Biosystems Engineering最新文献

{"title":"Shelter efficiency of windbreak influenced by stand configuration","authors":"Ruijie Zhang ,&nbsp;Rui Ma ,&nbsp;Yanjun Ma ,&nbsp;Fu Zhang ,&nbsp;Linyuan Wei ,&nbsp;Gazangzhuoma Xie","doi":"10.1016/j.biosystemseng.2025.104192","DOIUrl":"10.1016/j.biosystemseng.2025.104192","url":null,"abstract":"<div><div>Stand density and planting point configuration are two critical parameters influencing windbreak efficiency. This study investigates how stand density and triangular planting point configurations affect wind speed in <em>Haloxylon ammodendron</em> windbreaks. Wind tunnel experiments were conducted on four windbreaks with varying dimensions: A (17 cm × 17 cm), B₁ (17 cm × 34 cm), B₂ (34 cm × 17 cm), and C (34 cm × 34 cm), corresponding to field densities of 1500, 750, 750, and 375 plants·ha⁻¹, which also represented the planting density, reserved density and natural vegetation density of <em>H. ammodendron</em>, respectively. Results showed faster wind speed variation near windbreaks with higher densities, with windbreak B₁ (row spacing &gt; plant spacing) exhibiting faster variation than B₂ (plant spacing &gt; row spacing). The area of weak wind speed (<em>U/U</em>_<em>0</em> &lt; 0.5) around B₁ was approximately three times that of B₂. Wind reductions downstream the windbreaks were not proportional to stand density, with ratios of A:B:C ≈ 6:3:2. The position of <em>U</em>_<em>min</em> moved further leeward as stand density decreased, accompanied by an increase in <em>U</em>_<em>min</em> values. These results highlight the significant influence of both stand density and planting point configuration on windbreak efficiency, offering valuable insights for optimising windbreak design in arid regions.</div></div>","PeriodicalId":9173,"journal":{"name":"Biosystems Engineering","volume":"256 ","pages":"Article 104192"},"PeriodicalIF":4.4,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144170290","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":"Influence of dietary crude protein and electrolyte balance on nitrogen budget and gas emissions in broilers","authors":"Théophane de Rauglaudre ,&nbsp;Bertrand Méda ,&nbsp;Simon Fontaine ,&nbsp;William Lambert ,&nbsp;Aude Simongiovanni ,&nbsp;Dalila Larios ,&nbsp;Stéphane Godbout ,&nbsp;Sébastien Fournel ,&nbsp;Marie-Pierre Létourneau Montminy","doi":"10.1016/j.biosystemseng.2025.104175","DOIUrl":"10.1016/j.biosystemseng.2025.104175","url":null,"abstract":"<div><div>Nitrogen (N) use efficiency is a major issue in animal production, and improving it can reduce the environmental impact associated with N losses. Here, we studied the effects of dietary crude protein (CP) and dietary electrolyte balance (DEB) on N balance, litter composition and gas emissions. The experiment was conducted in two consecutive replicates of 37 d and 35 d with 432 and 456 one-day-old Ross 308 male chicks. Broilers were randomly distributed in 12 environmentally controlled chambers designed to measure ammonia (NH₃) and nitrous oxide (N₂O) emissions. After a common starter diet (0 d–9 d), four experimental treatments were tested during growing (10 d–20 d) and finishing (21 d to the end of the rearing period) periods: control (CT); low CP and reduced DEB (LCP; −16 and −30 g CP kg⁻¹; −75 and −78 mEq kg⁻¹ in growing and finishing periods); and two low-CP treatments with either potassium (K) carbonate (LCP + K) or sodium (Na) bicarbonate (LCP + Na) supplementation to bring the DEB back to the control level. The results show that the N₂O emissions per broiler were not impacted by dietary treatment, whereas NH₃ volatilisation was reduced in the LCP, LCP + K, and LCP + Na treatments compared with CT (−72%, −47%, and −57%, respectively; P &lt; 0.001). The reduction in NH₃ emissions is due to a reduction in N excretion and volatilisation rate. In fact, the reduction in N excretion accounts for between 35% and 52% of the reduction in NH₃ emissions, depending on the DEB level. Reducing dietary CP and DEB could be used to decrease NH₃ volatilisation in broiler production.</div></div>","PeriodicalId":9173,"journal":{"name":"Biosystems Engineering","volume":"256 ","pages":"Article 104175"},"PeriodicalIF":4.4,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144154423","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":"An end-to-end maturity prediction and hierarchical counting method for cherry tomatoes","authors":"Jiacheng Rong ,&nbsp;Xianjun Li ,&nbsp;Wanli Zheng ,&nbsp;Tongqiang Chen ,&nbsp;Ting Yuan ,&nbsp;Pengbo Wang ,&nbsp;Wei Li","doi":"10.1016/j.biosystemseng.2025.104191","DOIUrl":"10.1016/j.biosystemseng.2025.104191","url":null,"abstract":"<div><div>In vertical farming, accurately and automatically estimating cherry tomato maturity remains an unsolved challenge with significant potential to enhance agricultural efficiency and crop quality. This study addresses the complexities of maturity estimation and occlusion detection in greenhouse environments, where variable lighting and frequent occlusions complicate visual assessments. This work presents a Lightweight Cherry Tomato Multi-Task Detection (CT-MTD) model, a deep learning-based multi-task framework designed to perform maturity estimation and occlusion detection. The model utilises a shared MobileVIT backbone for efficient feature extraction, with task-specific branches for regression-based maturity estimation and classification-based occlusion detection. Coordinate Attention (CA) is integrated before each branch to capture spatial and channel-wise dependencies. To support precise data labelling, a custom annotation tool allowing horticultural experts to annotate maturity stages and occlusion statuses was developed. The experiments demonstrate that the CT-MTD model achieves a RMSE of 0.048 for maturity estimation, and Accuracy and F₁ scores of 0.817 and 0.778 for occlusion detection, validating the effectiveness of the MobileVIT and CA combination for multi-task learning in precision agriculture. Finally, to validate the maturity estimation algorithm in inspection robots, a fruit counting method for tomatoes at different maturity levels was developed. The mean maturity prediction accuracy for tomato counting in two greenhouses was 86.3 % and 80.2 %, demonstrating the model's robustness across different greenhouse conditions. This work can help greenhouse operators anticipate daily output, facilitating market forecasting and efficient logistics for transportation.</div></div>","PeriodicalId":9173,"journal":{"name":"Biosystems Engineering","volume":"256 ","pages":"Article 104191"},"PeriodicalIF":4.4,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144137816","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":"Spatially variant scattering-based single-image dust removal in agriculture","authors":"Peter Buckel ,&nbsp;Thomas Dietmüller ,&nbsp;Timo Oksanen","doi":"10.1016/j.biosystemseng.2025.104176","DOIUrl":"10.1016/j.biosystemseng.2025.104176","url":null,"abstract":"<div><div>Vision systems must deal with various environmental conditions, such as fog, haze, sun, clouds, and snow. A significant challenge in agriculture is the dust raised during soil cultivation. The dust affects image processing systems and algorithms. Like all particles in the atmosphere, dust particles are exposed to light and, therefore, scatter light. Earlier studies have discussed the optics of the atmosphere, identified the scattering process, and developed scene recovery methods. These approaches concentrate on haze removal. However, dust is non-homogeneous and spatially variant. Moreover, dust is raised at different times during the day; thus, the illumination differs. This work addresses the need for a local adaptive scattering model for scene recovery. First, a maximum filter was used to extract the local non-uniform lighting. The final illumination map was further refined with image-guided filtering, which resulted in a blur effect. The blurry map represents the multiple scattering of light through particles. The scene was then recovered on a per-pixel basis. Finally, the proposed method was validated. The validation requires a dataset with images from the same scene with and without dust. A reference-based dataset of nocturnal, dusty, and dust-free images is presented. The results show that the presented approach removes dust with low density but fails if the dust is too dense. Nevertheless, it is visible that dust is significantly reduced, and thus, more information can be extracted.</div></div>","PeriodicalId":9173,"journal":{"name":"Biosystems Engineering","volume":"256 ","pages":"Article 104176"},"PeriodicalIF":4.4,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144134506","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":"Analysis of the seed filling performance influenced by maize seed shape, posture, and suction force","authors":"Zhaodong Li ,&nbsp;Dinglin Ren ,&nbsp;Jialuo Huang ,&nbsp;Jiajie Wu ,&nbsp;Delong Duan ,&nbsp;Tian Zhang ,&nbsp;Yongxin Chen","doi":"10.1016/j.biosystemseng.2025.104186","DOIUrl":"10.1016/j.biosystemseng.2025.104186","url":null,"abstract":"<div><div>This study addresses the issue that during the seed filling process of air-suction seed metering devices, maize seeds are randomly adsorbed by suction holes, leading to various filling postures. This randomness contributes to a significant amount of multiple seeding and leakage. The relationship between the maize seed filling posture and the overall filling performance has remained unclear until now. This research establishes, for the first time, the laws governing the impact of maize seed posture and suction force on seed filling performance. A Computational Fluid Dynamics (CFD) model is developed to simulate maize seeds in different postures within a single-phase airflow field. Subsequently, a validation experiment, using a custom-built suction force measurement device, was conducted to analyse the effects of negative pressure, suction distance, and seed posture on the suction force. The findings indicated that discrepancies between the simulated and experimental results of suction force are within 5 × 10⁻³ N, thereby confirming the accuracy and reliability of the CFD model. Moreover, the relative error of the suction force fitting equations derived from the experimental data was found to be <strong>&lt;</strong> 10 %. The experiment on seed filling performance also determined that a higher proportion of maize seeds lying flat in their filling posture correlated positively with the seed filling pass rate of the metering device.</div></div>","PeriodicalId":9173,"journal":{"name":"Biosystems Engineering","volume":"256 ","pages":"Article 104186"},"PeriodicalIF":4.4,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144123414","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":"Optimised random forest for predicting bed expansion and pressure drop in media filter backwashing","authors":"P.J. García–Nieto ,&nbsp;E. García–Gonzalo ,&nbsp;J. Graciano–Uribe ,&nbsp;G. Arbat ,&nbsp;M. Duran–Ros ,&nbsp;T. Pujol ,&nbsp;J. Puig–Bargués","doi":"10.1016/j.biosystemseng.2025.104189","DOIUrl":"10.1016/j.biosystemseng.2025.104189","url":null,"abstract":"<div><div>In drip irrigation systems, media filters are frequently used to avoid emitter clogging, but periodic backwashing is required to restore media filtration capacity. Backwashing reverses the flow, fluidises the media, and expels trapped particles, being bed expansion (BE) and pressure drop (PD) the key parameters for assessing backwashing hydraulic performance. Available equations and models, however, often fail to reliably predict these parameters under diverse operational conditions. This study introduces a machine learning-based model utilising Random Forest Regression (RFR) and the Whale Optimisation Algorithm (WOA) to predict both BE and PD from a dataset of 705 backwashing runs carried out in the laboratory. For comparison, Lasso, Elastic-net, and Ridge regression models were also implemented with the WOA optimiser. The RFR model was tuned to enhance accuracy by identifying key operational inputs: filter medium type (three categories: silica sand 0.75–0.85 mm, microspheres 0.63–0.75 mm, and silica sand 0.63–0.75 mm), underdrain type (four categories), filter bed height (200 or 300 mm), and superficial velocity. Results showed the WOA/RFR model not only ranked input variable importance but also achieved superior predictive accuracy, with coefficients of determination of 0.9771 and 0.9957 for BE and PD, respectively. The WOA/RFR model consistently outperformed Lasso, Elastic-net, and Ridge models, but demonstrated robust alignment with experimental data. This study presents a novel and optimised approach for predicting bed expansion and pressure drop, enhancing the reliability of media filter backwashing performance assessments in irrigation systems.</div></div>","PeriodicalId":9173,"journal":{"name":"Biosystems Engineering","volume":"256 ","pages":"Article 104189"},"PeriodicalIF":4.4,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144107974","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":"Development of a modified Green–Ampt model considering the unsaturated zone","authors":"Yuchen Li,&nbsp;Weibo Nie,&nbsp;Zhengjiang Feng,&nbsp;Ge Li","doi":"10.1016/j.biosystemseng.2025.104174","DOIUrl":"10.1016/j.biosystemseng.2025.104174","url":null,"abstract":"<div><div>The Green–Ampt (GA) model is widely used for estimating water infiltration in soil. However, the assumptions of a rectangular saturated piston flow model limit its capacity to accurately represent the actual infiltration process. This study developed a modified Green–Ampt (MGA) model that considers the unsaturated zone during infiltration. This model assumes an elliptical distribution for the soil water content in the unsaturated zone and the average unsaturated hydraulic conductivity is equal to that corresponding to the average soil water content. It also offers a reasonable correction term for the average suction head at the wetting front. Results indicate a nonlinear decrease in the thickness ratio of the unsaturated zone to the wetting front. Validation of the MGA model was conducted using laboratory experiments and one-dimensional vertical infiltration simulation for 12 USDA soil textural classes by HYDRUS-1D. Mean absolute error (MAE), mean relative error (MRE), and percentage bias (PB) values were calculated to evaluate model performance. The average MAE, MRE and PB values between the estimated wetting front by MGA model with the measured and simulated values by HYDRUS-1D are 8.8 mm, 6.6 %, −2.7 %, and 20.6 mm, 6.4 %, −4.3 %, respectively. And the average MAE, MRE and PB values for the cumulative infiltration are 2.7 mm, 5.9 %, −3.9 %, and 2.1 mm, 4.0 %, −1.6 %, respectively. The estimated errors of the MGA model were lower than those of the GA model, indicating that the MGA model provides accurate estimates for one-dimensional vertical infiltration and is suitable for a variety of soil textures.</div></div>","PeriodicalId":9173,"journal":{"name":"Biosystems Engineering","volume":"256 ","pages":"Article 104174"},"PeriodicalIF":4.4,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144070883","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 collaborative planning framework for artificial forest harvesting and replanting","authors":"Guanjun Lian ,&nbsp;Huaiqing Zhang ,&nbsp;Kexin Lei ,&nbsp;Hanqing Qiu ,&nbsp;Jie Yang ,&nbsp;Ruihua Yan ,&nbsp;Ningbo Xie ,&nbsp;Haochen Sha","doi":"10.1016/j.biosystemseng.2025.104156","DOIUrl":"10.1016/j.biosystemseng.2025.104156","url":null,"abstract":"<div><div>Achieving precise and efficient selective harvesting-replanting management in artificial forest quality improvement projects faces significant challenges. Random search algorithms, such as Monte Carlo and simulated annealing, are often used to find near-optimal harvesting plans but are inefficient, costly, and poorly scalable, leading to unstable solutions. Additionally, replanting efficiency post-harvest is low and rule-based. The synergistic effects between harvesting and replanting are often overlooked, limiting the potential for forest structure improvement. To address this issue, this study develops a unified harvesting-replanting collaborative planning framework that integrates the novel monarch-based enhanced genetic algorithm (MEGA), a new Delaunay boundary force algorithm (DF-Boundary), and two multi-objective functions. The framework also incorporates 3D visualisation technology for simulation validation and has been applied to eight mingled plots in southern China. Simulation results show that the diameter distribution of the plots follows an inverted-J shape, with improved species diversity. After selective harvesting, the objective function values (<span><math><mrow><msubsup><mi>L</mi><mn>1</mn><mo>∗</mo></msubsup></mrow></math></span>) of 8 plots increased by 38.51 %–198.22 %, outperforming other algorithms in performance and stability. Replanting layout reduced the objective function values (<span><math><mrow><msubsup><mi>L</mi><mn>2</mn><mo>∗</mo></msubsup></mrow></math></span>) of 8 plots by 4.96 %–19.96 %. All spatial structure indicators significantly improved. The results demonstrate that the proposed framework excels in both accuracy and efficiency, enhancing forest structure and moving the forest toward a near-natural state. Further exploration of its application in larger-scale forest ecosystems is needed.</div></div>","PeriodicalId":9173,"journal":{"name":"Biosystems Engineering","volume":"256 ","pages":"Article 104156"},"PeriodicalIF":4.4,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143937879","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":"Unveiling host-seeking behaviour in entomopathogenic nematodes via lab-on-a-chip technology","authors":"Gianluca Manduca ,&nbsp;Valeria Zeni ,&nbsp;Anita Casadei ,&nbsp;Eustachio Tarasco ,&nbsp;Andrea Lucchi ,&nbsp;Giovanni Benelli ,&nbsp;Cesare Stefanini ,&nbsp;Donato Romano","doi":"10.1016/j.biosystemseng.2025.104159","DOIUrl":"10.1016/j.biosystemseng.2025.104159","url":null,"abstract":"<div><div>Entomopathogenic nematodes (EPNs) can be employed as biological control agents (BCAs) for many insect pests’ sustainable management. Despite their widespread use, our understanding of EPNs biology, particularly interactions with their hosts, remains limited. Advancing knowledge of EPNs ecology and host interactions is crucial for optimising their efficacy in pest management. This study pioneers an interdisciplinary approach, at the interface of engineering and applied entomology, to investigate the behaviour of the EPN <em>Steinernema carpocapsae</em>. A novel method combining microfluidics, machine learning, and optical flow is presented. A lab-on-a-chip platform was designed to enable accurate investigation of EPN response to stimuli. A convolutional neural network (CNN) identified nematodes and distinguished their responses to host-derived cues achieving 0.94 accuracy and 1.00 precision in detecting stimulus presence at video-level, classifying EPN behaviour within a controlled environment that simulates host conditions. Optical flow analysis revealed differences in motor activity of EPN upon exposure to stimuli, providing new insights into their dynamic responses. <em>Steinernema carpocapsae</em> exhibited more intense activity in presence of host-borne cues (<em>p</em> = 0.0055). Support vector machine (SVM) and multilayer perceptron (MLP) classifiers distinguished stimulus contexts from optical flow features, with an area under the receiver operating characteristic (ROC) curve of 0.71. These results highlight that, although <em>S. carpocapsae</em> is typically considered an ambusher, it may actively engage in host-seeking behaviour, suggesting a shift in our understanding of its search strategies. This methodology significantly enhances the detection and understanding of EPN responses to cues, advancing their potential in precision biocontrol programs for sustainable pest management actions.</div></div><div><h3>Science4Impact statement (S4IS)</h3><div>This study develops a novel lab-on-a-chip platform integrating artificial intelligence (AI) for the precise investigation of host-seeking behaviours in the entomopathogenic nematode <em>Steinernema carpocapsae</em>, a biological control agent (BCA) with potential for sustainable pest management. By combining microfluidic design with deep learning, the platform accurately assesses nematode responses to host-derived cues, providing new insights into its foraging adaptability beyond conventional techniques. This research can help researchers and agricultural stakeholders by enhancing understanding of BCA behaviour, optimising pest control applications, and informing evidence-based decisions on sustainable crop protection. The findings also support quality assurance in biological control validation by offering a rigorous framework for evaluating nematode effectiveness under realistic conditions, promoting its broader adoption in integrated pest management strategies.</div></div>","PeriodicalId":9173,"journal":{"name":"Biosystems Engineering","volume":"255 ","pages":"Article 104159"},"PeriodicalIF":4.4,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143922093","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":"Experimental study of airflow field distribution on the feature structure of cleaning screen with variable vibration intensity","authors":"Jialiang Zhang ,&nbsp;Fengshuang Liu ,&nbsp;Jun Fu","doi":"10.1016/j.biosystemseng.2025.104172","DOIUrl":"10.1016/j.biosystemseng.2025.104172","url":null,"abstract":"<div><div>The distribution of the airflow field above a vibrating screen plays a critical role in the grain cleaning process. It significantly influences the distribution of agricultural materials on the screen and the cleaning performance. However, there is a notable lack of quantitative studies examining the airflow field distribution above vibrating screens at varying vibration intensities. This study investigates the airflow field distribution above screens with typical characteristic structures used in grain cleaning under different vibration intensities. The results indicate that the chaff screen exhibits superior flow conduction and transverse distribution performance at vibration frequencies. The circular punching screen demonstrates better longitudinal distribution performance and overall distribution uniformity at vibration amplitudes. Furthermore, the woven screen exhibits the smallest variation magnitude in the fluctuation range of airflow vertical velocity over time across different vibration frequencies, with a value of 0.13 m s⁻¹. These findings enhance the understanding of airflow field distribution across various screen types and provide valuable guidance for designing screens optimised to address diverse airflow requirements in grain cleaning applications.</div></div>","PeriodicalId":9173,"journal":{"name":"Biosystems Engineering","volume":"255 ","pages":"Article 104172"},"PeriodicalIF":4.4,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143922094","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}