Biosystems Engineering最新文献

{"title":"Litchi bunch detection and ripeness assessment using deep learning and clustering with image processing techniques","authors":"Chenglin Wang,&nbsp;Haoming Wang,&nbsp;Qiyu Han,&nbsp;Zhandong Wu,&nbsp;Chunjiang Li,&nbsp;Zhaoguo Zhang","doi":"10.1016/j.biosystemseng.2025.104173","DOIUrl":"10.1016/j.biosystemseng.2025.104173","url":null,"abstract":"<div><div>Litchis typically need to be harvested in bunches. The detection of entire litchi bunches and the classification of ripeness are crucial issues for robotic harvesting and are also prerequisites for efficient and non-destructive picking. However, the detection process is complicated by the complex orchard environment and the green colour of litchis, and research on the ripeness grading of litchi bunches is still lacking. To address this issue, this paper proposes a litchi ripeness assessment method combining three components: (1) Litchi-YOLO model, (2) a novel image processing framework, and (3) KGAP-DBSCAN clustering algorithm. The HyCTAS module is embedded into the YOLOv8 model to perform instance segmentation on the litchi fruits in the collected images, obtaining the fruit target points and masks. Then, the KGAP-DBSCAN clustering algorithm automatically clusters the fruit points into litchi bunches by setting the clustering radius <em>ε</em> based on the density of the target points. Ripeness grading of individual fruits is achieved by calculating the proportion of red pericarp and determining the ripeness of litchi bunches based on the agronomic information related to litchi growth. The results show that in terms of detection performance, Litchi-YOLO achieved a precision (P), recall (R), and <em>F</em>₁-score of 95.96 %, 95.69 %, and 95.82 %, respectively, representing improvements of 1.25 %, 6.97 %, and 4.25 % over YOLOv8. In terms of clustering performance, the KGAP-DBSCAN algorithm achieved homogeneity, completeness, and v-measure scores of 0.91, 0.76, and 0.78, respectively, for clustering the fruit coordinate points. The ripeness grading method for individual fruits demonstrated good performance, with a precision of 94.20 % and a recall of 91.91 %. The ripeness of the litchi bunches, calculated from the ripeness parameters of individual fruits and clustering results, meets agronomic requirements. The study assesses the maturity of litchi bunches in a natural environment, assisting the orchard harvesting robot system in determining harvesting decisions.</div></div>","PeriodicalId":9173,"journal":{"name":"Biosystems Engineering","volume":"255 ","pages":"Article 104173"},"PeriodicalIF":4.4,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143922096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multimodal knowledge distillation framework for fish feeding behaviour recognition in industrial aquaculture","authors":"Zheng Zhang,&nbsp;Bosheng Zou,&nbsp;Qingsong Hu,&nbsp;Weiqian Li","doi":"10.1016/j.biosystemseng.2025.104170","DOIUrl":"10.1016/j.biosystemseng.2025.104170","url":null,"abstract":"<div><div>Fish feeding behaviour recognition based on machine vision has great significance in industrial aquaculture. Due to the problems of turbid water and overlapping fish during the feeding stage, accurate and low-cost feeding behaviour recognition becomes challenging in actual industrial aquaculture. To address these issues, a novel Multimodal Knowledge Distillation Recognition (MMKDR) framework, based on multimodal fusion and enhanced knowledge distillation, is proposed, to achieve low-complexity and low-cost deployment. Specifically, we utilised the Feature Extraction module of ConvNeXt-T (CNXFE) to extract image features from video streaming. Then, an Improved Multimodal Fusion (IMF) module is designed to generate the fused feature, which can dynamically adjust the weights of the image and water quality features. Next, a Lightweight Feeding Intensity Classification (LFIC) module is designed to predict the fish feeding intensity from the fused feature, which helps to optimise feeding strategies and reduce aquaculture management cost. To deploy the student model on low-cost embedded devices, we further reduce the parameters of the CNXFE and IMF, and obtain the smaller student model with 2.49M parameters. An Enhanced Knowledge Distillation (EKD) scheme, with semi-supervised domain adaptation, is present to achieve knowledge transfer with better recognition accuracy. It can reduce devices and data annotation costs to promote low-cost and low-carbon aquaculture. We carried out experiments and evaluated MMKDR in a real industrial aquaculture environment. The results demonstrated that the student model achieved an accuracy of 96.65 % on the testing set, and an accuracy of 91.36 % using low-cost embedded device in real industrial aquaculture scenarios.</div></div>","PeriodicalId":9173,"journal":{"name":"Biosystems Engineering","volume":"255 ","pages":"Article 104170"},"PeriodicalIF":4.4,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143921974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A reconstruction method for incomplete pig point clouds based on stepwise hole filling and its applications","authors":"Zhankang Xu ,&nbsp;Qifeng Li ,&nbsp;Weihong Ma ,&nbsp;Mingyu Li ,&nbsp;Xianglong Xue ,&nbsp;Chunjiang Zhao","doi":"10.1016/j.biosystemseng.2025.104171","DOIUrl":"10.1016/j.biosystemseng.2025.104171","url":null,"abstract":"<div><div>The 3D model accurately depicts the surface characteristics of pigs, enabling measurement of their body size and prediction of the weight. However, multi-view 3D point cloud reconstructions of pigs often suffer from significant missing areas in leg and torso regions due to factors like railing obstructions and camera blind spots. To address this issue, this paper proposes a method for reconstructing incomplete pig point clouds based on stepwise hole filling. This approach converts the point cloud into mesh, initially filling part of the large, high-curvature holes that are difficult to handle based on pig morphology to narrow their extent, followed by filling remaining areas. Experimental results show that the completion effect of this method is visually superior to existing completion methods. The mean relative errors for calculating cannon bone girth, chest girth, and abdominal girth using the completed model compared to manual measurements were 5.04 %, 3.83 %, and 3.51 %, respectively, representing reductions of 1.24 %, 11.47 %, and 9.48 % compared to the method of directly using incomplete point clouds. In addition, utilizing the watertight properties of the mesh model completed by this method, the volume of the pig was calculated, and a volume-based Logistic regression weight estimation model was established, achieving a mean absolute percentage error (MAPE) of 4.06 %. This underscores its high precision in estimating pig weight.</div></div>","PeriodicalId":9173,"journal":{"name":"Biosystems Engineering","volume":"255 ","pages":"Article 104171"},"PeriodicalIF":4.4,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143899272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancements in maize leaf disease detection, segmentation and classification: A review","authors":"Suresh Timilsina ,&nbsp;Sandhya Sharma ,&nbsp;Satoshi Kondo","doi":"10.1016/j.biosystemseng.2025.104162","DOIUrl":"10.1016/j.biosystemseng.2025.104162","url":null,"abstract":"<div><div>Maize is one of the most widely produced and consumed crops in the world. Production and quality are directly dependent on crop health. Many of the machine-learning (ML) and deep-learning (DL) approaches for maize leaf disease detection, segmentation and classification (MLDDSC) have been implemented for crucial tasks in sustainable agriculture. A total of 82 papers between the years 2020 and 2024 were selected after applying preliminary selection criteria focusing on the review's major goal. In this review paper, the latest developments of MLDDSC in the context of dataset sources, image pre-processing, image augmentation, feature extraction, evaluation metrics, machine-learning architectures, deep-learning architectures, and customisation techniques. The paper also discusses the challenges and future directions of research in MLDDSC, such as severity measurement, hyperspectral imaging, and lightweight models. Finally, a systematic and in-depth analysis is provided of the state-of-the-art methods and techniques for MLDDSC to highlight the potential and limitations of each approach. Overall, from the comparative analysis among the selected papers for review, it was found that multimodal logistic regression outperformed all ML algorithms, whereas pre-trained GoogleNet was efficient among DL models. Likewise, a customised model with fusion of inception and residual structure and a transfer learning model with EfficientNet outperformed all others. Regarding severity measurement, diseased leaf area was the most significant, but the techniques for calculating area can differ. The review also provides a taxonomy and comparison of the existing methods and techniques and identifies the research gaps and opportunities for further improvement.</div></div>","PeriodicalId":9173,"journal":{"name":"Biosystems Engineering","volume":"255 ","pages":"Article 104162"},"PeriodicalIF":4.4,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143879456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and test of a rotary centrifugal granular fertiliser hole-applied discharge device","authors":"Xinhe Shan ,&nbsp;Liwei Li ,&nbsp;Bingxin Yan ,&nbsp;Jianjun Dong ,&nbsp;Xueli Wei ,&nbsp;Zhijun Meng ,&nbsp;Guangwei Wu","doi":"10.1016/j.biosystemseng.2025.104163","DOIUrl":"10.1016/j.biosystemseng.2025.104163","url":null,"abstract":"<div><div>To solve the issues of inadequate loading and hole formation performance of the fertiliser hole-applied discharge device, a rotary centrifugal granular fertiliser hole-applied discharge device (RCGF-HDD) was proposed, and the key components were designed through theoretical analysis. The discrete element method was used to simulate the characteristics of loading and hole formation. Bench tests were designed to validate the simulation results and to explore the adaptability of the discharge device to various types of fertiliser. Through response surface analysis, the loading and hole formation performance were found to be optimal at a fertiliser cavity depth of 21.6 mm, a forward speed of 3.6 km h⁻¹, and a fertiliser dosage per hole of 5.3 g, resulting in average hole length, coefficient of variation of hole length, and error in fertiliser dosage per hole of 72.4 mm, 8.91 %, and 1.24 %, respectively. The results of the bench test showed that under the optimal parameter combination, the average fertiliser cluster length was 22.2 mm, the coefficient of variation was 7.88 %, and the error in fertiliser dosage per hole was 5.86 %. At forward speeds of 8–12 km h⁻¹, the average fertiliser cluster length was lower than 50.0 mm, which indicated that the RCGF-HDD had a certain degree of adaptability and good fertiliser agglomeration properties. The innovative RCGF-HDD developed can meet the demand, and the research methods and results can serve as references for the design and optimisation of fertiliser hole-applied discharge devices.</div></div>","PeriodicalId":9173,"journal":{"name":"Biosystems Engineering","volume":"255 ","pages":"Article 104163"},"PeriodicalIF":4.4,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143851548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning algorithms to identify individual finishing pigs using 3D data","authors":"Shiva Paudel ,&nbsp;Tami Brown-Brandl ,&nbsp;Gary Rohrer ,&nbsp;Sudhendu Raj Sharma","doi":"10.1016/j.biosystemseng.2025.104143","DOIUrl":"10.1016/j.biosystemseng.2025.104143","url":null,"abstract":"<div><div>The application of precision livestock farming technology is heavily reliant on the identification of individuals. However, due to the cost and time constraints, finishing pigs are rarely tagged or otherwise identified. Therefore, the objectives of this study were to determine the feasibility of using deep learning on 3D spatial data to identify individual finishing pigs and to quantify the amount of data required, image resolution needed, and frequency of retraining for continuous identification using two different architectures: PointNet (which utilises point clouds directly) and 3D convolution neural network (3D CNN). Digital/depth images were collected using ToF (Time of Flight) camera positioned over RFID (Radio Frequency Identification) instrumented drinkers. A subset of this data were used for this initial validation study, which included 31976 images from eight pigs over 14 days. The data were then processed to create different sets of training and testing data with varying point sets (1500, 3000, 6000, 12000, 24000, and 48000) for point clouds and voxel sizes (50, 35, 25, and 15 mm) for 3D CNN. The findings revealed that the 3D CNN model achieved the highest F1 score of 0.91 after the sixth training session with a point voxel size of 15 mm. PointNet achieved its highest F1 score of 0.90 after five training sessions with a point set size of 1500 points. This study underscores the potential of utilising deep learning techniques for the purpose of individual pig identification within actual barn environments, including those with natural lighting conditions.</div></div>","PeriodicalId":9173,"journal":{"name":"Biosystems Engineering","volume":"255 ","pages":"Article 104143"},"PeriodicalIF":4.4,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143850735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mechanism of low damage rate maize ear pre-threshing based on cob internal expansion cracking","authors":"Deyi Zhou,&nbsp;Pengfei Hou,&nbsp;Jinsong Zhang,&nbsp;Chunsheng Yu,&nbsp;Daxin Liu,&nbsp;Zeshe Huang,&nbsp;Chengyu Zhang,&nbsp;Zhiheng Wang,&nbsp;Zhenyuan Lin,&nbsp;Tingkun Chen","doi":"10.1016/j.biosystemseng.2025.104157","DOIUrl":"10.1016/j.biosystemseng.2025.104157","url":null,"abstract":"<div><div>During mechanical threshing of maize, the connection forces between the kernels and the cobs, along with the mutual support forces among the kernels, are crucial factors determining the applied force by the threshing elements, impacting the extent of kernel damage. Currently, solutions aimed at reducing or eliminating the mutual support forces among kernels to minimise threshing damage have not been found. Thus, this study proposes a novel pre-threshing method involving the cob's internal expansion to split the maize ears into fragments to achieve partial threshing and diminish the mutual support forces among the kernels. A detailed analysis is conducted on the impact of kernel arrangement, position, and support quantity on both intact maize ears and maize ear fragments concerning stripping forces. Furthermore, based on the comprehensive force analysis on the process of fragmenting the maize ear from the internal, we have designed and fabricated a new test device. Experiments were performed on comparing three types of maize ears through 4-, 6-, and 8-bulging wedges on the expanding rod, respectively. Results indicated that the number of maize ear fragments ranged from 13 to 18 for TK 601 maize ears. As the number of wedge elements increased, fragment size decreased, with the average number of kernels per fragment reducing from 38 to 15. The proportion of individually detached kernels increased from 18.16 % to 55.89 %, while the kernel damage rate had a slightly increase from 0.08 % to 0.96 %. Similar trends were observed in the other two types of maize ears. This study provides a new solution for achieving low-damage threshing of maize.</div></div>","PeriodicalId":9173,"journal":{"name":"Biosystems Engineering","volume":"255 ","pages":"Article 104157"},"PeriodicalIF":4.4,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143847625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Preliminary results of extensive tractor rollover stability tests using a tilting-rotating rig","authors":"Giovanni Carabin ,&nbsp;Merve Karaca ,&nbsp;Fabrizio Mazzetto","doi":"10.1016/j.biosystemseng.2025.104146","DOIUrl":"10.1016/j.biosystemseng.2025.104146","url":null,"abstract":"<div><div>Addressing safety issues in mountain agro-forestry operations, a critical focus is on the stability of machines to prevent rollovers. Despite advancements in technologies and techniques enhancing overall safety, fatalities remain a significant concern. Italy, for instance, witnesses over 120 fatal accidents annually due to tractor rollovers. Even in less serious cases, they still lead to considerable vehicle damage and financial losses. Consequently, investigating and characterising the tractor stability behaviour emerges as a crucial endeavour. This has led to consider in this work, also for certification purposes, the definition of mixed approaches typical of twin models, with predictive modelling assessments extended to a broad application context complemented by punctual measurements on full-scale machines. These measurements have been carried out by means of a novel rotating and tilting test-rig for tractor rollover evaluation available at the Agroforestry Innovation Laboratory (AFILab) of the Free University of Bozen-Bolzano. The study concentrates in particular on examining and comparing the (static) rollover stability results on three different types of tractors commonly employed in mountain operations: a conventional tractor, a narrow track tractor, and a mountain-specialist model. The output of this approach are the stability maps, graphical tools summarising stability limit conditions for diverse configurations. The preliminary results, despite some simplifications adopted in the first version of the digital model, show an excellent correlation between the modelling approach and real measurements. Aspects for future refinement may concern the inclusion of procedures capable of reproducing tyre deformation with greater fidelity under conditions of significant slope.</div></div>","PeriodicalId":9173,"journal":{"name":"Biosystems Engineering","volume":"254 ","pages":"Article 104146"},"PeriodicalIF":4.4,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143842925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating stomatal conductance in maize from 3D plant scans","authors":"Chiara Rusconi ,&nbsp;Roberto Confalonieri ,&nbsp;Luigi Bazzana ,&nbsp;Filippo Fanchi ,&nbsp;Emma Zanotti ,&nbsp;Livia Paleari","doi":"10.1016/j.biosystemseng.2025.104161","DOIUrl":"10.1016/j.biosystemseng.2025.104161","url":null,"abstract":"<div><div>Given conflicts for blue water are projected to exacerbate, optimising irrigation will be increasingly crucial. Despite stomatal conductance (<em>g</em>_<em>s</em>) being among the variables with the greatest potential to quantify crop water status, the difficulties and the cost of performing measurements have prevented its use in operational contexts. A model is proposed for estimating <em>g</em>_<em>s</em> in maize from smartphone-based 3D leaf scans, as a function of leaf insertion angle of the penultimate leaf and the degree of leaf curvature in the top canopy layers. The model was evaluated – against <em>g</em>_<em>s</em> measurements from an infrared gas analyser (IRGA) – for three maize hybrids using data from a dedicated pot experiment where different irrigation treatments were applied. The agreement between <em>g</em>_<em>s</em> values from IRGA and from the proposed model was satisfactory for two hybrids (R² = 0.78 and 0.73), whereas slightly poorer results were achieved for the third one (R² = 0.51). The three hybrids responded to water stress by adopting different behaviours in terms of reducing/increasing insertion angles and of straightening/curving leaf blades, leading to genotype-specific coefficients for the two predictors. The relationships between <em>g</em>_<em>s</em> and canopy architectural indicators could be implemented in monitoring platforms based on LIDAR or multi-view stereo imaging, opening new opportunities for developing improved systems to optimise irrigation under operational farming conditions.</div></div>","PeriodicalId":9173,"journal":{"name":"Biosystems Engineering","volume":"254 ","pages":"Article 104161"},"PeriodicalIF":4.4,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143842922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"3D plant segmentation: Comparing a 2D-to-3D segmentation method with state-of-the-art 3D segmentation algorithms","authors":"Bart M. van Marrewijk ,&nbsp;Tim van Daalen ,&nbsp;Bolai Xin ,&nbsp;Eldert J. van Henten ,&nbsp;Gerrit Polder ,&nbsp;Gert Kootstra","doi":"10.1016/j.biosystemseng.2025.104147","DOIUrl":"10.1016/j.biosystemseng.2025.104147","url":null,"abstract":"<div><div>Plant measurements are crucial to determine which plants grow optimal under certain conditions. These measurements can be done by hand, or automated using cameras, also known as image-based plant phenotyping. These images can be used to create point clouds to measure plant traits in 3D. To extract plant traits, accurate segmentation is crucial. Most point cloud segmentation methods rely on 3D segmentation algorithms. These algorithms are not as advanced and developed as 2D algorithms. In addition, 2D neural networks are pre-trained on large diverse datasets. In our work, it was therefore hypothesised that segmentation of point clouds using projection-based methods can obtain a higher accuracy than voxel or point-based algorithms. To test this hypothesis, a 2D-to-3D reprojection method was developed and compared with three state-of-the-art 3D segmentation algorithms; Swin3D-s, Point Transformer v3 and MinkUNet34C. The 2D-to-3D method segmented images using Mask2Former, reprojected the predictions to the point cloud, and used a majority vote algorithm to merge multiple predictions. All algorithms were trained and tested to segment 3D point clouds into leaves, main stem, side stem, and pole. There was no significant difference between the 2D-to-3D, Swin3D-s and Point Transformer v3 algorithm, indicating that state-of-the-art voxel or point-based methods perform similar than our projection-based method. However, the 2D-to-3D method had a higher performance by including virtual cameras and it had a higher training efficiency. With only five annotated plants, a similar performance was obtained than training Swin3D-s on 25 plants indicating the added value of the developed pipeline.</div></div>","PeriodicalId":9173,"journal":{"name":"Biosystems Engineering","volume":"254 ","pages":"Article 104147"},"PeriodicalIF":4.4,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143842923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}