Computers and Electronics in Agriculture最新文献

{"title":"Physiology at work to model apple expansion growth and skin pigment changes","authors":"Roy McCormick ,&nbsp;Pol Tijskens ,&nbsp;Nils Siefen ,&nbsp;Konni Biegert","doi":"10.1016/j.compag.2025.111027","DOIUrl":"10.1016/j.compag.2025.111027","url":null,"abstract":"<div><div>Fruit sizing is a major factor in determining yield while non-destructive monitoring of the fruit skin colour and internal quality attributes on the tree can provide valuable maturity and quality information for precision horticulture. Repeated spectral scanning and fruit sizing data of ‘Braeburn’ apples were collected on the tree from about 60 days after flowering until harvest. Assessed variables were: fruit diameter, dry matter (DMC), soluble solids content (SSC), a normalised difference vegetation index (NDVI) and a normalised anthocyanin index (NAI) analysed using indexed non-linear regression based on an adapted von Bertalanffy model (diameter, DMC, SSC), or a logistic model (NDVI, NAI). The reaction rate constants in the models were estimated in common for all fruit in a selection, while the biological shift factors (<span><math><mi>Δ</mi></math></span><em>t</em>) estimated the development stage or fruit maturity per individual fruit. Explained parts (R²_adj) range from 85 to 97%. Tree location or crop load treatment only minimally affected the rate constants but did affect the estimated <span><math><mi>Δ</mi></math></span><em>t</em> values that describe almost all variation in the data. There is a close relationship between the <span><math><mi>Δ</mi></math></span><em>t</em> values for diameter, DMC and SSC but less with those of NDVI and almost none with the NAI. These data support the assumption that there is only one stage of fruit maturity, but it is estimated slightly differently depending on the measured variable. The actual relative growth rate strongly depends on the current size. Understanding apple expansion growth will therefore require a closer focus on the cell production period.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"239 ","pages":"Article 111027"},"PeriodicalIF":8.9,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266579","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":"Performance analysis on root-soil separation of Panax notoginseng using DEM-CFD method and air-jet system","authors":"Chenglin Wang,&nbsp;Pengfei Li,&nbsp;Zedong Zhao,&nbsp;Weiyu Pan,&nbsp;Qiyu Han,&nbsp;Zhi He,&nbsp;Maoling Yang,&nbsp;Zhaoguo Zhang","doi":"10.1016/j.compag.2025.111050","DOIUrl":"10.1016/j.compag.2025.111050","url":null,"abstract":"<div><div>PN (Panax notoginseng root) is a kind of Chinese traditional medicinal crop. A large amount of soil is attached to PN after the harvest. Therefore, PN-S (PN and soil mixture) separation operation is a key step in the postharvest processing of PN. However, the root and soil separation operation of PN suffers from two problems: poor root-soil separation effect and unclear separation process. In this study, a method for PN-S separation using jetting is proposed. The three-dimensional scales and geometries of PN and soil particles were measured and calibrated. A coupled fluid–solid separation model of PN-S was constructed using DEM-CFD (discrete element coupling method and computational fluid dynamics). Based on the structure of the air-jet soil separation device and the problems to be solved in the study, a new evaluation standard combining φ (soil separation rate) and η (soil attachment rate) was proposed to quantify the working effect of the air-jet device. This study established experimental platforms for PN and soil air-jet separation devices in both simulated and actual environments. Through the mutual corroboration of physical and simulation experiments, the results prove that the method is feasible. Obtain a clear process of PN and soil separation through the use of a simulation experimental platform. The optimum parameters of the air-jet device were further obtained through the BBD (Box-Behnken Design) method. The optimal parameters are: pressure of 2285 Pa, distance of 10 cm, angle of 90°, and φ of 62.06 %. The fluid–solid coupling model and the new evaluation criteria established in this study provide a theoretical basis for the effective separation of PN-S. The proposed air-jet separation method can effectively separate PN-S. The results of this study provide new ideas and technical support for the optimization and design of PN-S separation equipment.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"239 ","pages":"Article 111050"},"PeriodicalIF":8.9,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219574","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":"Bit-STED: A lightweight transformer for accurate agave counting with UAV imagery","authors":"Diego Villatoro-Geronimo,&nbsp;Gildardo Sanchez-Ante,&nbsp;Luis E. Falcon-Morales","doi":"10.1016/j.compag.2025.111047","DOIUrl":"10.1016/j.compag.2025.111047","url":null,"abstract":"<div><div>This paper presented Bit-STED, a novel and simplified transformer encoder architecture for efficient agave plant detection and accurate counting using unmanned aerial vehicle (UAV) imagery. Addressing the critical need for accessible and cost-efficient solutions in agricultural monitoring, this approach automates a process that is typically time-consuming, labor-intensive, and prone to human error in manual practices. The Bit-STED model features a lightweight transformer design that incorporates innovative techniques for efficient feature extraction, model compression through quantization, and shape-aware object localization using circular bounding boxes for the roughly circular shape of the agave rosettes. To complement the detection model, a novel counting algorithm was developed to manage plants spanning multiple image tiles accurately. The experimental results demonstrated that the Bit-STED model outperformed the baseline models in terms of detection and agave plant count performance. Specifically, the Bit-STED nano model achieved F1 scores of 96.66% on a map with younger plants and 96.43% on a map with larger, highly overlapping plants. These scores surpassed state-of-the-art baselines, such as YOLOv8 Nano (F1 scores of 96.42% and 96.38%, respectively) and DETR (F1 scores of 93.03% and 85.61%, respectively). Furthermore, the Bit-STED nano model was significantly smaller, being less than one-eighth the size of the YOLOv8 nano model (1.4 MB compared to 12.0 MB), had fewer trainable parameters (0.35M compared to 3.01M), and was faster in average inference times (14.62 ms compared to 18.28 ms).</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"239 ","pages":"Article 111047"},"PeriodicalIF":8.9,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219576","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 ground testing of a Zero-Discharge plant growth system for microgravity Applications","authors":"Claudia Garrido-Ruiz ,&nbsp;Juan D. González-Teruel ,&nbsp;Chihiro Dixon ,&nbsp;Sarah Schreck ,&nbsp;Curtis Bingham ,&nbsp;Thane Winward ,&nbsp;Riley Hutchings ,&nbsp;Gail E. Bingham ,&nbsp;Bruce Bugbee ,&nbsp;Scott B. Jones","doi":"10.1016/j.compag.2025.111044","DOIUrl":"10.1016/j.compag.2025.111044","url":null,"abstract":"<div><div>Plant-based bioregenerative life-support systems play an essential role for long-duration space missions, offering a renewable source of fresh, nutritious food and psychological benefits for crew members. As human space missions extend further and last longer, developing efficient technologies for space agriculture becomes increasingly critical. In microgravity, altered fluid dynamics change water, nutrient, and gas distribution within the root-zone, potentially limiting plant growth. The Utah Reusable Root Module (URRM) system housed within NASA’s Ohalo III Crop Production System addresses these challenges through five root modules equipped with automated fertigation, redundant moisture sensors, and media containment materials. Designed to support repetitive harvests of pick-and-eat vegetables with minimal crew intervention, the URRM advances water and nutrient management strategies for space-based agriculture. Preliminary ground tests with Mizuna (Brassica rapa var. nipposinica) demonstrated that the URRM maintained optimal root-zone conditions and uniform resource distribution, yielding more than 1 kg of fresh biomass over 17 days. The use of different top cover designs and materials across root modules affected plant establishment and yield, as well as evapotranspiration, whereas water use efficiency (WUE) exceeded 2 g L^-1 across the system. These findings highlight the URRM’s ability to support crop production using automated state-of-the-art technologies, strengthening the feasibility of long-term human space exploration.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"239 ","pages":"Article 111044"},"PeriodicalIF":8.9,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219552","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":"CATR-Net: Cattle–Attentive transformer with adaptive and enhanced segmentation and recognition","authors":"Xiaopu Feng ,&nbsp;Jiaying Zhang ,&nbsp;Yongsheng Qi ,&nbsp;Liqiang Liu ,&nbsp;Yongting Li","doi":"10.1016/j.compag.2025.111038","DOIUrl":"10.1016/j.compag.2025.111038","url":null,"abstract":"<div><div>In open–range cattle–face analysis, conventional segmentation networks struggle to preserve fine–scale edge cues, and recognition networks weakly model salient regions and local–global context, yielding brittle performance under changing poses and illumination. We propose CATR–Net, an end–to–end framework that unifies segmentation and identification. In its segmentation branch, a Multiscale Edge–enhanced Upsampling Module (MEUM) is grafted onto the DA–TransUNet decoder to restore high–frequency boundaries and suppress up–sampling blur; in the recognition branch, a Dynamic Contextual Attention Module (DCAM) is inserted between the Stem and MaxViT blocks, and Dynamic Adaptive Interaction Normalization (DAIN) replaces the static Layer Normalization in LSRA (Local Region Self-Attention) with DyT (Dynamic tanh), together enabling pose– and scale–aware fusion of local priors with global dependencies. The recognition loss is further equipped with a confidence–gap regularizer that dynamically tunes the Dynamic–Tanh parameters, amplifying ambiguous features while stabilizing high–confidence activations. On a 57645–image multi–pose dataset, the segmentation branch achieves 93.35 % mIoU and 96.45 % mDSC with a 417  MB model, whereas the recognition branch attains 97.03 % accuracy and 95.19 % F1-score with a 457  MB footprint—both surpassing state–of–the–art baselines at comparable complexity.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"239 ","pages":"Article 111038"},"PeriodicalIF":8.9,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219577","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 derivative approach for efficient hydroponic vertical farm monitoring using hyperspectral vision","authors":"Maria Merin Antony ,&nbsp;M.M. Bijeesh ,&nbsp;C.S. Suchand Sandeep ,&nbsp;Murukeshan Vadakke Matham","doi":"10.1016/j.compag.2025.111029","DOIUrl":"10.1016/j.compag.2025.111029","url":null,"abstract":"<div><div>Vertical indoor hydroponic farms offer sustainable solutions in land scarce countries to foster agriculture productivity for addressing growing demand. Such farms require extensive controllability of the growing conditions to ensure year round-cultivation of diverse crops within the space available. Continuous monitoring of the crops and early remedial measures are essential to ensure non-compromised, high-quality yield from these farms. Currently, most farms rely on human vision based monitoring, which is quite subjective and time-consuming and could be ineffective in identifying crop stresses at early stages. Hence, efficient management of these farms requires advanced automated systems to monitor crop health, including possible stress factors such as nutrient, water, and light deficiencies at early stages to enable timely intervention. This research, in this context, explores innovative strategies using assessment parameters such as spectral ratios and derivative reflectance derived from hyperspectral images for crop monitoring. Customized spectral index for nutrient deficiency detection and approaches for quantification of derivative spectra for stress detection are developed. These strategies can be used to rapidly detect the stresses at the early stages non-destructively (within hours in case of light and water deficiencies) and could promptly guide in timely remedial actions. The proposed method offers automation possibilities for non-invasive monitoring systems utilizing hyperspectral vision. This non-invasive imaging system integrated on a robotic platform is envisaged to revolutionize the development of unmanned indoor hydroponic farms for a sustainable future.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"239 ","pages":"Article 111029"},"PeriodicalIF":8.9,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219550","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":"Feeding behavior recognition of group-housed pigs based on pose estimation and keypoint features discrimination","authors":"Yuhang Hu ,&nbsp;Xin Dai ,&nbsp;Baisheng Dai ,&nbsp;Ran Li ,&nbsp;Junlong Fang ,&nbsp;Yanling Yin ,&nbsp;Honggui Liu ,&nbsp;Weizheng Shen","doi":"10.1016/j.compag.2025.111039","DOIUrl":"10.1016/j.compag.2025.111039","url":null,"abstract":"<div><div>In the field of intelligent sensing for smart animal husbandry, accurate recognition of feeding behavior in group-housed pigs is crucial for achieving precision farming and improving pig welfare. Currently, pig feeding behavior recognition relies on detection boxes-based methods, which are difficult to exclude Non-Nutritive Visiting Behavior within the feeding zone. To precisely recognize the feeding behavior of group-housed pigs, this study proposes a feeding behavior recognition method based on pose estimation and keypoint features discrimination. Firstly, Pig-HRNet is designed to estimate the pose of group-housed pigs, in which a Context Transformer (COT) attention module is specially introduced to detect the keypoints of pigs more accurately under crowded conditions. Secondly, by analyzing the correlation between keypoints and feeding zone, group-housed pigs are divided into visiting the feeding zone and Non-Feeding Behavior (NFB). For visiting the feeding zone, the behaviors are further categorized into Feeding Behavior (FB) and Non-Nutritive Visiting Behavior (NNVB). The experimental data of group-housed pigs were collected in commercial pig farms, including a total of 1400 video frames. Experimental results show that the Pig-HRNet model achieves an average precision (AP) of 97.1% in estimating pig poses. Compared to other pose estimation network models such as KAPAO, HigherHRNet, DeepLabCut, and HRNet, the detection AP improved by 69.0%, 16.3%, 12.3%, and 0.5%, respectively. The feeding behavior recognition method proposed in this paper achieved precision and recall rates of 98.8% and 99.9%, respectively. The relevant results indicate that the proposed feeding behavior recognition method performs well, while also meeting the requirement for accurately estimating pig poses under crowded conditions. The feeding behavior dataset established in this paper has been shared on <span><span><u>https://github.com/IPCLab-NEAU/Group-housed-pigs-Feeding-Behavior-Recognition</u></span><svg><path></path></svg></span> for use by the precision animal husbandry research community.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"239 ","pages":"Article 111039"},"PeriodicalIF":8.9,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219551","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":"DEM simulation of straw-soil-cutting system interactions for performance assessment of a spin-descent straw cutter","authors":"Rongrong Li ,&nbsp;Hongwen Li ,&nbsp;Shuofei Yang ,&nbsp;Caiyun Lu ,&nbsp;Zhengyang Wu ,&nbsp;Zhinan Wang","doi":"10.1016/j.compag.2025.111022","DOIUrl":"10.1016/j.compag.2025.111022","url":null,"abstract":"<div><div>Accurate measurement of straw mulch mass requires effective separation of residues inside and outside the sampling frame. To achieve this, a novel spin-descent straw cutter was designed, integrating a stabilizing ring and a rotating and descending separation column. The stabilizing ring pressed the straw to reduce movement, while the separation column simultaneously rotated and descended to cut and separate the straw at the frame boundary, representing an integrated structure not previously reported. A discrete element method (DEM) model was established to simulate straw-soil-cutting system interactions, and three blade types (sawtooth, corrugated, and notch-shaped) were compared. Response surface methodology and multi-objective optimization were employed to balance cutting rate, power consumption, and soil disturbance area. Bench tests confirmed the DEM predictions with errors within 12 %. Results demonstrated that the sawtooth blade achieved the most favorable trade-off, with optimal parameters of 287 N downward force, 230 rpm rotation speed, and 10 mm insertion depth. The study highlighted the unique suitability of the spin-descent cutter for automated straw mulching detection and provided a validated simulation–optimization framework to inform future cutter designs and broader applications in conservation tillage.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"239 ","pages":"Article 111022"},"PeriodicalIF":8.9,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219631","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":"Intelligent dual-modal hyperspectral imaging system with deep learning for in-field soil fauna identification","authors":"Jing Luo ,&nbsp;He Zhu ,&nbsp;Ronggui Tang ,&nbsp;Sailing He","doi":"10.1016/j.compag.2025.111040","DOIUrl":"10.1016/j.compag.2025.111040","url":null,"abstract":"<div><div>Accurate and efficient identification of living soil fauna is crucial for ecological biodiversity assessment and sustainable agriculture practices, yet traditional methods are labor-intensive, time-consuming, and often ineffective in complex field environments. This study pioneeres a novel dual-modal hyperspectral soil fauna identification (HSFI) system, which ingeniously Integrates both reflectance and fluorescence hyperspectral imaging with a custom-designed deep learning model, HSFI-Net, for robust semantic segmentation. This synergistic approach effectively overcomes the challenges of rapidly and accurately identifying soil fauna, even those partially concealed with heterogeneous soil backgrounds. Using the HSFI system, we established a comprehensive dual-modal spectral database for diverse soil macrofauna, including earthworms, centipedes, scorpions, pillworms, millipedes, crickets, beetles, ants and so on. Extensive experimental evaluations demonstrated the system’s exceptional robustness and high precision, achieving average IoU of 0.734 across various exposure levels and various species under challenging soil conditions. Furthermore, in-field experiments successfully validated the system’s capability for in-situ identification and analysis of soil fauna’s horizontal and vertical distribution. This innovative HSFI system offers an automated, intelligent tool for monitoring soil biodiversity, which is vital for precision agricultural management, environmental conservation, and understanding the intricate dynamics of soil ecosystems.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"239 ","pages":"Article 111040"},"PeriodicalIF":8.9,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219633","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":"Novel implementation of Colby’s method for analyzing interactions between mesotrione and diflufenican using hyperspectral and multispectral machine vision","authors":"Zhongzhong Niu ,&nbsp;Abigail Norsworthy ,&nbsp;Julie Young ,&nbsp;Bryan Young ,&nbsp;Tianzhang Zhao ,&nbsp;Xuan Li ,&nbsp;Alden Mo ,&nbsp;Charles Wang ,&nbsp;Jian Jin","doi":"10.1016/j.compag.2025.111018","DOIUrl":"10.1016/j.compag.2025.111018","url":null,"abstract":"<div><div>Herbicides play a crucial role in cropping systems by providing effective weed control strategies that help farmers eliminate yield-reducing weeds. However, crop injury may result from herbicides applied in current or previous cropping systems, and in some instances, this injury may reduce crop yield. Currently, herbicide related crop injury is commonly determined by subjective visual assessments. Spectral imaging provides an alternative solution, which is high-throughput and non-invasive. In this study, a novel machine vision method utilizing hyperspectral imaging (HSI) and multispectral imaging (MSI) was developed and integrated into Colby’s method—a traditional approach in weed science for analyzing the interaction effects of herbicide mixtures. Mesotrione and diflufenican, both herbicides that cause bleaching symptomology, were applied in this study. Two rounds of field experiments were conducted in the summer of 2024, where hyperspectral and multispectral images were collected 26 DAT in each trial. Partial Least Squares Discriminant Analysis (PLS-DA) models were built to identify soybean injury from mesotrione, diflufenican, and the mixture. For Colby’s method to study the interaction effect, spatial-spectral features were generated from MSI. The HSI models achieved an accuracy exceeding 90 %. Thirteen distinct features were identified and selected to illustrate the synergistic effects of the herbicides, showing consistency across two experimental rounds and aligning with findings from traditional methods.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"239 ","pages":"Article 111018"},"PeriodicalIF":8.9,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219686","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}