Computers and Electronics in Agriculture最新文献

{"title":"Multi-temporal remote sensing of ground cover reveals beneficial effects of soil carbon farming in a semi-arid landscape","authors":"Andres Sutton ,&nbsp;Adrian G. Fisher ,&nbsp;David J. Eldridge ,&nbsp;Graciela Metternicht","doi":"10.1016/j.compag.2025.110278","DOIUrl":"10.1016/j.compag.2025.110278","url":null,"abstract":"<div><div>Sustainable land management practices are a strategic tool for addressing land degradation processes that threaten agroecosystem services supply. Currently, carbon credit schemes are important promoters of the adoption of such practices, yet their effectiveness on providing services other than carbon sequestration is not frequently assessed. Particularly, vegetation cover may not only be a mediator of CO₂ fixation, but also act as protection against soil erosion and prevent water quality deterioration. The overarching aim was to develop generalizable methods to assess the effectiveness of sustainable land management practices for maintaining agroecosystem integrity. To achieve this, we assessed the effect of soil carbon sequestration practices on remotely sensed groundcover levels and its stability, and on its response to short-term antecedent accumulated rainfall. These methods were tested in the Cowra Trough, an agricultural region of semi-arid New South Wales, Australia. Time series statistics (mean and standard deviation) and non-parametric tests were used to analyse temporal change in remotely sensed groundcover on paddocks undergoing different land management change intensities. This was complemented with a regional scale analysis of the effect of land use to contextualize paddock-scale results. Moreover, sequential linear regressions of remotely sensed vegetation cover response to antecedent rainfall through a moving temporal window were employed to assess trends in this relationship. A significant effect of land management change was demonstrated: over 90% of the sites implementing sustainable practices had increased and more stable ground cover levels, and the same number (though not the same sites) decreased their ground cover dependence on rainfall. The size of the effect was not related to the intensity of management change implemented for soil carbon sequestration. Land use type proved to be an important spatiotemporal predictor of ground cover and its stability at the Cowra Trough scale with cropping performing worse than grazing systems. Notably, the implementation of carbon farming practices was found to have a more prevalent positive impact on ground cover than on soil carbon contents, suggesting that such practices may provide co-benefits even when no carbon sequestration occurred. This study advances the possibility of monitoring agroecosystem multifunctionality and the development of integrative ‘payment for ecosystem services’ schemes.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"234 ","pages":"Article 110278"},"PeriodicalIF":7.7,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143611084","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":"MFD-YOLO: A fast and lightweight model for strawberry growth state detection","authors":"Haoyan Yang ,&nbsp;Lina Yang ,&nbsp;Thomas Wu ,&nbsp;Yujian Yuan ,&nbsp;Jincheng Li ,&nbsp;Peng Li","doi":"10.1016/j.compag.2025.110177","DOIUrl":"10.1016/j.compag.2025.110177","url":null,"abstract":"<div><div>Strawberry farming requires efficient and adaptable solutions for real-time monitoring to tackle challenges like rapid ripening, perishability, and bad fruit recognition in field applications. However, existing methods often lack the robustness and lightweight design necessary for resource-constrained environments. To address these limitations, we propose MFD-YOLO, a feature-enhanced, distilled neural architecture based on YOLOv7-tiny, for accurate detection of strawberry growth states. First, we developed the MobileNet-MCA (M-MCA) backbone, which enhances feature extraction while significantly reducing redundant computations. Additionally, Partial Convolution (PConv) is incorporated into the E-ELAN module in the neck, improving feature fusion efficiency while reducing parameters. We also proposed the FocusDownNet (FDN) adaptive downsampling method to better capture and fuse multi-scale features. The DepthLiteBlock is designed to replace the CBL module in the prediction layer, further reducing computational complexity. Finally, an adaptive weighted knowledge distillation (AWKD) strategy is employed to balance performance and efficiency. Experimental results demonstrate that MFD-YOLO achieves a [email protected] of 97.5%, precision of 96.5%, recall of 93.8%, and an F1 score of 95.0%, operating at 128 FPS with a model size of only 3.58 MB. The proposed model outperforms state-of-the-art models and is successfully deployed on both desktop and Android devices, enabling real-time, efficient detection in resource-constrained environments.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"234 ","pages":"Article 110177"},"PeriodicalIF":7.7,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143611085","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":"Exploring plant stress-reducing ventilation in greenhouses with plant sensors and decision tree analysis","authors":"Rune Vanbeylen ,&nbsp;Fjo De Ridder ,&nbsp;Herman Marien ,&nbsp;Griet Janssen ,&nbsp;Kathy Steppe","doi":"10.1016/j.compag.2025.110267","DOIUrl":"10.1016/j.compag.2025.110267","url":null,"abstract":"<div><div>In spring and summer, tomato plants grown in greenhouses often experience high levels of (solar) irradiation in a dry atmosphere during the day. On such hot and sunny days, the resulting high transpiration rates greatly deplete the internal water storage pools (i.e., living cells) of the plant, which gives the plant higher daily stress and may result in irreversible plant or fruit damage. To facilitate the replenishment of internal water storage pools of a plant, greenhouse farmers in Belgium and the Netherlands employ a targeted ventilation strategy, which we have dubbed the ‘plant stress-reducing ventilation’ strategy. This is a commonly used, though scientifically largely understudied, technique in greenhouse cultivation. This makes the strategy difficult to master, leaving growers divided on its effectiveness. To better understand and quantify the effects of the stress-reducing ventilation strategy, we equipped tomato plants (<em>Solanum lycopersicum</em> L.) in a commercial Belgian greenhouse with sap flow and stem diameter variation sensors to continuously measure the plant response to the technique. Climate and greenhouse control data were recorded by the climate computer. This plant response was classified and used to generate a decision tree using machine learning, pointing out the most important factors that reduced plant stress when applying the technique. Our approach is novel in the sense that it incorporates plant sensor measurements into a decision tree algorithm for climate control. This integration has proven crucial in comprehending the practical application of the plant stress-reducing ventilation strategy, now better understood from an ecophysiological perspective.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"234 ","pages":"Article 110267"},"PeriodicalIF":7.7,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143619913","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":"Enhanced machine vision system for field-based detection of pickable strawberries: Integrating an advanced two-step deep learning model merging improved YOLOv8 and YOLOv5-cls","authors":"Zixuan He,&nbsp;Manoj Karkee,&nbsp;Qin Zhang","doi":"10.1016/j.compag.2025.110173","DOIUrl":"10.1016/j.compag.2025.110173","url":null,"abstract":"<div><div>In order to successfully deploy robotic harvesting in open field conditions, the development of an effective machine vision system becomes crucial. In this research, we proposed a novel two-step deep learning model consisting of a modified YOLOv8s and a YOLOv5s-cls to accomplish strawberry detection and pickability classification (whether a mature fruit is pickable by a robot). Firstly, the YOLOv8s was enhanced by incorporating C3x modules and an additional head network structure, specifically tailored for accurate strawberry detection. To further improve training performance, the <span><math><mi>α</mi></math></span>-IOU (intersection over union) technique was integrated. Subsequently, the YOLOv5s-cls was utilized to determine suitability of the detected mature strawberries. Through evaluations, Model D (+C3x+head+<span><math><mi>α</mi></math></span>IoU), which was a model based on modifying YOLOv8 using the new modules and techniques mentioned above, was found to perform the best among the tested models achieving the highest AP scores of 84.2% in Stage I (immature), 77.8% in Stage II (nearly mature), and 87.8% in Stage III (mature), along with the highest mAP of 83.2%. Overall, this modified model achieved a 2.5% improvement in mAP compared to the same achieved by original YOLOv8s model. Despite a slightly slower inference speed of 8.4 ms per image, Model D maintains real-time capabilities, making it an optimal choice for strawberry detection. Additionally, YOLOv5s-cls was identified as the preferred model for classifying mature strawberries into pickable and unpickable groups, offering a good inference speed of 2.8 ms per image and comparable accuracy with other compared models including YOLOv8s-cls, ResNet 18, EfficientNet-b0, and EfficientNet-b1. Finally, the combined two-step model developed in this study was evaluated in 10 different field scenarios from a completely different strawberry field that was not used in model training and initial testing. In this validation test the machine vision system achieved an AP of 89.0%, 82.0%, and 90.0% in detecting strawberries from Stage I, II, and III while the classification accuracy was 100.0% in unpickable group and 95.0% in pickable group. The results showed that the developed two-step machine vision system has a potential to improve the overall robotic harvesting system for strawberries grown in open-field conditions.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"234 ","pages":"Article 110173"},"PeriodicalIF":7.7,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143600166","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":"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}