Computers and Electronics in Agriculture最新文献

{"title":"Unveiling the infectious morphological behaviour of banana crop pathogenic nematodes inhabited from soil medium to pseudostem using an artificial intelligence approach","authors":"S.S. Jayakrishna,&nbsp;S. Sankar Ganesh","doi":"10.1016/j.compag.2025.110277","DOIUrl":"10.1016/j.compag.2025.110277","url":null,"abstract":"<div><div>Soil-borne microorganisms target the rhizosphere by invading from soil to plants through pseudostem. <em>Fusarium oxysporum</em> f.sp. <em>cubense</em>, an infectious agent’s host, interacts with nematodes present in the single point regional area (SPRA), causing tissue necrosis, and physical disordering of banana plants poses high yield loss. Diagnosing the source of pathogenic microbes on a crop significantly prevents its transmission to other regions. Disease characteristics cannot be accurately assessed through physical observation alone. We proposed Nematode Detection and Morphological Analysis (NDMA-YOLO), a deep learning-based futuristic algorithm, and Tracking Live Parasites (TLP) to tackle this issue. Experiments demonstrated in Fusarium-affected fields with similar soil properties. The chemical composition of soils is characterized by FTIR spectroscopic analysis, pH, moisture, SEM, and fluorescence spectrophotometer content characteristics. Physically identifying the source of infection using the (x, y) Grid Ring Axis Pseudo Stem Holistic (GRAPH) method, obtained plant tissue samples, and generated large image datasets through phase contrast microscopic. Recorded structure of nematodes to understand physiological, behavioral, and biotic stress patterns. We utilized AI-based computer vision for live event monitoring and morphological analysis, employing an enhanced YOLO-v8 model trained on a custom dataset to detect nematodes with 86.66 % accuracy and an overall performance of 98.93%. Our model surpasses previous versions like YOLO-v3, YOLO-v5, and YOLO-v7, showcasing significant advancements in dataset preparation for accurate predictions in plant pathology.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"234 ","pages":"Article 110277"},"PeriodicalIF":7.7,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143631973","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":"Accurate water level monitoring in Alternate Wetting and Drying rice cultivation using attention-based ConvNeXt architecture","authors":"Ahmed Rafi Hasan ,&nbsp;Niloy Kumar Kundu ,&nbsp;Saad Hasan ,&nbsp;Mohammad Rashedul Hoque ,&nbsp;Swakkhar Shatabda","doi":"10.1016/j.compag.2025.110216","DOIUrl":"10.1016/j.compag.2025.110216","url":null,"abstract":"<div><div>The Alternate Wetting and Drying (AWD) method is a rice-growing water management technique promoted as a sustainable alternative to Continuous Flooding (CF). Climate change has placed the agricultural sector in a challenging position, particularly as global water resources become increasingly scarce, affecting rice production on irrigated lowlands. Rice, a staple food for over half of the world’s population, demands significantly more water than other major crops. In Bangladesh, the cultivation of dry season, irrigated <em>Boro</em> rice demands substantial water inputs. Traditional manual water level measurement methods are time-consuming and error-prone, while ultrasonic sensors offer more precise readings but may be affected by environmental factors such as temperature fluctuations, changes in humidity levels, varying light conditions, and accumulation of dust or debris To overcome these limitations, we propose an innovative approach leveraging computer vision, specifically an attention-based ConvNeXt architecture, to automate water height measurement. Our method achieves state-of-the-art performance with an <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> score of 0.989, a Root Mean Squared Error (RMSE) of 0.523 cm, and a Mean Squared Error (MSE) of 0.277 <span><math><mrow><mi>c</mi><msup><mrow><mi>m</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></math></span>, demonstrating superior accuracy and efficiency in managing AWD systems. This advancement represents a significant contribution to sustainable agriculture, enabling precise and automated water management in rice cultivation.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"234 ","pages":"Article 110216"},"PeriodicalIF":7.7,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143628781","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":"Integrating ecological niche and epidemiological models to predict wheat fusarium head blight using remote sensing and meteorological data","authors":"Shangzhou Li,&nbsp;Ping Dong,&nbsp;Hui Zhang,&nbsp;Xin Xu,&nbsp;Lei Shi,&nbsp;Tong Sun,&nbsp;Hongbo Qiao,&nbsp;Jibo Yue,&nbsp;Wei Guo","doi":"10.1016/j.compag.2025.110255","DOIUrl":"10.1016/j.compag.2025.110255","url":null,"abstract":"<div><div>Fusarium head blight (FHB) is a major wheat disease worldwide, significantly affecting yield and quality. Disease risk assessment and spatiotemporal dynamic prediction are crucial for effective FHB management and control. Although ecological niche models (ENMs) and epidemiological models (EMs) have been widely applied to assess the potential distribution of diseases and simulate their progression, studies integrating these models with satellite remote sensing and meteorological data for crop disease prediction remain limited. To fill this gap, our study developed an integrated prediction framework based on susceptible-exposed-infected (SEI) model. First, remote sensing data extracted host factors, including wheat spatial distribution, key phenological (KPh) stages defined by Day of Year (DOY), and early physiological changes. This information, along with meteorological features, topographic factors, and sampling coordinates, was utilized to construct an ENM based on Maximum Entropy (MaxEnt) algorithm. MaxEnt evaluation results guided input adjustments, ensuring high AUC output to characterize initial infection levels for SEI model. Next, transition rates in SEI model were determined by the coupling of the parameterized response functions of daily temperature, relative humidity, and DOY for KPh stages to mechanize the EM. The mechanistic model (MM), with optimal parameter values derived from sensitivity analysis and optimization, provided a robust prediction of disease occurrence on the sampling day and enabled spatiotemporal dynamic simulation of wheat FHB. The final MM achieved a coefficient of determination of 0.83, mean absolute error of 0.06, root mean square error of 0.072, and classification F1-score of 0.88. The simulated disease progression curve was consistent with the epidemiological characteristics of FHB, exhibiting an S-shaped pattern. These results suggest that integrating remote sensing and meteorological data with MaxEnt and SEI models for FHB prediction holds significant application potential.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"234 ","pages":"Article 110255"},"PeriodicalIF":7.7,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143628779","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":"Integrating UAV and high-resolution satellite remote sensing for multi-scale rice disease monitoring","authors":"Lin Yuan ,&nbsp;Qimeng Yu ,&nbsp;Lirong Xiang ,&nbsp;Fanguo Zeng ,&nbsp;Jie Dong ,&nbsp;Ouguan Xu ,&nbsp;Jingcheng Zhang","doi":"10.1016/j.compag.2025.110287","DOIUrl":"10.1016/j.compag.2025.110287","url":null,"abstract":"<div><div>Rice Bacterial Blight (RBB), caused by <em>Xanthomonas oryzae pv. oryzae (Xoo)</em>, is a major rice disease that significantly threatens yield and quality. RBB spreads rapidly under favorable conditions, affects extensive areas, and requires timely, large-scale monitoring due to its narrow window for effective detection. Traditional satellite monitoring methods, which rely on specific remote sensing platforms and extensive ground surveys, often fail to meet the timely and efficient needs of large-scale disease monitoring. To address the limitations of these traditional methods, this study proposes a cross-scale crop disease monitoring approach that integrates unmanned aerial vehicle (UAV) and satellite remote sensing. With RBB disease monitoring in rice as a case study, the inconsistency between different scale remote sensing data is first introduced to align satellite imagery with UAV data. Next, a sensitivity analysis of the original reflectance and disease-related vegetation indices at both scales is conducted to identify features with consistent performance. The minimum redundancy maximum relevance (mRMR) feature selection algorithm is then employed to obtain sensitive feature sets for each scale. Three machine learning algorithms—Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN)—were used to develop disease monitoring models at both UAV and satellite scales. The optimal UAV-scale RF model was then applied to the corrected satellite data for cross-scale monitoring. Results indicate that the proposed cross-scale monitoring method achieved an accuracy of 87.78%, a precision of 88.13%, a recall of 87.78%, and an F1-score of 0.88 for the three-class classification of healthy, mildly infected, and severely infected RBB. The method effectively overcomes the reliance on extensive ground survey data typical of traditional large-scale crop disease remote sensing monitoring methods. Furthermore, the developed approach enables the cross-scale transfer of small-scale monitoring models, ensuring timely disease monitoring during outbreaks.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"234 ","pages":"Article 110287"},"PeriodicalIF":7.7,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143628665","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":"Analyzing different phenotypic methods of soybean leaves under the high temperature stress with near-infrared spectroscopy, microscopic Image, and multispectral image","authors":"Youhui Deng ,&nbsp;Weizhi Yang ,&nbsp;Jiajia Li ,&nbsp;Xiaodan Zhang ,&nbsp;Yuan Rao ,&nbsp;Haoran Chen ,&nbsp;Jianghui Xiong ,&nbsp;Xi Chen ,&nbsp;Xiaobo Wang ,&nbsp;Xiu Jin","doi":"10.1016/j.compag.2025.110281","DOIUrl":"10.1016/j.compag.2025.110281","url":null,"abstract":"<div><div>High temperature stress (HT) plays an important role in soybean selection and breeding, it can cause changes in soybean physiological, biochemical and morphological traits, and directly affect the growth and yield of soybean plants. Among these changes, soybean leaves are particularly sensitive to HT during growth and development. It is important to establish a non-destructive method to distinguish the phenotypic differences between soybean plants under HT and control (CK). In this study, data from two years of soybean field trials were used. In the first year, phenotypic information was collected by near-infrared spectroscopy (NIR), microscopic images, and further difference analysis and classification modelling experiments were conducted. In the second year, multispectral image data were collected and analyzed by Soybean high temperature mask autoencoder (SHT_MAE). The SHT_MAE model with a 75% masking ratio achieved an accuracy of 89.16% and an F1-score of 89.18%. Compared with one-dimensional near-infrared and two-dimensional microscopic image fusion models, the classification accuracy of HT and CK is improved by 2.68%. The accuracy of SHT_MAE multispectral model was improved by 16.84% and 6.88%, respectively, compared with models using only NIR or microscopic images. Both spectral and imaging methods effectively distinguish the phenotypic differences between HT and CK soybean leaves, with the multispectral approach based on the SHT_MAE model demonstrating a clear advantage. This study realized the effective distinction of soybean leaves under HT and CK. It provides theoretical support for HT intelligent breeding (using artificial intelligence and data analysis to optimize breeding decisions) and high temperature grade prediction.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"234 ","pages":"Article 110281"},"PeriodicalIF":7.7,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143628666","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 causal prediction method for soil organic carbon storage change estimation, with Shaanxi Province as a case study","authors":"Yanqing Liu ,&nbsp;Chuanliang Jiang ,&nbsp;Aiping Feng ,&nbsp;Hao Xu ,&nbsp;Yuxue Wang ,&nbsp;Yue Yin ,&nbsp;Chenyi Wang ,&nbsp;Dongkai Xie ,&nbsp;Bingbo Gao","doi":"10.1016/j.compag.2025.110271","DOIUrl":"10.1016/j.compag.2025.110271","url":null,"abstract":"<div><div>Soil organic carbon (SOC) plays a crucial role in global climate change, the carbon cycle, and agricultural productivity, making accurate predictions of SOC changes in a region highly significant. However, due to the complex process of SOC changes, there are many confounding variables and it is not easy to derive robust predictions. The key to the solution is to remove or control these confounding factors. In response to this challenge, this study proposed a method combining causal inference with machine learning to get robust predictions of SOC storage changes. The method first identifies direct and indirect causal variables affecting temporal changes in SOC storage using structural equation modeling (SEM). It then directly predicts the temporal changes with those causal variables based on a newly developed method called two-point machine learning (TPML), rather than comparing spatial interpolation results across different times. In this way, the confounding variables can be removed and it is abbreviated as SemTPML. The SemTPML method was used in a case study of surface SOC (0–10 cm) of Shaanxi Province. The results show that it produces more robust predictions and the highest accuracy. NDVI and average annual precipitation (APre) were identified as the main controlling factors of surface SOC changes in Shaanxi Province. The results also revealed that changes in surface SOC from 1980 to 2020 in Shaanxi Province exhibit a trend of “increasing in the south and decreasing in the north”, with the total changes amounting to a reduction of approximately 1.59 × 10⁷ kg.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"234 ","pages":"Article 110271"},"PeriodicalIF":7.7,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143628677","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":"Technologies and strategies for optimizing the potato supply chain: A systematic literature review and some ideas for application in the algerian context","authors":"Lilia Ghizlene Allal ,&nbsp;Mohammed Bennekrouf ,&nbsp;Belgacem Bettayeb ,&nbsp;M’hammed Sahnoun","doi":"10.1016/j.compag.2025.110171","DOIUrl":"10.1016/j.compag.2025.110171","url":null,"abstract":"<div><div>The global potato supply chain, very important for food security and livelihoods, faces diverse challenges spanning technology, environment, and socio-economics. This paper presents a systematic literature review exploring technology, planning, achievements, challenges, and solutions within the potato supply chain. Emerging technologies hold promise for enhancing traceability, efficiency, and sustainability. However, successful adoption requires addressing economic viability, technical infrastructure, stakeholder acceptance, and regulations. Technological integration influences planning, enabling precise demand forecasting and streamlined logistics. Achievements include reduced waste and improved profitability. Challenges persist due to climate change, water scarcity, pest management, and market fluctuations. In Algeria, potential solutions include precision irrigation, pest management, and collaborative initiatives. This review offers insights for stakeholders, policymakers, and researchers, suggesting future research on decision support systems, pilot studies, and innovative business models promoting circularity within the potato supply chain.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"234 ","pages":"Article 110171"},"PeriodicalIF":7.7,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143628782","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":"Corrigendum to “AI-driven adaptive grasping and precise detaching robot for efficient citrus harvesting” [Comput. Electron. Agricult. 232 (2025) 110131]","authors":"Dong Woon Choi ,&nbsp;Jong Hyeon Park ,&nbsp;Ji-Hyeon Yoo ,&nbsp;KwangEun Ko","doi":"10.1016/j.compag.2025.110268","DOIUrl":"10.1016/j.compag.2025.110268","url":null,"abstract":"","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"234 ","pages":"Article 110268"},"PeriodicalIF":7.7,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143619796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Behavior detection of dairy goat based on YOLO11 and ELSlowFast-LSTM","authors":"Junpeng Zhang ,&nbsp;Zihan Bai ,&nbsp;Yifan Wei ,&nbsp;Jinglei Tang ,&nbsp;Ruizi Han ,&nbsp;Jiaying Jiang","doi":"10.1016/j.compag.2025.110224","DOIUrl":"10.1016/j.compag.2025.110224","url":null,"abstract":"<div><div>Monitoring dairy goat behavior can effectively assess their health status and welfare levels, ensuring both the yield and quality of goat milk. However, achieving accurate and rapid detection of dairy goat behaviors remains challenging. This study proposes a dairy goat behavior detection method based on YOLO11 and ELSlowFast-LSTM (3D-ELA-SlowFast-LSTM) to locate dairy goats and recognize five behaviors: standing, walking, lying down, climbing, and fighting. Firstly, the YOLO11 object detection module is used to pinpoint the locations of dairy goats. Next, the ELSlowFast-LSTM behavior recognition module is introduced to classify behaviors within the detected regions. This module utilizes the SlowFast network for spatiotemporal feature extraction, incorporating the 3D-Efficient Local (EL) attention mechanism specifically designed to enhance the extraction of behavior-related features. Additionally, the Long Short-Term Memory (LSTM) module is applied to model temporal sequence features. Finally, by combining the results of the two modules, the task of dairy goat behavior detection is accomplished. To evaluate the proposed method, we constructed the DairyGoat dataset. Experimental results show that our method achieved a <span><math><mrow><mi>m</mi><mi>A</mi><mi>P</mi></mrow></math></span> value of 78.70%. Additionally, we compared the <span><math><mrow><mi>m</mi><mi>A</mi><mi>P</mi></mrow></math></span> value of our proposed method with other behavior detection models, and the results demonstrate that our method achieves the best detection performance while maintaining a relatively low parameter count and computational load. In summary, this is an effective dairy goat behavior detection method that provides a new strategy for intelligent farming. The dataset and code are available at <span><span>https://github.com/JunpengZZhang/ELSlowFast-LSTM</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"234 ","pages":"Article 110224"},"PeriodicalIF":7.7,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143628780","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":"Optimizing edge-enabled system for detecting green passion fruits in complex natural orchards using lightweight deep learning model","authors":"Hongwei Li ,&nbsp;Jiasheng Chen ,&nbsp;Zenan Gu ,&nbsp;Tianyun Dong ,&nbsp;Jiqing Chen ,&nbsp;Junduan Huang ,&nbsp;Jingyao Gai ,&nbsp;Hao Gong ,&nbsp;Zhiheng Lu ,&nbsp;Deqiang He","doi":"10.1016/j.compag.2025.110269","DOIUrl":"10.1016/j.compag.2025.110269","url":null,"abstract":"<div><div>To address labor shortages and rising costs, developing cost-effective fruit detection technology capable of functioning effectively in complex orchard environments is especially crucial for the advancement of robotic passion fruit harvesting systems. Moreover, achieving edge device-based efficient detection is highly expected under field conditions given its operating portability and cost-effective effects. This study proposed an improved YOLOv8n model for automatic passion fruits detection. First of all, a ParNet attention mechanism was added to the C2f module of YOLOv8n to improve feature extraction. To extract more information about small targets in the images, an additional detection layer was added for small targets in the Neck network. Furthermore, a SlimNeck architecture was employed to optimize the original neck part, reducing the model parameters while maintaining detection performance. The proposed model was trained and tested using a dataset divided by Hold-out, achieving an accuracy of 96.0 %, a recall rate of 83.7 %, and a [email protected] of 91.9 %. The model size was optimal with 2,650,300 parameters, 10.4G FLOPs, and an inference speed of 115fps in Windows-based platform. Compared to the other state-of-the-art deep learning models such as YOLOv4-Tiny, YOLOv5n, YOLOv6n, YOLOv7-Tiny, YOLOv8n, YOLOv9, YOLOv10n, YOLOv11n, Faster R-CNN and SSD, the improved YOLOv8n model showcased overall superior detection performance. When deploying this proposed model on Nvidia Jetson Orin Nano, the inference speed of the improved model was 28.15fps in the C++ environment using the TensorRT API, showing real-time detection performance. This study can provide basic technology for passion fruit robotic harvesting on the basis of the potable edge devices.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"234 ","pages":"Article 110269"},"PeriodicalIF":7.7,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143619911","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}