced the convolution kernel requirements, resulting in the final model, Model_Lite. The experimental dataset comprises 20 types of plant diseases, including 13 selected from the publicly available Plant Village dataset and seven self-constructed images of apple leaves with complex backgrounds containing disease symptoms. The experimental results demonstrated that our improved network model, Model_Lite, contains only about 1/344th of the parameters and requires 1/35th of the computational effort compared to the original ResNet18 model, with a marginal decrease in the average accuracy of only 0.34%. Comparing Model_Lite with MobileNet, ShuffleNet, SqueezeNet, and GhostNet, our proposed Model_Lite model achieved a superior average recognition accuracy while maintaining a much smaller number of parameters and computational requirements than the above models. Thus, the Model_Lite model holds significant potential for widespread application in plant disease recognition and can serve as a valuable reference for future research on lightweight network model design. Full article
(This article belongs to the Special Issue Computer Vision and Deep Learning Technology in Agriculture)
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13 pages, 3622 KiB  
Article
Improving Lettuce Fresh Weight Estimation Accuracy through RGB-D Fusion
by Dan Xu, Jingjing Chen, Ba Li and Juncheng Ma
Agronomy 2023, 13(10), 2617; https://doi.org/10.3390/agronomy13102617 - 14 Oct 2023
Cited by 2 | Viewed by 1095
Abstract
Computer vision provides a real-time, non-destructive, and indirect way of horticultural crop yield estimation. Deep learning helps improve horticultural crop yield estimation accuracy. However, the accuracy of current estimation models based on RGB (red, green, blue) images does not meet the standard of [...] Read more.
Computer vision provides a real-time, non-destructive, and indirect way of horticultural crop yield estimation. Deep learning helps improve horticultural crop yield estimation accuracy. However, the accuracy of current estimation models based on RGB (red, green, blue) images does not meet the standard of a soft sensor. Through enriching more data and improving the RGB estimation model structure of convolutional neural networks (CNNs), this paper increased the coefficient of determination (R2) by 0.0284 and decreased the normalized root mean squared error (NRMSE) by 0.0575. After introducing a novel loss function mean squared percentage error (MSPE) that emphasizes the mean absolute percentage error (MAPE), the MAPE decreased by 7.58%. This paper develops a lettuce fresh weight estimation method through the multi-modal fusion of RGB and depth (RGB-D) images. With the multimodal fusion based on calibrated RGB and depth images, R2 increased by 0.0221, NRMSE decreased by 0.0427, and MAPE decreased by 3.99%. With the novel loss function, MAPE further decreased by 1.27%. A MAPE of 8.47% helps to develop a soft sensor for lettuce fresh weight estimation. Full article
(This article belongs to the Special Issue Computer Vision and Deep Learning Technology in Agriculture)
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16 pages, 3440 KiB  
Article
Intelligent Detection of Lightweight “Yuluxiang” Pear in Non-Structural Environment Based on YOLO-GEW
by Rui Ren, Haixia Sun, Shujuan Zhang, Ning Wang, Xinyuan Lu, Jianping Jing, Mingming Xin and Tianyu Cui
Agronomy 2023, 13(9), 2418; https://doi.org/10.3390/agronomy13092418 - 20 Sep 2023
Cited by 4 | Viewed by 1501
Abstract
To detect quickly and accurately “Yuluxiang” pear fruits in non-structural environments, a lightweight YOLO-GEW detection model is proposed to address issues such as similar fruit color to leaves, fruit bagging, and complex environments. This model improves upon YOLOv8s by using GhostNet as its [...] Read more.
To detect quickly and accurately “Yuluxiang” pear fruits in non-structural environments, a lightweight YOLO-GEW detection model is proposed to address issues such as similar fruit color to leaves, fruit bagging, and complex environments. This model improves upon YOLOv8s by using GhostNet as its backbone for extracting features of the “Yuluxiang” pears. Additionally, an EMA attention mechanism was added before fusing each feature in the neck section to make the model focus more on the target information of “Yuluxiang” pear fruits, thereby improving target recognition ability and localization accuracy. Furthermore, the CIoU Loss was replaced with the WIoUv3 Loss as the loss function, which enhances the capability of bounding box fitting and improves model performance without increasing its size. Experimental results demonstrated that the enhanced YOLO-GEW achieves an F1 score of 84.47% and an AP of 88.83%, while only occupying 65.50% of the size of YOLOv8s. Compared to lightweight algorithms such as YOLOv8s, YOLOv7-Tiny, YOLOv6s, YOLOv5s, YOLOv4-Tiny, and YOLOv3-Tiny; there are improvements in AP by 2.32%, 1.51%, 2.95%, 2.06%, 2.92%, and 5.38% respectively. This improved model can efficiently detect “Yuluxiang” pears in non-structural environments in real-time and provides a theoretical basis for recognition systems used by picking robots. Full article
(This article belongs to the Special Issue Computer Vision and Deep Learning Technology in Agriculture)
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13 pages, 10628 KiB  
Article
Buckwheat Plant Height Estimation Based on Stereo Vision and a Regression Convolutional Neural Network under Field Conditions
by Jianlong Zhang, Wenwen Xing, Xuefeng Song, Yulong Cui, Wang Li and Decong Zheng
Agronomy 2023, 13(9), 2312; https://doi.org/10.3390/agronomy13092312 - 1 Sep 2023
Viewed by 884
Abstract
Buckwheat plant height is an important indicator for producers. Due to the decline in agricultural labor, the automatic and real-time acquisition of crop growth information will become a prominent issue for farms in the future. To address this problem, we focused on stereo [...] Read more.
Buckwheat plant height is an important indicator for producers. Due to the decline in agricultural labor, the automatic and real-time acquisition of crop growth information will become a prominent issue for farms in the future. To address this problem, we focused on stereo vision and a regression convolutional neural network (CNN) in order to estimate buckwheat plant height. MobileNet V3 Small, NasNet Mobile, RegNet Y002, EfficientNet V2 B0, MobileNet V3 Large, NasNet Large, RegNet Y008, and EfficientNet V2 L were modified into regression CNNs. Through a five-fold cross-validation of the modeling data, the modified RegNet Y008 was selected as the optimal estimation model. Based on the depth and contour information of buckwheat depth image, the mean absolute error (MAE), root mean square error (RMSE), mean square error (MSE), and mean relative error (MRE) when estimating plant height were 0.56 cm, 0.73 cm, 0.54 cm, and 1.7%, respectively. The coefficient of determination (R2) value between the estimated and measured results was 0.9994. Combined with the LabVIEW software development platform, this method can estimate buckwheat accurately, quickly, and automatically. This work contributes to the automatic management of farms. Full article
(This article belongs to the Special Issue Computer Vision and Deep Learning Technology in Agriculture)
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19 pages, 7429 KiB  
Article
YOLOv5-AC: A Method of Uncrewed Rice Transplanter Working Quality Detection
by Yue Wang, Qiang Fu, Zheng Ma, Xin Tian, Zeguang Ji, Wangshu Yuan, Qingming Kong, Rui Gao and Zhongbin Su
Agronomy 2023, 13(9), 2279; https://doi.org/10.3390/agronomy13092279 - 29 Aug 2023
Viewed by 1056
Abstract
With the development and progress of uncrewed farming technology, uncrewed rice transplanters have gradually become an indispensable part of modern agricultural production; however, in the actual production, the working quality of uncrewed rice transplanters have not been effectively detected. In order to solve [...] Read more.
With the development and progress of uncrewed farming technology, uncrewed rice transplanters have gradually become an indispensable part of modern agricultural production; however, in the actual production, the working quality of uncrewed rice transplanters have not been effectively detected. In order to solve this problem, a detection method of uncrewed transplanter omission is proposed in this paper. In this study, the RGB images collected in the field were inputted into a convolutional neural network, and the bounding box center of the network output was used as the approximate coordinates of the rice seedlings, and the horizontal and vertical crop rows were fitted by the least square method, so as to detect the phenomenon of rice omission. By adding atrous spatial pyramid pooling and a convolutional block attention module to YOLOv5, the problem of image distortion caused by scaling and cropping is effectively solved, and the recognition accuracy is improved. The accuracy of this method is 95.8%, which is 5.6% higher than that of other methods, and the F1-score is 93.39%, which is 4.66% higher than that of the original YOLOv5. Moreover, the network structure is simple and easy to train, with the average training time being 0.284 h, which can meet the requirements of detection accuracy and speed in actual production. This study provides an effective theoretical basis for the construction of an uncrewed agricultural machinery system. Full article
(This article belongs to the Special Issue Computer Vision and Deep Learning Technology in Agriculture)
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21 pages, 11785 KiB  
Article
CLT-YOLOX: Improved YOLOX Based on Cross-Layer Transformer for Object Detection Method Regarding Insect Pest
by Lijuan Zhang, Haibin Cui, Jiadong Sun, Zhiyi Li, Hao Wang and Dongming Li
Agronomy 2023, 13(8), 2091; https://doi.org/10.3390/agronomy13082091 - 9 Aug 2023
Viewed by 1066
Abstract
This paper presents an enhanced YOLOX-based algorithm for pest detection, adopting a nature-inspired approach for refining its methodology. To tackle the limited availability of image data pertaining to pests and diseases, the paper incorporates Mosaic and Mixup technologies for effective image preprocessing. Furthermore, [...] Read more.
This paper presents an enhanced YOLOX-based algorithm for pest detection, adopting a nature-inspired approach for refining its methodology. To tackle the limited availability of image data pertaining to pests and diseases, the paper incorporates Mosaic and Mixup technologies for effective image preprocessing. Furthermore, a novel training strategy is proposed to enhance the overall quality of the results. The existing architecture is enriched by integrating shallow information, while the CLT module is devised to facilitate cross-layer fusion and extract essential feature information. This advancement enables improved object detection across various scales. Additionally, the paper optimizes the original PFPN structure by eliminating the convolutional layer preceding upsampling, enhancing the C3 module, and integrating the convolutional attention model (CBAM) to identify salient regions within complex scenes. The performance of the proposed CLT-YOLOX model is extensively evaluated using the IP102 dataset, demonstrating its effectiveness. Notably, the model exhibits significant improvements compared to the original AP evaluation index, with an increase of 2.2% in average precision (mAP) and 1.8% in AP75. Furthermore, favorable results are achieved in the COCOmAP index, particularly in the APsmall category where there is a 2.2% improvement in performance. Full article
(This article belongs to the Special Issue Computer Vision and Deep Learning Technology in Agriculture)
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21 pages, 16211 KiB  
Article
RepC-MVSNet: A Reparameterized Self-Supervised 3D Reconstruction Algorithm for Wheat 3D Reconstruction
by Hui Liu, Cheng Xin, Mengzhen Lai, Hangfei He, Yongzhao Wang, Mantao Wang and Jun Li
Agronomy 2023, 13(8), 1975; https://doi.org/10.3390/agronomy13081975 - 26 Jul 2023
Cited by 1 | Viewed by 1206
Abstract
The application of 3D digital models to high-throughput plant phenotypic analysis is a research hotspot nowadays. Traditional methods, such as manual measurement and laser scanning, have high costs, and multi-view, unsupervised reconstruction methods are still blank in the field of crop research. It [...] Read more.
The application of 3D digital models to high-throughput plant phenotypic analysis is a research hotspot nowadays. Traditional methods, such as manual measurement and laser scanning, have high costs, and multi-view, unsupervised reconstruction methods are still blank in the field of crop research. It is challenging to obtain a high-quality 3D crop surface feature composition for 3D reconstruction. In this paper, we propose a wheat point cloud generation and 3D reconstruction method based on SfM and MVS using sequential wheat crop images. Firstly, the camera intrinsics and camera extrinsics of wheat were estimated using a structure-from-motion system with feature maps, which effectively solved the problem of camera point location design. Secondly, we proposed the ReC-MVSNet, which integrates the heavy parametric structure into the point cloud 3D reconstruction network, overcoming the difficulty of capturing complex features via the traditional MVS model. Through experiments, it was shown that this research method achieves non-invasive reconstruction of the 3D phenotypic structure of realistic objects, the accuracy of the proposed model was improved by nearly 43.3%, and the overall value was improved by nearly 14.3%, which provided a new idea for the development of virtual 3D digitization. Full article
(This article belongs to the Special Issue Computer Vision and Deep Learning Technology in Agriculture)
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17 pages, 141007 KiB  
Article
An Improved YOLOv5s-Based Agaricus bisporus Detection Algorithm
by Chao Chen, Feng Wang, Yuzhe Cai, Shanlin Yi and Baofeng Zhang
Agronomy 2023, 13(7), 1871; https://doi.org/10.3390/agronomy13071871 - 15 Jul 2023
Cited by 4 | Viewed by 1278
Abstract
This study aims to improve the Agaricus bisporus detection efficiency and performance of harvesting robots in the complex environment of the mushroom growing house. Based on deep learning networks, an improved YOLOv5s algorithm was proposed for accurate A. bisporus detection. First, A. bisporus [...] Read more.
This study aims to improve the Agaricus bisporus detection efficiency and performance of harvesting robots in the complex environment of the mushroom growing house. Based on deep learning networks, an improved YOLOv5s algorithm was proposed for accurate A. bisporus detection. First, A. bisporus images collected in situ from the mushroom growing house were preprocessed and augmented to construct a dataset containing 810 images, which were divided into the training and test sets in the ratio of 8:2. Then, by introducing the Convolutional Block Attention Module (CBAM) into the backbone network of YOLOv5s and adopting the Mosaic image augmentation technique in training, the detection accuracy and robustness of the algorithm were improved. The experimental results showed that the improved algorithm had a recognition accuracy of 98%, a single-image processing time of 18 ms, an A. bisporus center point locating error of 0.40%, and a diameter measuring error of 1.08%. Compared with YOLOv5s and YOLOv7, the YOLOv5s-CBAM has better performance in recognition accuracy, center positioning, and diameter measurement. Therefore, the proposed algorithm is capable of accurate A. bisporus detection in the complex environment of the mushroom growing house. Full article
(This article belongs to the Special Issue Computer Vision and Deep Learning Technology in Agriculture)
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24 pages, 6643 KiB  
Article
Lightweight Isotropic Convolutional Neural Network for Plant Disease Identification
by Wenfeng Feng, Qiushuang Song, Guoying Sun and Xin Zhang
Agronomy 2023, 13(7), 1849; https://doi.org/10.3390/agronomy13071849 - 13 Jul 2023
Viewed by 1087
Abstract
In today’s world, agricultural products are becoming increasingly scarce globally due to a variety of factors, and the early and accurate automatic identification of plant diseases can help ensure the stability and sustainability of agricultural production, improve the quality and safety of agricultural [...] Read more.
In today’s world, agricultural products are becoming increasingly scarce globally due to a variety of factors, and the early and accurate automatic identification of plant diseases can help ensure the stability and sustainability of agricultural production, improve the quality and safety of agricultural products, and help promote agricultural modernization and sustainable development. For this purpose, a lightweight deep isotropic convolutional neural network model, FoldNet, is designed for plant disease identification in this study. The model improves the architecture of residual neural networks by first folding the chain of the same blocks and then connecting these blocks with jump connections of different distances. Such a design allows the neural network to explore a larger receptive domain, enhancing its multiscale representation capability, increasing the direct propagation of information throughout the network, and improving the performance of the neural network. The FoldNet model achieved a recognition accuracy of 99.84% on the laboratory dataset PlantVillage using only 685k parameters and a recognition accuracy of 90.49% on the realistic scene dataset FGVC8 using only 516k parameters, which is competitive with other state-of-the-art models. In addition, as far as we know, our model is the first model that has fewer than 1M parameters while achieving state-of-the-art accuracy in plant disease identification. This proposal facilitates precision agriculture applications on mobile, low-end terminals. Full article
(This article belongs to the Special Issue Computer Vision and Deep Learning Technology in Agriculture)
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16 pages, 3014 KiB  
Article
Method for Segmentation of Banana Crown Based on Improved DeepLabv3+
by Junyu He, Jieli Duan, Zhou Yang, Junchen Ou, Xiangying Ou, Shiwei Yu, Mingkun Xie, Yukang Luo, Haojie Wang and Qiming Jiang
Agronomy 2023, 13(7), 1838; https://doi.org/10.3390/agronomy13071838 - 11 Jul 2023
Cited by 2 | Viewed by 1268
Abstract
As the banana industry develops, the demand for intelligent banana crown cutting is increasing. To achieve efficient crown cutting of bananas, accurate segmentation of the banana crown is crucial for the operation of a banana crown cutting device. In order to address the [...] Read more.
As the banana industry develops, the demand for intelligent banana crown cutting is increasing. To achieve efficient crown cutting of bananas, accurate segmentation of the banana crown is crucial for the operation of a banana crown cutting device. In order to address the existing challenges, this paper proposed a method for segmentation of banana crown based on improved DeepLabv3+. This method replaces the backbone network of the classical DeepLabv3+ model with MobilenetV2, reducing the number of parameters and training time, thereby achieving model lightweightness and enhancing model speed. Additionally, the Atrous Spatial Pyramid Pooling (ASPP) module is enhanced by incorporating the Shuffle Attention Mechanism and replacing the activation function with Meta-ACONC. This enhancement results in the creation of a new feature extraction module, called Banana-ASPP, which effectively handles high-level features. Furthermore, Multi-scale Channel Attention Module (MS-CAM) is introduced to the Decoder to improve the integration of features from multiple semantics and scales. According to experimental data, the proposed method has a Mean Intersection over Union (MIoU) of 85.75%, a Mean Pixel Accuracy (MPA) of 91.41%, parameters of 5.881 M and model speed of 61.05 f/s. Compared to the classical DeepLabv3+ network, the proposed model exhibits an improvement of 1.94% in MIoU and 1.21% in MPA, while reducing the number of parameters by 89.25% and increasing the model speed by 47.07 f/s. The proposed method enhanced banana crown segmentation accuracy while maintaining model lightweightness and speed. It also provided robust technical support for relevant parameters calculation of banana crown and control of banana crown cutting equipment. Full article
(This article belongs to the Special Issue Computer Vision and Deep Learning Technology in Agriculture)
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16 pages, 4110 KiB  
Article
Tomato Leaf Disease Identification Method Based on Improved YOLOX
by Wenbo Liu, Yongsen Zhai and Yu Xia
Agronomy 2023, 13(6), 1455; https://doi.org/10.3390/agronomy13061455 - 25 May 2023
Cited by 7 | Viewed by 2411
Abstract
In tomato leaf disease identification tasks, the high cost and consumption of deep learning-based recognition methods affect their deployment and application on embedded devices. In this study, an improved YOLOX-based tomato leaf disease identification method is designed. To address the issue of positive [...] Read more.
In tomato leaf disease identification tasks, the high cost and consumption of deep learning-based recognition methods affect their deployment and application on embedded devices. In this study, an improved YOLOX-based tomato leaf disease identification method is designed. To address the issue of positive and negative sample imbalance, the sample adaptive cross-entropy loss function (LBCE−β) is proposed as a confidence loss, and MobileNetV3 is employed instead of the YOLOX backbone for lightweight model feature extraction. By introducing CBAM (Convolutional Block Attention Module) between the YOLOX backbone and neck network, the model’s feature extraction performance is increased. CycleGAN is used to enhance the data of tomato disease leaf samples in the PlantVillage dataset, solving the issue of an imbalanced sample number. After data enhancement, simulation experiments and field tests revealed that the YOLOX’s accuracy improved by 1.27%, providing better detection of tomato leaf disease samples in complex environments. Compared with the original model, the improved YOLOX model occupies 35.34% less memory, model detection speed increases by 50.20%, and detection accuracy improves by 1.46%. The enhanced network model is quantized by TensorRT and works at 11.1 FPS on the Jetson Nano embedded device. This method can provide an efficient solution for the tomato leaf disease identification system. Full article
(This article belongs to the Special Issue Computer Vision and Deep Learning Technology in Agriculture)
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