Computers and Electronics in Agriculture最新文献

{"title":"Past, present and future of deep plant leaf disease recognition: A survey","authors":"Romiyal George ,&nbsp;Selvarajah Thuseethan ,&nbsp;Roshan G. Ragel ,&nbsp;Kayathiri Mahendrakumaran ,&nbsp;Sivaraj Nimishan ,&nbsp;Chathrie Wimalasooriya ,&nbsp;Mamoun Alazab","doi":"10.1016/j.compag.2025.110128","DOIUrl":"10.1016/j.compag.2025.110128","url":null,"abstract":"<div><div>Agriculture is the foundation of life that faces numerous daily attacks from nature and living organisms. A major challenge for farmers is timely plant disease identification, which is crucial to prevent productivity losses and the production of poor-quality products. Researchers have recently been focusing on automating the plant leaf disease recognition process using computer vision and machine learning techniques. More importantly, the recent developments in deep learning have significantly advanced the field of plant leaf disease recognition. Regardless of these advancements, significant challenges remain in automatic leaf disease recognition, and researchers are continuing to seek better performance, in-field applicability, and compatibility with resource-constrained devices. This survey provides a comprehensive overview of real-world and laboratory datasets, feature extraction methods, deep learning frameworks, limitations, recommendations, and future directions for deep plant leaf disease recognition. It offers a detailed comparative analysis of various deep learning models applied to different datasets, preprocessing techniques, and data collection methods. This work also highlights the need for an ideal dataset and explores future directions like the Internet of Things integration, Explainable AI, and Smart Farming, which previous surveys have not covered. The primary aim of this survey is to assist researchers in understanding state-of-the-art plant leaf disease recognition techniques, support farmers in the field of plant pathology, address limitations, provide recommendations and outline future directions.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"234 ","pages":"Article 110128"},"PeriodicalIF":7.7,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143611086","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":"Adaptive modeling method integrating slime mould algorithm and cascade ensemble: Nondestructive detection of silage quality under VIS-NIRS","authors":"Kai Zhao ,&nbsp;Haiqing Tian ,&nbsp;Jue Zhang ,&nbsp;Li’na Guo ,&nbsp;Yang Yu ,&nbsp;Haijun Li","doi":"10.1016/j.compag.2025.110247","DOIUrl":"10.1016/j.compag.2025.110247","url":null,"abstract":"<div><div>Rapid and scientific evaluation of silage quality is essential for livestock farming. The aim is fast, large-scale, and non-destructive detection of silage pH and quality grades. The Slime Mould Algorithm (SMA) was integrated with a cascade ensemble (cascading) to create an intelligent and adaptive modeling algorithm (SMA-configured cascading). Firstly, visible-near-infrared spectra of aerobically deteriorated silage were collected and preprocessed. Secondly, SMA was employed to mine spectral features. Finally, SMA-configured cascading was applied for adaptive modeling by configuring the learners. The results demonstrated that 39 features extracted by SMA performed optimally regarding predictive effectiveness compared to two benchmark algorithms. These features effectively captured key quality information whereas avoiding interference. The SMA-configured cascading achieved the best prediction accuracy for silage quality, outperforming conventional adaptive-based and single-learner-based modeling methods. For pH prediction, the <span><math><msubsup><mi>R</mi><mrow><mi>p</mi></mrow><mn>2</mn></msubsup></math></span>, <em>RMSE_P</em>, <em>MAE_P</em>, <em>MAPE_P</em>, <em>RPD</em>, <em>configuration time</em> (<em>ET_con</em>), and <em>prediction time</em> (<em>ET_pre</em>) of the prediction set were 0.9954, 0.1020, 0.0750, 1.6836 %, 14.9808, 19070 s, and 20.98 s, respectively. The optimal cascading configuration was Partial Least Squares Regression (PLSR), PLSR, support vector machine (SVM), and SVM. For quality grade determination, the <em>Accuracy_p</em>, <em>F1-score_p</em>, <em>ET_con</em>, and <em>ET_pre</em> of the prediction set were 86.11 %, 0.8639, 3097.47 s, and 21.49 s, respectively, with the configured cascading being adaptive boosting and K-nearest neighbor. The proposed method enables efficient and adaptive modeling based on spectral features, optimizing quality prediction. It holds the potential for in-situ detection through offline configuration and online prediction. This study provides theoretical and technical support for the rapid assessment of silage quality in production environments.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"234 ","pages":"Article 110247"},"PeriodicalIF":7.7,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143600366","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":"Screening drought-resistant and water-saving winter wheat varieties by predicting yields with multi-source UAV remote sensing data","authors":"Xu Liu ,&nbsp;Han Yang ,&nbsp;Syed Tahir Ata-Ul-Karim ,&nbsp;Urs Schmidhalter ,&nbsp;Yunzhou Qiao ,&nbsp;Baodi Dong ,&nbsp;Xiaojun Liu ,&nbsp;Yongchao Tian ,&nbsp;Yan Zhu ,&nbsp;Weixing Cao ,&nbsp;Qiang Cao","doi":"10.1016/j.compag.2025.110213","DOIUrl":"10.1016/j.compag.2025.110213","url":null,"abstract":"<div><div>The uneven spatial and temporal distribution of precipitation poses significant challenges to the growth and development of winter wheat. Screening drought-resistant and water-saving winter wheat varieties in water-limited regions is crucial for increasing crop production. However, quickly screening suitable cultivars remains a challenge. Utilizing unmanned aerial vehicles (UAVs) for remote sensing (RS) offers a solution by enabling the prediction of yields, overcoming issues such as the labor-intensive process of manual yield data collection and the difficulty of screening during the growing season. In this study, three types of water treatments were applied to 48 varieties screened in the North China Plain, with each water treatment repeated three times using a randomized block design. The aim is to explore the potential of UAVs for non-destructive yield prediction at various crop growth stages by integrating UAVs-based RS with machine learning, while also screening for drought-resistant and water-saving variety based on predicted yields, actual evapotranspiration (ET) derived from soil water balance and water use efficiency (WUE) at grain yield level. The results indicate that the random forest regression (RFR) model achieved the best prediction results. The optimal data combination of RS, canopy temperature, and data of variety by using RFR yielded the highest coefficient of determination (R²). Additionally, the RFR performs best when using data from the mid-filling stage (single-stage data) and the entire growth stage data (multi-stage data), with R² 0.58 and 0.69, respectively. Among the varieties, Malan 1 and Jimai 765 ranked first and second in both predicted and measured yield assessments, indicating the reliability of the yield prediction model for top-performing varieties. By combining predicted yields from RFR with ET, the screening results demonstrated high consistency between predicted and measured yields. Notably, even yield prediction models with lower R² can still provide satisfactory screening results. These findings will contribute to screening drought-resistant and water-saving winter wheat varieties by UAV. This research accelerates the variety screening process and addresses the conflict between agricultural production and water scarcity in the North China Plain.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"234 ","pages":"Article 110213"},"PeriodicalIF":7.7,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143611212","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":"BEGV2-UNet: A method for automatic segmentation and calculation of backfat and eye muscle region in pigs","authors":"Wenzheng Liu ,&nbsp;Tonghai Liu ,&nbsp;Jinghan Cai ,&nbsp;Zhihan Li ,&nbsp;Xue Wang ,&nbsp;Rui Zhang ,&nbsp;Xiaoyue Seng","doi":"10.1016/j.compag.2025.110272","DOIUrl":"10.1016/j.compag.2025.110272","url":null,"abstract":"<div><div>Rapid and accurate measurements of eye muscle area and backfat thickness in breeding pigs is crucial for improving breeding traits. Within reasonable ranges, these traits significantly influence the number of piglets born, their birth weights, and survival rates. Traditional detection methods are time-consuming and heavily reliant on operational expertise. While B-mode ultrasound is widely used as a non-invasive tool for measuring backfat thickness and eye muscle area, its efficiency and precision are limited by dependence on the operator.</div><div>To address these issues, this study introduces the BEGV2-UNet model, an innovative UNet network based on reconstructing down-sampling and up-sampling paths, incorporating GhostModuleV2, and incorporating a large kernel attention mechanism to better capture the boundaries and positions of backfat and eye muscle regions. The model can be used to segment these regions in breeding pigs and improve the loss function for accelerate convergence while remedying the low precision caused by class imbalance. Using a dataset of ultrasound images, the BEGV2-UNet model achieved an MIoU of 96.18 % and MPA of 98.12 %, with model size reduced to 18.69 MB and strong inference accuracy. We calculated the backfat thickness and eye muscle area using the model to achieve R² values of 0.98 and 0.96, respectively.</div><div>This study highlights the significant advantages of BEGV2-UNet in terms of image segmentation accuracy and lightweight design.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"234 ","pages":"Article 110272"},"PeriodicalIF":7.7,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143611082","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":"Agricultural large language model for standardized production of distinctive agricultural products","authors":"Wenlong Yi ,&nbsp;Li Zhang ,&nbsp;Sergey Kuzmin ,&nbsp;Igor Gerasimov ,&nbsp;Muhua Liu","doi":"10.1016/j.compag.2025.110218","DOIUrl":"10.1016/j.compag.2025.110218","url":null,"abstract":"<div><div>To address the diverse nature of specialty agricultural product standardization, its complex and cumbersome development process, and lengthy drafting cycles, while simultaneously tackling challenges such as outdated standardization documents and hallucinations caused by general large language models’ delayed access to agricultural domain information. This study constructs a multi-stage cascaded large language model based on a hybrid retrieval-augmented mechanism. The model comprises three core modules: (1) A multi-source retrieval augmentation module that achieves comprehensive external knowledge acquisition through vector retrieval, keyword retrieval, and knowledge graph retrieval branches; (2) A knowledge fusion module that filters redundant information using inverse ranking fusion and graph structure pruning methods to achieve precise injection of high-quality knowledge; (3) A domain adaptation module that enhances the model’s understanding of agricultural terminology through vertical domain fine-tuning. Experimental results show that in the standardization document summarization task, the model achieves chrF, BERTscore, and Gscore metrics of 34.85, 74.88, and 39.85, respectively, representing improvements of 59.52%, 35.28%, and 72.84% over the BART baseline model, and 58.54%, 24.25%, and 59.54% over the T5 model. This study enriches the theoretical foundation of large language models in agriculture and provides intelligent technical support for specialty agricultural product standardization development.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"234 ","pages":"Article 110218"},"PeriodicalIF":7.7,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143600866","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":"Effects of structure and soil parameters on the detection performance of a contact soil surface height detection device","authors":"Haitao Peng ,&nbsp;Hanping Mao ,&nbsp;Mohamed Farag Taha ,&nbsp;Luhua Han ,&nbsp;Zhiyu Zuo ,&nbsp;Guoxin Ma","doi":"10.1016/j.compag.2025.110242","DOIUrl":"10.1016/j.compag.2025.110242","url":null,"abstract":"<div><div>The complex environment of the soil surface in the field poses severe challenges to contact soil surface height detection devices, as the device's vibration and soil subsidence can introduce detection errors. To address these problems, a contact soil surface height detection device based on an angle sensor was designed in this study. The kinematic and dynamic relationships between the device and the soil during the detection process were analyzed, and a dynamic model of the detection device based on the soil-machine system was established. The dynamic process of ‘soil excitation → device vibration → soil subsidence’ during detection was revealed. The Kelvin model was used to describe the transient subsidence process of the ground wheel, and the model's parameters under different soil moisture contents were experimentally determined with a coefficient of determination (<em>R</em>²) of 0.85 ∼ 0.97. To investigate the influence of soil moisture content and device structural parameters (inertia parameter (<em>J</em>), initial angle (<em>γ</em>₀), prepressure of spring (<em>F_t</em>₀), and spring stiffness coefficient (<em>k</em>)) on the detection results, a simulation model was established using MATLAB/Simulink to simulate the interaction between the detection device and the soil during detection based on the proposed dynamic model, and the simulation results were validated experimentally. The peak overshoot percentage (<em>σ</em>) and steady-state error percentage (<em>Ess</em>) were used as indices. The experimental and simulation indices exhibited a strong linear relationship with a linear regression coefficient of 0.82 ∼ 0.99, confirming the validity of the established model. The results obtained in this study can provide theoretical and technical support for the design, optimization, compensation, and control of contact detection and soil pressure devices with similar structures.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"234 ","pages":"Article 110242"},"PeriodicalIF":7.7,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592078","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":"Next generation crop protection: A systematic review of trends in modelling approaches for disease prediction","authors":"Alison Jensen ,&nbsp;Philip Brown ,&nbsp;Karli Groves ,&nbsp;Ahsan Morshed","doi":"10.1016/j.compag.2025.110245","DOIUrl":"10.1016/j.compag.2025.110245","url":null,"abstract":"<div><div>Digital agriculture tools and advances in modelling approaches have the potential to deliver precise decision support systems for more effective, efficient and sustainable crop disease management. Historically, disease prediction in agriculture has relied on knowledge of the relationships between a few key environmental parameters and crop disease development. The emergence of new sensor technologies is now expanding the range of input data readily accessible for use in modelling. In addition, Artificial Intelligence (such as machine learning and deep learning algorithms) offers the capacity to process the large datasets available from a wider range of input variables relating to the three components of disease development: host, pathogen and environment. This review examined the rate and extent to which machine learning has replaced traditional modelling approaches for disease predictive model development. A systematic protocol was developed to investigate trends in modelling approaches for disease prediction in four major crop types: cereals, grape, potato and citrus. A total of 104 publications, reporting on the development of 146 disease predictive models were evaluated for modelling approach, data inputs and model performance. The results from this review indicate that the application of machine learning for predictive model development has greatly increased over the past two decades. Increased application of machine learning models (including Support Vector Machine and Random Forest) was associated with the development of more high-performance models and incorporation of higher numbers of predictor variables. The potential of deep learning models to deliver more precise and adaptable models for next generation disease management will be determined by applying these methods to large datasets. Further research is needed to investigate multi-model, machine learning approaches for disease prediction and to ensure model design captures important input variables relating to environment, host and pathogen.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"234 ","pages":"Article 110245"},"PeriodicalIF":7.7,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592065","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":"A systematic literature review on the applications of federated learning and enabling technologies for livestock management","authors":"R.J. Garro ,&nbsp;C.S. Wilson ,&nbsp;D.L. Swain ,&nbsp;A.J. Pordomingo ,&nbsp;S. Wibowo","doi":"10.1016/j.compag.2025.110180","DOIUrl":"10.1016/j.compag.2025.110180","url":null,"abstract":"<div><div>This paper conducts a systematic review of the literature on the application and integration of federated learning, blockchain technology, and the Internet of Things (IoT) in livestock management. To achieve this objective, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology was applied, guiding the review process to ensure transparency and comprehensiveness. Extensive searches were carried out from five academic databases, such as ScienceDirect, IEEE Xplore, Springer, Multidisciplinary Digital Publishing Institute (MDPI), and Association for Computing Machinery (ACM). A total of 1,259 articles were reviewed and 20 articles were finally selected for analysis. The study reveals that there is limited research on the integration of federated learning, blockchain technology, and the IoT in the livestock sector. However, these technologies have application in the sector for improving efficiency, optimizing crop and animal management, and promoting environmentally sustainable practices. The study suggested that several key issues need to be considered for using these technologies such as the protection of data privacy, the management of information diversity, and restrictions on connectivity, as well as the need to motivate cooperation and commitment between different stakeholders in the sector. This study provides a reference for researchers on the usefulness of these technologies for increasing efficiency and transparency in livestock management.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"234 ","pages":"Article 110180"},"PeriodicalIF":7.7,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592067","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":"The role of spectro-temporal remote sensing in vegetation classification: A comprehensive review integrating machine learning and bibliometric analysis","authors":"Arif Ur Rehman ,&nbsp;Abdur Raziq ,&nbsp;Bhaskar Shrestha ,&nbsp;Kim-Anh Nguyen ,&nbsp;Yuei-An Liou","doi":"10.1016/j.compag.2025.110184","DOIUrl":"10.1016/j.compag.2025.110184","url":null,"abstract":"<div><div>Spectro-temporal remote sensing (STRS) is a reliable source for mapping and monitoring earth’s surface dynamics. This review investigates the role of STRS in Vegetation Classification (VC) by analyzing 159 articles from Web of Science Core Collection (WSCC) database, spanning from 1980 to 2023. By integrating machine learning and bibliometric analysis, it provides comprehensive examination of trends, themes and advancements in the application of STRS in VC. The findings indicate significant growth in the use of STRS for VC, highlighted an exponential increase in publications over time. Recently, commonly used classification methods include machine learning, deep learning and spectral matching techniques, with research themes covering crop types, agricultural land, and forests. Notably, the study underscores the dominance of the USA and China in both publication quantity and collaborative efforts, reflecting their leadership in this field. Frequently utilized STRS data sources include MODIS, Landsat and Sentinel-1/2 sensors. Furthermore, the review emphasizes the need for developing flexible frameworks that integrate spectro-temporal data and accuracy evaluation metrics to build robust, intelligent, and transfer-learning classification methods. Overall, this review sheds light on the role of STRS in VC and provides valuable insights for researchers and decision-makers involved in vegetation monitoring and mapping. It emphasizes the potential of STRS to revolutionize VC and outlines directions for further research to address existing challenges and capitalize on emerging opportunities in this rapidly growing field.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"234 ","pages":"Article 110184"},"PeriodicalIF":7.7,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592066","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 novel approach to water stress assessment in plants: New bioimpedance method with PSO-optimized Cole-Cole impedance modeling","authors":"Flórián Kovács ,&nbsp;Ákos Odry ,&nbsp;Zoltán Vizvári ,&nbsp;Sundoss Kabalan ,&nbsp;Enikő Papdi ,&nbsp;Péter Odry ,&nbsp;Katalin Juhos","doi":"10.1016/j.compag.2025.110167","DOIUrl":"10.1016/j.compag.2025.110167","url":null,"abstract":"<div><div>In order to characterize plant water deficiencies, this paper presents a custom-developed bioimpedance (BIS) measurement setup designed for <em>in vivo</em> studies that extracts plant leaf parameters using a novel optimization approach based on the particle swarm optimization (PSO) algorithm. The system performs, four-electrode measurements on plant leaves and employs a custom multi-objective cost function to validate parameters for the Double Shell Cole-Cole model. The experiment consisted of two parts: first, pepper plants (<em>Capsicum annuum</em> L.) as a model plant were exposed to drought stress in a light chamber, and their impedance and physiological parameters were measured. In the second part of the experiment, detached pepper leaves were allowed to dry naturally, and impedance measurements were recorded at hourly and tri-hourly intervals. Impedance spectrum measurements from 230 samples (1 Hz to 100 kHz), collected during both experiments, demonstrated that extracellular fluid resistance increases linearly with water loss. The proposed PSO-optimized Double Shell model showed a stronger correlation between extracellular fluid resistance and water loss compared to the widely used Zfit algorithm, which exhibited higher coefficient of variation in the Cole-Cole parameters. Both algorithms showed a significant negative correlation between relative water content and extracellular fluid resistance, but only the proposed PSO-based model detected a relationship between cell membrane capacity and membrane stability index. Additionally, extracellular fluid resistance correlated with photosynthetic efficiency. The results highlight the effectiveness of impedance measurements for assessing plant water status and support the reliability of proposed PSO-based optimization for bioimpedance analysis.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"234 ","pages":"Article 110167"},"PeriodicalIF":7.7,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592062","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}