Computers and Electronics in Agriculture最新文献

{"title":"Satellite vs uncrewed aircraft systems (UAS): Combining high-resolution SkySat and UAS images for cotton yield estimation","authors":"Benjamin Ghansah ,&nbsp;Jose L. Landivar Scott ,&nbsp;Lei Zhao ,&nbsp;Michael J. Starek ,&nbsp;Jamie Foster ,&nbsp;Juan Landivar ,&nbsp;Mahendra Bhandari","doi":"10.1016/j.compag.2025.110280","DOIUrl":"10.1016/j.compag.2025.110280","url":null,"abstract":"<div><div>Uncrewed Aircraft Systems (UAS) are widely used for crop growth monitoring and yield estimation in Precision Agriculture (PA). However, UAS are limited by their relatively small area coverage, high cost, and high data processing needs. High resolution satellites (such as SkySat) are valuable alternatives to UAS in PA. Nonetheless, persistent cloud cover, especially in regions like the South of Texas, limits their utility. This study compared and explored the integration of satellite and UAS imagery for cotton yield estimation. The rationale was to determine the best performing platform among the two, as well as leverage their synergy to mitigate data gaps caused by persistent cloud cover. Using deep learning model, vegetation indices derived from SkySat and P4M (Phantom 4 Multispectral) images were correlated with crop yield data collected during the 2023 season. Results demonstrated that SkySat slightly outperformed P4M in yield estimation, with median accuracies of R² = 0.81 and RMSE = 0.20 ton/ha for SkySat, compared to R² = 0.80 and RMSE = 0.21 ton/ha for P4M. More importantly, when all the SkySat and P4M datasets were combined, accuracy improved by 3 % compared to SkySat-only data. In addition, data collected between 74 and 114 days after planting contributed most significantly to yield prediction. The fusion approach used in this study allows for better spatial and temporal coverage, ultimately enhancing yield prediction reliability in PA. Future research should explore the inclusion of additional sensors such as Synthetic Aperture Radar (SAR) and thermal imagery, which could further improve yield prediction accuracy, especially in cloud-prone regions.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"234 ","pages":"Article 110280"},"PeriodicalIF":7.7,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143724240","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":"Neural network estimation of thermal conductivity across full saturation for various soil types","authors":"Yongwei Fu ,&nbsp;Robert Horton ,&nbsp;Joshua Heitman","doi":"10.1016/j.compag.2025.110321","DOIUrl":"10.1016/j.compag.2025.110321","url":null,"abstract":"<div><div>Soil thermal conductivity (λ) relates directly to heat conduction in soil. Numerous models have been developed to estimate soil thermal conductivity, but their applicability is often limited to specific types of soils. Recognizing the similarity between the soil water retention curve and the λ versus water content (θ) curve, Lu and Dong presented a λ(θ) model, which can provide accurate λ estimates for various soils but does not converge to the thermal conductivity value of a saturated soil (λ_sat) at saturation. In this study, we develop a modified form of the Lu and Dong (MLD) model. Additionally, we present a neural network (NN) approach to estimate parameters of the MLD model using soil porosity, sand, silt, and clay contents, as well as the thermal conductivity of soil solids (λ_s) as input features. The neural network is trained to optimize the hyperparameters, which are used to establish the NN-MLD model after the hyperparameter tuning process is completed. The NN-MLD model is then tested with an independent testing dataset and compared with five pre-existing models taken from the literature. Results show that the NN-MLD model outperforms the other models across four error metrics with a normalized root mean square error (NRMSE) of 0.049, a mean absolute error (MAE) of 0.098 W m⁻¹ K⁻¹, an Akaike’s information criterion (AIC) of −1699 and a coefficient of determination (R²) of 0.94. In addition, error analysis across varying degrees of saturation (<em>S</em>) reveals that the NN-MLD model consistently outperforms the other models across the entire range of saturation levels and its superiority is most pronounced at medium levels of saturation, where the other models yield NRMSEs and MAEs values three times larger than those of the NN-MLD model. The NN-MLD model is available in Python code in the <span><span>Supplementary Material</span></span>.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"235 ","pages":"Article 110321"},"PeriodicalIF":7.7,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143715644","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":"Soil zinc content estimation using GF-5 hyperspectral image with mitigation of soil moisture influence","authors":"Songtao Ding ,&nbsp;Weihao Wang ,&nbsp;Weichao Sun ,&nbsp;Yaqiong Zhang ,&nbsp;Youxin Sun ,&nbsp;Xia Zhang ,&nbsp;Wenliang Chen ,&nbsp;Arif UR Rehman","doi":"10.1016/j.compag.2025.110318","DOIUrl":"10.1016/j.compag.2025.110318","url":null,"abstract":"<div><div>Hyperspectral imagery has a high potential for large-area estimation of soil heavy contents. However, soil moisture significantly influences spectral analysis accuracy, which many existing studies on soil metal estimation have overlooked. This study investigates the impact of soil moisture on the characteristic spectral range of Soil Spectrally Active Constituents (SSAC) by analyzing soil spectra under varying moisture conditions. Based on this analysis, the SSAC characteristic bands were identified and subjected to segmented Orthogonal Signal Correction (OSC)to mitigate moisture influence. Then, a stacking ensemble model was constructed based on the corrected SSAC bands. A total of 105 soil samples were collected from the Dongsheng coalfield mining area in the Inner Mongolia Autonomous Region, China, alongside Chinese Gaofen-5 (GF-5) satellite hyperspectral imagery acquired simultaneously. The results demonstrate that the segmented OSC can effectively mitigate the influence of soil moisture when moisture is 15% or less. After applying the segmented OSC, the accuracy R² of the test set is improved significantly from 0.0508 to 0.7697. Additionally, the stacking ensemble model outperformed conventional single models, demonstrating superior accuracy in estimating soil heavy metal content. The use of SSAC characteristic bands also reduced model overfitting. The estimated spatial distribution of soil zinc (Zn) content in the study area is accurate and reasonable, indicating high reliability and applicability of the proposed method. This approach provides a robust solution for precise soil metal estimation under varying moisture conditions.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"235 ","pages":"Article 110318"},"PeriodicalIF":7.7,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716218","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 multi-machine path planning for crop precision seeding with Lovebird Algorithm","authors":"Amalia Utamima ,&nbsp;Miftakhul J. Sulastri ,&nbsp;Lidiya Yuniarti ,&nbsp;Amir H. Ansaripoor","doi":"10.1016/j.compag.2025.110207","DOIUrl":"10.1016/j.compag.2025.110207","url":null,"abstract":"<div><div>This paper investigates path planning in agriculture, with a specific focus on the seeding process. It underscores the crucial role of path planning in enhancing the efficiency and productivity of agricultural machinery operations. The research is centered on minimizing the operational times for agricultural robots, encompassing sowing activities and auxiliary travel periods. The study compares the effectiveness of the Lovebird Algorithm against the Genetic Algorithm (GA) and Ant Colony Optimization (ACO) in optimizing routes for precision seeding across various field layouts, addressing a range of geometric and operational challenges. The proposed Lovebird Algorithm demonstrates a runtime efficiency approximately three times faster than GA and one and a half times faster than ACO. Furthermore, it consistently reduces auxiliary travel distances by 14% compared to GA and 28% compared to ACO in the crop-seeding scenario. The findings align with the objectives of precision seeding by efficiently guiding machinery, thereby reducing travel-time and auxiliary travel distances. The proposed algorithm exhibits efficient computational performance, suggesting its suitability for time-sensitive agricultural operations that demand timely decision-making. Overall, the results have the potential to provide a tool that conserves resources and enhances efficiency in the agricultural sector, contributing to future advancements in precision agriculture technology.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"235 ","pages":"Article 110207"},"PeriodicalIF":7.7,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716219","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":"ODP: A novel indicator for estimating photosynthetic capacity and yield of maize through UAV hyperspectral images","authors":"Shaolong Zhu ,&nbsp;Tianle Yang ,&nbsp;Dongwei Han ,&nbsp;Weijun Zhang ,&nbsp;Muhammad Zain ,&nbsp;Qiaoqiao Yu ,&nbsp;Yuanyuan Zhao ,&nbsp;Fei Wu ,&nbsp;Zhaosheng Yao ,&nbsp;Tao Liu ,&nbsp;Chengming Sun","doi":"10.1016/j.compag.2025.110350","DOIUrl":"10.1016/j.compag.2025.110350","url":null,"abstract":"<div><div>Rapid and accurate monitoring of photosynthetic indicator is of great significance for understanding crop growth and development, and predicting yield. Hyperspectral imagery has become a powerful tool for evaluating photosynthetic capacity due to its non-destructive nature in sensing crop radiation. Most photosynthetic indicators have instantaneous ideal values, which cannot fully reflect the photosynthetic capacity of crop populations in field environments. This study introduces a novel indicator “one day photosynthesis” (ODP) based on the various photosynthetic indicators including net photosynthetic rate (Pn), stomatal conductance (Gs), internal CO₂ concentration (Ci), and transpiration rate (Tr). We performed trend fitting on the time-series photosynthetic indicators obtained at a frequency of two hours, and then calculated the projection area of the fitting curve on the time axis. Later on, the ODP was calculated by assigning weight to the projection area using the CRITIC and correlation method, and the feasibility of ODP was tested using the growth of hundred-grain weight (HGW). Finally, we constructed the ODP estimation model based on canopy hyperspectral data, and further estimated the yield. The results showed that the correlation coefficients between ODP and the growth of HGW were 0.831, 0.882, 0.856, and 0.833 at 10, 20, 30, and 40 days after flowering, respectively. The R² of the ODP estimation model based on hyperspectral vegetation indices (VIs) in the four growth stages were 0.71, 0.83, 0.79, and 0.75, respectively. Moreover, ODP also showed high accuracy and adaptability in different sites, years, sowing dates, and cultivars. We noticed that ODP also has good accuracy in estimating the maize yield, as the R² of estimated yield on the base of measured and estimated ODP was 0.770 and 0.716 respectively. Furthermore, the VIs screened by ODP modeling can also be used for yield estimation, and this VIs screening method is superior to the yield estimation model built based on the correlation between VIs and yield. This study findings provides a novel insight regarding the new ODP indicator that has potential application prospects for efficient estimation of maize photosynthetic capacity and yield.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"235 ","pages":"Article 110350"},"PeriodicalIF":7.7,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716221","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":"Plant recognition and counting of Amorphophallus konjac based on UAV RGB imagery and deep learning","authors":"Ziyi Yang ,&nbsp;Kunrong Hu ,&nbsp;Weili Kou ,&nbsp;Weiheng Xu ,&nbsp;Huan Wang ,&nbsp;Ning Lu","doi":"10.1016/j.compag.2025.110352","DOIUrl":"10.1016/j.compag.2025.110352","url":null,"abstract":"<div><div>Quantifying the number of Amorphophallus konjac (Konjac) plants can provide valuable insights for yield prediction. Early monitoring of the plant population facilitates timely adjustments in cultivation practices, ultimately leading to improved productivity of Konjac. The majority of research employed deep learning (DL) for plant counting using original images derived from unmanned aerial vehicle (UAV) or ground-based platforms, but this method may lack adaptability to different scenarios and face challenges in achieving plant counting over large areas. This study systematically evaluated the performance of UAV-based original images, the generated orthomosaic, and the combination of both for the detection and counting of the Konjac plant. We proposed an innovative approach by integrating three Convolutional Block Attention Modules (CBAM) into YOLOv5 and utilizing the combined dataset of original images and orthomosaic, which exhibited the highest accuracy performance in Konjac plants recognition (Precision = 94.3 %, Recall = 96.0 %, F1-Score = 95.1 %). Our findings illustrate that the orthomosaic generated from original images acquired via UAV outperformed individual original images in terms of accuracy for counting Konjac plants across expansive areas. This study provides new insight into the recognition and counting of various crop plants across large-scale regions, presenting a practical and efficient approach.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"235 ","pages":"Article 110352"},"PeriodicalIF":7.7,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716217","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":"Multimodal weed infestation rate prediction framework for efficient farmland management","authors":"Yang Huang ,&nbsp;Xingcai Wu ,&nbsp;Zhenbo Liu ,&nbsp;Qi Wang ,&nbsp;Shichao Jin ,&nbsp;Chaoyang Xie ,&nbsp;Gefei Hao","doi":"10.1016/j.compag.2025.110294","DOIUrl":"10.1016/j.compag.2025.110294","url":null,"abstract":"<div><div>Weed, as one of the main hazards of agricultural production, is being widely studied for efficient field management via Multi-spectral sensors. In the field of precision weed control, the weed infestation rate is an important indicator of weed damage, which has been predicted by various methods and attempts to be applied to guide pesticide spraying via UAVs. However, existing prediction methods not only face the problem of scarcity of data types, but most of them also require pixel-level labeling, which makes them difficult to apply practically. It is also challenging to deal with the lack of consistency in multimodal data, which leads to an inability to quantify differences in characteristics between weeds and crops. To address the above problems, we collect a multimodal database (PWMD) of early pepper weeds containing 1495 pairs of visible and infrared images using a UAV and a multispectral camera. Moreover, we further design a multimodal weed infestation rate prediction system (MWPS) to achieve efficient performance in the field. In detail, MWPS implements dual-path generative adversarial learning and a multilevel feature matching module to mitigate modal differences between multimodal images and utilizes a multilayer perceptron model containing dual attention to achieve efficient weed infestation rate prediction. Experimentally validate on our dataset, our proposed framework has a mean square error of 0.12 and a mean absolute error of only 0.09 for the prediction of field weed rates. This study proposes an effective new method for distal field weed management. Code and dataset are available at <span><span>http://wirps.samlab.cn</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"235 ","pages":"Article 110294"},"PeriodicalIF":7.7,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716222","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 collaborative scheduling and planning method for multiple machines in harvesting and transportation operations-part Ⅱ: Scheduling and planning of harvesters and grain trucks","authors":"Ning Wang ,&nbsp;Shunda Li ,&nbsp;Jianxing Xiao ,&nbsp;Tianhai Wang ,&nbsp;Yuxiao Han ,&nbsp;Hao Wang ,&nbsp;Man Zhang ,&nbsp;Han Li","doi":"10.1016/j.compag.2025.110344","DOIUrl":"10.1016/j.compag.2025.110344","url":null,"abstract":"<div><div>In Part Ⅰ of this two-part paper (A Collaborative Scheduling and Planning Method for Multiple Machines in Harvesting and Transportation Operations—Part Ⅰ: Harvester Task Allocation and Sequence Optimization), the primary focus was to address the issue of collaborative scheduling for harvesters through task allocation and whole-process path planning. In this paper (Part II), the emphasis shifts to addressing the collaborative scheduling and planning of both harvesters and grain trucks while considering the efficiency of grain trucks. First, a novel algorithm named the Headland area Unloading-based Harvester Unloading Point Generation and Adjustment algorithm (HU-HUPGA) was proposed, which can generate and adjust the position of unloading points based on the harvester’s operational path. This method can effectively reduce the complexity of grain truck paths while preventing the trucks from entering the plot and crushing the crops. Next, a scheduling and planning model for multiple grain trucks was constructed, and a hybrid genetic and heuristic iterative (HGHI) algorithm was proposed to solve the model. The method fully utilizes the genetic algorithm’s global search capability and the heuristic method’s local optimization capability. It not only improves the quality and accuracy of the solution but also speeds up the optimization process. Finally, using the generated sequence of harvester unloading locations, the operation schedules and paths of both grain trucks and harvesters were updated. The experimental results demonstrate that the HU-HUPGA method has effectively generated and adjusted harvester unloading points within the field, ensuring their precise location in the designated headland area. The HGHI algorithm effectively addresses the collaborative scheduling problem for grain trucks while simultaneously implementing their path planning through a dedicated path planning method. This study, comprising Part Ⅰ and Part II, provides theoretical and technical support for the collaborative scheduling and planning of different types of agricultural machines.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"235 ","pages":"Article 110344"},"PeriodicalIF":7.7,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716223","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":"Improved corn phenology monitoring using translation and weighting of characteristic points from time-series vegetation index","authors":"Qinyan Zhu ,&nbsp;Fumin Wang ,&nbsp;Siting Chen ,&nbsp;Dailiang Peng ,&nbsp;Qiuxiang Yi ,&nbsp;Liming He ,&nbsp;Zhanyu Liu","doi":"10.1016/j.compag.2025.110297","DOIUrl":"10.1016/j.compag.2025.110297","url":null,"abstract":"<div><div>Crop phenology plays a vital role in field management and yield prediction of crops. The current remote sensing phenology identification methods utilize characteristic points extracted from time-series vegetation index curves to directly correspond to the beginning of growth stages. However, due to the differences between the meaning of remotely sensed phenological dates and ground-observed phenological stages, there may be certain systematic errors in phenology identification using this method. Therefore, the study proposed a novel phenology extraction framework for crop phenological stages, which does not directly correspond to the dates of remote sensing characteristic points extracted from NDVI curves to the ground phenological stages, but establishes functions between them to improve monitoring accuracy, including single-characteristic point translation method (SCTM) and double-characteristic points weighting method (DCWM). The two methods were applied for monitoring the corn phenology in 12 states in the United States using MODIS. The results showed that DCWM had a better performance than SCTM in phenology extraction, and both of them were superior to the conventional method in which the characteristic points directly correspond to the crop phenological stage. Combining the two methods, the optimal RMSEs of Emerged, Silking, Dough, Dented, Mature and Harvested were 5.28 days, 3.44 days, 4.65 days, 3.88 days, 4.09 days and 6.73 days. Compared with the results from direct correspondence method, they were decreased by 80.48 %, 41.69 %, 40.15 %, 22.55 %, 13.53 % and 29.38 %. The R² also increased by 20.51 %, 9.52 %, 8.93 %, 17.74 %, 16.67 %, 3.03 %, respectively. The framework proposed in this study is a further in-depth study based on the extraction of remote sensing characteristic points, which significantly improves the monitoring accuracy of corn phenological stages, and provides technical enlightenment for the precise phenological extraction in future study.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"234 ","pages":"Article 110297"},"PeriodicalIF":7.7,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143724105","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":"Assessing the stability of herd productivity groups across lactation periods in automatic milking systems using multi-algorithm clustering","authors":"Karina Brotto Rebuli ,&nbsp;Laura Ozella ,&nbsp;Fernando Masía ,&nbsp;Elisa Vrieze ,&nbsp;Mario Giacobini","doi":"10.1016/j.compag.2025.110295","DOIUrl":"10.1016/j.compag.2025.110295","url":null,"abstract":"<div><div>Automatic Milking Systems (AMSs) generate extensive data at each milking event, potentially offering valuable insights for data-driven herd management and sustainable farming practices. This study investigates an innovative analysis of AMS data aiming to identify and characterise dairy farms that effectively maintain cows with high productivity levels over multiple lactation periods. This analysis represents a new data-driven tool to guide farmers and decision-makers towards more informed herd management. Using Multi-Algorithm Clustering Analysis, we analysed data from 16 AMS-equipped farms to assess the continuity of High Productivity Group (PGs), defined by milk yield and quality, across seven lactation periods. Our findings reveal that farms capable of retaining cows in the High PG, called Continued Productivity farms, exhibit distinctive characteristics, such as slightly lower milk yield but higher milk protein content, compared to farms unable to maintain their High PGs. Notably, the Continued Productivity farms show less intensive milking events, longer milking intervals, and manage lactation cycles to mitigate early-life production pressures, especially in the first lactation. Conversely, Non-Continued Productivity farms, i.e. those unable to retain high PG cows, demonstrate higher milking frequency, shorter intervals, and younger delivery ages, particularly during the first lactation, which may contribute to higher herd turnover. These novel insights support more targeted farm management strategies aimed at sustainability and animal welfare, providing actionable information for decision-makers to optimise herd productivity across lactation periods.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"235 ","pages":"Article 110295"},"PeriodicalIF":7.7,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143705582","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}