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Xu L, Liu T, Xue Z, Song J, Yuan Y, Zhang Z, Chen Y. Unique Plant Resources and Distribution Patterns in the Valley Forest of the Irtysh River Basin. PLANTS (BASEL, SWITZERLAND) 2024; 13:1957. [PMID: 39065484 PMCID: PMC11281289 DOI: 10.3390/plants13141957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2024] [Revised: 07/14/2024] [Accepted: 07/15/2024] [Indexed: 07/28/2024]
Abstract
The river valley forests of the Irtysh River Basin are a germplasm bank of Salicaceae species and rare plant resources in China, and the distribution varies with the river and is highly distinctive. However, there is a dearth of systematic research on the characteristics of plant resources. In this study, a comprehensive investigation was conducted in the trunk stream and six tributaries with valley forest distribution in the Irtysh River Basin, and 244 quadrats were set up. The analysis focused on the composition of the flora and resource characteristics. The results reveal the following: (1) The valley forests of the Irtysh River Basin contain 256 species of plants belonging to 57 families and 178 genera, among which 19 species of trees, 23 species of shrubs, and 214 species of herbs were investigated. (2) Among the identified species, 226 (88.67%) were recognized as resource plants, with medicinal plants being the most abundant (176 species, 68.75% of the total). (3) The distribution patterns of trees, shrubs, and herbs of each resource type vary across rivers. Elevation drop, river length, and river distance all significantly affect the number of specie. This study elucidated the current status and distributional characteristics of plant resources in the valley forests of the Irtysh River Basin, which is essential for both biodiversity conservation and sustainable resource utilization.
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Affiliation(s)
- Ling Xu
- College of Life Science, Shihezi University, Shihezi 832003, China; (L.X.); (Z.X.); (J.S.); (Y.Y.); (Z.Z.); (Y.C.)
- Xinjiang Production and Construction Corps Key Laboratory of Oasis Town and Mountain-Basin System Ecology, Shihezi 832003, China
| | - Tong Liu
- College of Life Science, Shihezi University, Shihezi 832003, China; (L.X.); (Z.X.); (J.S.); (Y.Y.); (Z.Z.); (Y.C.)
- Xinjiang Production and Construction Corps Key Laboratory of Oasis Town and Mountain-Basin System Ecology, Shihezi 832003, China
| | - Zhifang Xue
- College of Life Science, Shihezi University, Shihezi 832003, China; (L.X.); (Z.X.); (J.S.); (Y.Y.); (Z.Z.); (Y.C.)
- Xinjiang Production and Construction Corps Key Laboratory of Oasis Town and Mountain-Basin System Ecology, Shihezi 832003, China
| | - Jihu Song
- College of Life Science, Shihezi University, Shihezi 832003, China; (L.X.); (Z.X.); (J.S.); (Y.Y.); (Z.Z.); (Y.C.)
- Xinjiang Production and Construction Corps Key Laboratory of Oasis Town and Mountain-Basin System Ecology, Shihezi 832003, China
| | - Ye Yuan
- College of Life Science, Shihezi University, Shihezi 832003, China; (L.X.); (Z.X.); (J.S.); (Y.Y.); (Z.Z.); (Y.C.)
- Xinjiang Production and Construction Corps Key Laboratory of Oasis Town and Mountain-Basin System Ecology, Shihezi 832003, China
| | - Zidong Zhang
- College of Life Science, Shihezi University, Shihezi 832003, China; (L.X.); (Z.X.); (J.S.); (Y.Y.); (Z.Z.); (Y.C.)
- Xinjiang Production and Construction Corps Key Laboratory of Oasis Town and Mountain-Basin System Ecology, Shihezi 832003, China
| | - Yongyu Chen
- College of Life Science, Shihezi University, Shihezi 832003, China; (L.X.); (Z.X.); (J.S.); (Y.Y.); (Z.Z.); (Y.C.)
- Xinjiang Production and Construction Corps Key Laboratory of Oasis Town and Mountain-Basin System Ecology, Shihezi 832003, China
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Agarla M, Napoletano P, Schettini R. Quasi Real-Time Apple Defect Segmentation Using Deep Learning. SENSORS (BASEL, SWITZERLAND) 2023; 23:7893. [PMID: 37765950 PMCID: PMC10537567 DOI: 10.3390/s23187893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 09/01/2023] [Accepted: 09/12/2023] [Indexed: 09/29/2023]
Abstract
Defect segmentation of apples is an important task in the agriculture industry for quality control and food safety. In this paper, we propose a deep learning approach for the automated segmentation of apple defects using convolutional neural networks (CNNs) based on a U-shaped architecture with skip-connections only within the noise reduction block. An ad-hoc data synthesis technique has been designed to increase the number of samples and at the same time to reduce neural network overfitting. We evaluate our model on a dataset of multi-spectral apple images with pixel-wise annotations for several types of defects. In this paper, we show that our proposal outperforms in terms of segmentation accuracy general-purpose deep learning architectures commonly used for segmentation tasks. From the application point of view, we improve the previous methods for apple defect segmentation. A measure of the computational cost shows that our proposal can be employed in real-time (about 100 frame-per-second on GPU) and in quasi-real-time (about 7/8 frame-per-second on CPU) visual-based apple inspection. To further improve the applicability of the method, we investigate the potential of using only RGB images instead of multi-spectral images as input images. The results prove that the accuracy in this case is almost comparable with the multi-spectral case.
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Affiliation(s)
| | | | - Raimondo Schettini
- Dipartimento di Informatica, Sistemistica e Comunicazione, Università Milano-Bicocca, 20126 Milano, Italy; (M.A.); (P.N.)
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Wang X, Wang T, Zheng Y, Yin X. Recognition of liver tumors by predicted hyperspectral features based on patient's Computed Tomography radiomics features. Photodiagnosis Photodyn Ther 2023:103638. [PMID: 37247798 DOI: 10.1016/j.pdpdt.2023.103638] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 05/24/2023] [Accepted: 05/26/2023] [Indexed: 05/31/2023]
Abstract
BACKGROUND Primary liver tumors have posed a serious threat to human life and health, and their early diagnosis is urgent. Therefore, enhancing the accuracy of non-invasive early detection of liver tumors is imperative. METHODS Firstly, image enhancement was applied to augment the dataset, resulting in a total of 464 samples after employing seven data augmentation methods. Subsequently, the XGBoost model was utilized to construct and learn the mapping relationship between Computed Tomography (CT) and corresponding hyperspectral imaging (HSI) data. This model enables the prediction of HSI features corresponding to CT features, thereby enriching CT with more comprehensive hyperspectral information. RESULTS Four classifiers were employed to discern the presence of tumors in patients. The results demonstrated exceptional performance, with a classification accuracy exceeding 90%. CONCLUSIONS This study proposes an artificial intelligence-based methodology that utilizes early CT radiomics features to predict HSI features. Subsequently, the results are utilized for non-invasive tumor prediction and early screening, thereby enhancing the accuracy of non-invasive liver tumor detection.
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Affiliation(s)
- Xuehu Wang
- College of Electronic and Information Engineering, Hebei University, Baoding 071000, China; Research Center of Machine Vision Engineering & Technology of Hebei Province, Baoding 071000, China; Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding 071000, China
| | - Tianqi Wang
- College of Electronic and Information Engineering, Hebei University, Baoding 071000, China; Research Center of Machine Vision Engineering & Technology of Hebei Province, Baoding 071000, China; Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding 071000, China
| | - Yongchang Zheng
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing, 100010, P. R. China.
| | - Xiaoping Yin
- Affiliated Hospital of Hebei University, Baoding 071000, China
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Grabska J, Beć KB, Ueno N, Huck CW. Analyzing the Quality Parameters of Apples by Spectroscopy from Vis/NIR to NIR Region: A Comprehensive Review. Foods 2023; 12:foods12101946. [PMID: 37238763 DOI: 10.3390/foods12101946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Revised: 05/04/2023] [Accepted: 05/08/2023] [Indexed: 05/28/2023] Open
Abstract
Spectroscopic methods deliver a valuable non-destructive analytical tool that provides simultaneous qualitative and quantitative characterization of various samples. Apples belong to the world's most consumed crops and with the current challenges of climate change and human impacts on the environment, maintaining high-quality apple production has become critical. This review comprehensively analyzes the application of spectroscopy in near-infrared (NIR) and visible (Vis) regions, which not only show particular potential in evaluating the quality parameters of apples but also in optimizing their production and supply routines. This includes the assessment of the external and internal characteristics such as color, size, shape, surface defects, soluble solids content (SSC), total titratable acidity (TA), firmness, starch pattern index (SPI), total dry matter concentration (DM), and nutritional value. The review also summarizes various techniques and approaches used in Vis/NIR studies of apples, such as authenticity, origin, identification, adulteration, and quality control. Optical sensors and associated methods offer a wide suite of solutions readily addressing the main needs of the industry in practical routines as well, e.g., efficient sorting and grading of apples based on sweetness and other quality parameters, facilitating quality control throughout the production and supply chain. This review also evaluates ongoing development trends in the application of handheld and portable instruments operating in the Vis/NIR and NIR spectral regions for apple quality control. The use of these technologies can enhance apple crop quality, maintain competitiveness, and meet the demands of consumers, making them a crucial topic in the apple industry. The focal point of this review is placed on the literature published in the last five years, with the exceptions of seminal works that have played a critical role in shaping the field or representative studies that highlight the progress made in specific areas.
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Affiliation(s)
- Justyna Grabska
- Institute of Analytical Chemistry and Radiochemistry, University of Innsbruck, Innrain 80-82, 6020 Innsbruck, Austria
| | - Krzysztof B Beć
- Institute of Analytical Chemistry and Radiochemistry, University of Innsbruck, Innrain 80-82, 6020 Innsbruck, Austria
| | - Nami Ueno
- Institute of Analytical Chemistry and Radiochemistry, University of Innsbruck, Innrain 80-82, 6020 Innsbruck, Austria
| | - Christian W Huck
- Institute of Analytical Chemistry and Radiochemistry, University of Innsbruck, Innrain 80-82, 6020 Innsbruck, Austria
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Yu F, Lu T, Xue C. Deep Learning-Based Intelligent Apple Variety Classification System and Model Interpretability Analysis. Foods 2023; 12:foods12040885. [PMID: 36832960 PMCID: PMC9956933 DOI: 10.3390/foods12040885] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 02/06/2023] [Accepted: 02/07/2023] [Indexed: 02/22/2023] Open
Abstract
In this study, series networks (AlexNet and VGG-19) and directed acyclic graph (DAG) networks (ResNet-18, ResNet-50, and ResNet-101) with transfer learning were employed to identify and classify 13 classes of apples from 7439 images. Two training datasets, model evaluation metrics, and three visualization methods were used to objectively assess, compare, and interpret five Convolutional Neural Network (CNN)-based models. The results show that the dataset configuration had a significant impact on the classification results, as all models achieved over 96.1% accuracy on dataset A (training-to-testing = 2.4:1.0) compared to 89.4-93.9% accuracy on dataset B (training-to-testing = 1.0:3.7). VGG-19 achieved the highest accuracy of 100.0% on dataset A and 93.9% on dataset B. Moreover, for networks of the same framework, the model size, accuracy, and training and testing times increased as the model depth (number of layers) increased. Furthermore, feature visualization, strongest activations, and local interpretable model-agnostic explanations techniques were used to show the understanding of apple images by different trained models, as well as to reveal how and why the models make classification decisions. These results improve the interpretability and credibility of CNN-based models, which provides guidance for future applications of deep learning methods in agriculture.
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Affiliation(s)
- Fanqianhui Yu
- Haide College, Ocean University of China, Qingdao 266100, China
- Department of Computer Science and Technology, Ocean University of China, Qingdao 266100, China
- College of Food Science and Engineering, Ocean University of China, Qingdao 266003, China
| | - Tao Lu
- School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, China
- Correspondence:
| | - Changhu Xue
- College of Food Science and Engineering, Ocean University of China, Qingdao 266003, China
- Laboratory of Marine Drugs and Biological Products, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao 266237, China
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Liang X, Jia X, Huang W, He X, Li L, Fan S, Li J, Zhao C, Zhang C. Real-Time Grading of Defect Apples Using Semantic Segmentation Combination with a Pruned YOLO V4 Network. Foods 2022; 11:foods11193150. [PMID: 36230226 PMCID: PMC9563605 DOI: 10.3390/foods11193150] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Revised: 10/03/2022] [Accepted: 10/04/2022] [Indexed: 11/16/2022] Open
Abstract
At present, the apple grading system usually conveys apples by a belt or rollers. This usually leads to low hardness or expensive fruits being bruised, resulting in economic losses. In order to realize real-time detection and classification of high-quality apples, separate fruit trays were designed to convey apples and used to prevent apples from being bruised during image acquisition. A semantic segmentation method based on the BiSeNet V2 deep learning network was proposed to segment the defective parts of defective apples. BiSeNet V2 for apple defect detection obtained a slightly better result in MPA with a value of 99.66%, which was 0.14 and 0.19 percentage points higher than DAnet and Unet, respectively. A model pruning method was used to optimize the structure of the YOLO V4 network. The detection accuracy of defect regions in apple images was further improved by the pruned YOLO V4 network. Then, a surface mapping method between the defect area in apple images and the actual defect area was proposed to accurately calculate the defect area. Finally, apples on separate fruit trays were sorted according to the number and area of defects in the apple images. The experimental results showed that the average accuracy of apple classification was 92.42%, and the F1 score was 94.31. In commercial separate fruit tray grading and sorting machines, it has great application potential.
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Affiliation(s)
- Xiaoting Liang
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- College of Information Technology, Shanghai Ocean University, Shanghai 201306, China
- National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China
| | - Xueying Jia
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- College of Information Technology, Shanghai Ocean University, Shanghai 201306, China
- National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China
| | - Wenqian Huang
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China
| | - Xin He
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China
| | - Lianjie Li
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China
| | - Shuxiang Fan
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China
| | - Jiangbo Li
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China
| | - Chunjiang Zhao
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China
| | - Chi Zhang
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China
- Correspondence:
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Feng Q, Wang S, Wang H, Qin Z, Wang H. Circle Fitting Based Image Segmentation and Multi-Scale Block Local Binary Pattern Based Distinction of Ring Rot and Anthracnose on Apple Fruits. FRONTIERS IN PLANT SCIENCE 2022; 13:884891. [PMID: 35755697 PMCID: PMC9218820 DOI: 10.3389/fpls.2022.884891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 05/11/2022] [Indexed: 06/15/2023]
Abstract
Ring rot caused by Botryosphaeria dothidea and anthracnose caused by Colletotrichum gloeosporioides are two important apple fruit diseases. It is critical to conduct timely and accurate distinction and diagnosis of the two diseases for apple disease management and apple quality control. The automatic distinction between the two diseases was investigated based on image processing technology in this study. The acquired disease images were preprocessed via image scaling, color image contrast stretching, and morphological opening and closing reconstruction. Then, two lesion segmentation methods based on circle fitting were proposed and used to conduct lesion segmentation. After comparison with the manual segmentation results obtained via the software Adobe Photoshop CC, Lesion segmentation method 1 was chosen for further disease image processing. The gray images on the nine components in the RGB, HSI, and L*a*b* color spaces of the segmented lesion images were filtered by using multi-scale block local binary pattern operators with the sizes of pixel blocks of 1 × 1, 2 × 2, and 3 × 3, respectively, and the corresponding local binary pattern (LBP) histogram vectors were calculated as the features of the lesion images. Subsequently, support vector machine (SVM) models and random forest models were built based on individual LBP histogram features or different LBP histogram feature combinations for distinguishing the diseases. The optimal SVM model with the distinction accuracies of the training and testing sets equal to 100 and 95.12% and the optimal random forest model with the distinction accuracies of the training and testing sets equal to 100 and 90.24% were achieved. The results indicated that the distinction between the two diseases could be implemented with high accuracy by using the proposed method. In this study, a method based on image processing technology was provided for the distinction of ring rot and anthracnose on apple fruits.
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Affiliation(s)
- Qin Feng
- College of Plant Protection, China Agricultural University, Beijing, China
| | - Shutong Wang
- College of Plant Protection, Hebei Agricultural University, Baoding, China
| | - He Wang
- Forest Pest Management and Quarantine Station of Beijing, Beijing, China
| | - Zhilin Qin
- College of Plant Protection, China Agricultural University, Beijing, China
| | - Haiguang Wang
- College of Plant Protection, China Agricultural University, Beijing, China
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Li X, Ding Z, Miao H, Bao J, Tian X. Complete chloroplast genome studies of different apple varieties indicated the origin of modern cultivated apples from Malus sieversii and Malus sylvestris. PeerJ 2022; 10:e13107. [PMID: 35321410 PMCID: PMC8935992 DOI: 10.7717/peerj.13107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 02/22/2022] [Indexed: 01/12/2023] Open
Abstract
Background Apple is one of the most important temperate deciduous fruit trees worldwide, with a wide range of cultivation. In this study, we assessed the variations and phylogenetic relationships between the complete chloroplast genomes of wild and cultivated apples (Malus spp.). Method We obtained the complete chloroplast genomes of 24 apple varieties using next-generation sequencing technology and compared them with genomes of (downloaded from NCBI) the wild species. Result The chloroplast genome of Malus is highly conserved, with a genome length of 160,067-160,290 bp, and all have a double-stranded circular tetrad structure. The gene content and sequences of genomes of wild species and cultivated apple were almost the same, but several mutation hotspot regions (psbI-atpA, psbM-psbD, and ndhC-atpE) were detected in these genomes. These regions can provide valuable information for solving specific molecular markers in taxonomic research. Phylogenetic analysis revealed that Malus formed a new clade and four cultivated varieties clustered into a branch with M. sylvestris and M. sieversii, which indicated that M. sylvestris and M. sieversii were the ancestor species of the cultivated apple.
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MA X, LUO H, ZHANG F, GAO F. A bibliometric and visual analysis of fruit quality detection research. FOOD SCIENCE AND TECHNOLOGY 2022. [DOI: 10.1590/fst.72322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- Xueting MA
- Tarim University, China; Tarim University, China
| | - Huaping LUO
- Tarim University, China; Tarim University, China
| | - Fei ZHANG
- Tarim University, China; Tarim University, China
| | - Feng GAO
- Tarim University, China; Tarim University, China
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Remote drain inspection framework using the convolutional neural network and re-configurable robot Raptor. Sci Rep 2021; 11:22378. [PMID: 34789747 PMCID: PMC8599452 DOI: 10.1038/s41598-021-01170-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Accepted: 10/18/2021] [Indexed: 11/09/2022] Open
Abstract
Drain blockage is a crucial problem in the urban environment. It heavily affects the ecosystem and human health. Hence, routine drain inspection is essential for urban environment. Manual drain inspection is a tedious task and prone to accidents and water-borne diseases. This work presents a drain inspection framework using convolutional neural network (CNN) based object detection algorithm and in house developed reconfigurable teleoperated robot called 'Raptor'. The CNN based object detection model was trained using a transfer learning scheme with our custom drain-blocking objects data-set. The efficiency of the trained CNN algorithm and drain inspection robot Raptor was evaluated through various real-time drain inspection field trial. The experimental results indicate that our trained object detection algorithm has detect and classified the drain blocking objects with 91.42% accuracy for both offline and online test images and is able to process 18 frames per second (FPS). Further, the maneuverability of the robot was evaluated from various open and closed drain environment. The field trial results ensure that the robot maneuverability was stable, and its mapping and localization is also accurate in a complex drain environment.
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