Smart agricultural technology最新文献

{"title":"The effect of UAV sprayer operational characteristics on spray deposition within the target area","authors":"Reza Shoja Khankandi ,&nbsp;Mehrnoosh Jafari ,&nbsp;Seyed Ahmad Mireei ,&nbsp;Aminolah Masoumi ,&nbsp;Hamid Reza Eshghizadeh ,&nbsp;Davood Mirzaei","doi":"10.1016/j.atech.2025.101715","DOIUrl":"10.1016/j.atech.2025.101715","url":null,"abstract":"<div><div>In recent years, unmanned aerial vehicles (UAVs) have emerged as a promising technology in agriculture. In aerial spraying, accurately estimating droplet distribution is crucial for evaluating the effectiveness of the spraying process. This study presents the application of spectroscopy and a machine vision-based droplet detection system to develop tools for testing and evaluating UAV sprayers under static and field conditions, demonstrated through the assessment of a specific UAV sprayer. A droplet detection system was designed and developed, consisting of a frame, drive and driven rollers, a stepper motor, LED lights, a power supply, a camera, and Mylar sheet. This system was used in both static and field evaluations. The results showed that the spray width for a single nozzle of the UAV sprayer was approximately 2.5 m. To achieve the lowest coefficient of variation, the optimal distance between adjacent flight paths was determined to be 2.6 m. Both the volume median diameter (VMD) and number median diameter (NMD) increased with distance from the nozzle. Average of deposition ranged from 0.00 to 0.096 μL/cm², with the highest deposition occurring along the centerline of the flight path and decreasing with increasing distance from the centerline. Lowering the altitude significantly reduced wind drift. Due to the influence of wind, turbulence generated by UAV rotor rotation, and downwash airflow during the field test, the VMD and NMD values were lower than those observed in the static test.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"13 ","pages":"Article 101715"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comprehensive deep learning framework for rice variety classification with real-time deployment","authors":"Md. Masudul Islam ,&nbsp;Md. Shafiqul Islam ,&nbsp;Md. Golam Moazzam ,&nbsp;Mohammad Shorif Uddin","doi":"10.1016/j.atech.2025.101710","DOIUrl":"10.1016/j.atech.2025.101710","url":null,"abstract":"<div><div>Rice is a staple food for more than half of the global population and a cornerstone of agricultural economies. With the growing digitalization of agri-food supply chains, automated rice variety recognition has become essential for breeding, seed authentication, quality assurance, and fraud prevention. However, most existing studies are constrained by small datasets often including fewer than twenty varieties limiting model generalization and real-world applicability. To address this gap, we developed a comprehensive deep learning framework that evaluates (i) transfer learning models, (ii) a stacked ensemble of 13 meta-classifiers, and (iii) both raw and pretrained Vision Transformer (ViT-B/16) models on a large-scale and diverse image dataset comprising 62 rice varieties. Experimental results demonstrate that the stacked ensemble achieved the highest classification accuracy, outperforming individual CNN and transformer architectures. For real-world use, three top-performing models VGG16, the stacked ensemble, and Google’s ViT-B/16 are deployed through a lightweight web platform (RiceVision) and a mobile application, enabling real-time rice variety identification. This study underscores the translational potential of AI-driven computer vision in advancing smart agriculture and digital seed supply chain management.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"13 ","pages":"Article 101710"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Animating the transition: How agriculture 5.0 revitalises agroecological principles","authors":"M. Naim ,&nbsp;A. Boukhizzou","doi":"10.1016/j.atech.2026.101788","DOIUrl":"10.1016/j.atech.2026.101788","url":null,"abstract":"<div><div>Agriculture is undergoing a rapid digital transformation that challenges its ecological, social, and ethical foundations. This study explores how the transition from two revolutions, from Agriculture 4.0 (A4.0) to Agriculture 5.0 (A5.0), redefines the relationship between technology and agroecology. The dominant approach of A4.0, driven by automation, big data, and artificial intelligence, has enhanced efficiency but missed many agroecological principles, mainly those contributing to secure social equity and responsibility. Emerging as a corrective paradigm, A5.0 seeks to integrate technological progress with agroecological principles that value the social and human dimension. Adopting a scoping review following PRISMA-ScR guidelines, scientific publications indexed in Scopus and CABI up to October 2025 were screened and coded to assess how current A5.0 research embeds the thirteen agroecological principles defined by the High-Level Panel of Experts in 2019. A total of 136 documents were analysed through bibliometric and thematic synthesis. Results show that A5.0 represents a philosophical and structural evolution beyond the efficiency-oriented logic of A4.0, integrating distributed computing, explainable artificial intelligence, digital twins, and collaborative robotics within ecologically restorative and socially inclusive frameworks. However, while A5.0 strengthens resource efficiency, resilience, and certain social segments through open-source technologies and participatory design, gaps remain in policy coherence, emotional engagement, and human–machine co-learning. To address these, the study proposes two complementary agroecological principles, cognitive symbiosis and emotional ecology, emphasising shared intelligence and affective stewardship between humans, machines, and ecosystems. Overall, Agriculture 5.0 reframes digitalisation as a human-ecological partnership that can operationalise agroecology’s ethical goals if governed by inclusion, transparency, and regeneration rather than control and optimisation.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"13 ","pages":"Article 101788"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing weed detection in strawberry plasticulture: Evaluating stable diffusion models for synthetic image generation of Geranium carolinianum","authors":"Renato Herrig Furlanetto ,&nbsp;Ana Claudia Buzanini ,&nbsp;Arnold Walter Schumann ,&nbsp;Nathan Shawn Boyd","doi":"10.1016/j.atech.2025.101773","DOIUrl":"10.1016/j.atech.2025.101773","url":null,"abstract":"<div><div>The development of vision-guided smart sprayers depends heavily on diverse and high-quality datasets, either collected in the field or sourced from public repositories. However, for certain crops, acquiring such datasets is both labor-intensive and challenging. Text-to-image generative models, such as those based on Stable Diffusion (SD), offer a promising alternative by generating synthetic images from a limited number of real samples. This study explores the potential of two SD models (SDXL and SD3.5L) to generate realistic images of <em>Geranium carolinianum</em> growing alongside strawberry plants. The goal was to assess the YOLOv11 model performance by evaluating training and validation strategies involving: 1) Real images only, 2) 50 % Real + 50 % SD3.5L, 3) 50 % Real + 50 % SDXL, 4) SD3.5L synthetic only, and 5) SDXL synthetic only. A total of 400 high-resolution field images were collected during the 2024-2025 strawberry season in Florida, with 50 representative images of <em>G. carolinianum</em> at various growth stages selected to fine-tune the SD models. The quality of generated images was evaluated using Fréchet Inception Distance (FID), Inception Score (IS), and Structural Similarity Index Measure (SSIM). YOLOv11 model performance was assessed using F1-scores and mean average precision at IoU thresholds of 50 % to 95 % (mAP50, mAP50-95 %). SDXL achieved superior FID and IS scores and produced more photorealistic images, whereas SD3.5L generated images with greater morphological accuracy, variability, and higher SSIM values. YOLOv11 models trained on a mix of real and synthetic images performed comparably to models trained only on real data, with no statistically significant differences in detection accuracy. Models trained exclusively on synthetic data yielded lower performance. These results highlight the potential of generative models to supplement dataset creation, significantly reducing field collection efforts, particularly valuable during off-season periods, for species with limited geographic distribution, under environmental constraints, or for rare or newly discovered species.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"13 ","pages":"Article 101773"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The contribution of explainable machine learning in the detection of two zoonotic pathogens: Coxiella burnetii and Listeria spp., in the milk of dairy small ruminants","authors":"Eleni I. Katsarou ,&nbsp;Yiannis Kiouvrekis ,&nbsp;Daphne T. Lianou ,&nbsp;Themistoklis Giannoulis ,&nbsp;Efthymia Petinaki ,&nbsp;Charalambia K. Michael ,&nbsp;Natalia G.C. Vasileiou ,&nbsp;Anargyros Skoulakis ,&nbsp;Angeliki I. Katsafadou ,&nbsp;Nikolaos Solomakos ,&nbsp;Dimitriοs C. Chatzopoulos ,&nbsp;Maria V. Bourganou ,&nbsp;Dimitra V. Liagka ,&nbsp;George C. Fthenakis ,&nbsp;Dimitris Kalatzis ,&nbsp;Vasia S. Mavrogianni","doi":"10.1016/j.atech.2025.101767","DOIUrl":"10.1016/j.atech.2025.101767","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Among the zoonotic pathogens, &lt;em&gt;Coxiella burnetii&lt;/em&gt; and &lt;em&gt;Listeria&lt;/em&gt; spp. are of particular significance, as they may cause relevant infections in people. These often arise as the consequence of consumption of dairy products contaminated with these pathogens. Hence, there is a scope for developing a tool through the application of machine learning methodologies, by means of which one may produce an accurate prediction regarding the presence of the above pathogens in the milk produced at small ruminant (sheep, goat) farms. The objective of the present study was the development of computational models for predictions for the presence of these pathogens in the bulk-tank milk in dairy sheep and goat farms. The presence of &lt;em&gt;C. burnetii&lt;/em&gt; or &lt;em&gt;Listeria&lt;/em&gt; spp. therein was defined as the target value and computational models were constructed for their prediction, through the use of independent variables (&lt;em&gt;n&lt;/em&gt; = 11 for &lt;em&gt;C. burnetii&lt;/em&gt; and &lt;em&gt;n&lt;/em&gt; = 8 for &lt;em&gt;Listeria&lt;/em&gt; spp.) related to factors prevailing at the farms. Seven machine learning tools were employed: Decision Trees (36 different models evaluated for each pathogen), Random Forest (9 models), LightGBM (24 models), XGBoost (54 models), k-Nearest Neighbours (32 models), Support Vector Machines (3 models) and Neural Networks (24 models). In all, 182 evaluations were performed for each pathogen. For the prediction of the presence of &lt;em&gt;C. burnetii&lt;/em&gt; in the bulk-tank milk, Random Forest and XGBoost were the two tools that produced the best measures of quality: the median value for four measures of quality of the predictions (accuracy, precision, recall, F1-score) evaluated was over 0.942 for each of these two tools. The results of the analysis for SHapley Additive exPlanations values for the impact of the independent variables in the prediction of the presence of the pathogen indicated that (a) the management system applied on the farm and (b) the average wind speed at the location of the farm during the 15 days prior to sampling, were the variables that mostly influenced the prediction outcome. In the case of &lt;em&gt;Listeria&lt;/em&gt; spp., meaningful classification could not be achieved, due to the highly imbalanced and limited nature of the dataset. For this pathogen, the models heavily favoured the majority class and produced predictions that defaulted almost entirely to ‘no presence’ of the pathogen; for the minority class, recall and precision were as low as 0.300. The findings of this study indicate that machine learning algorithms can be employed for the prediction of the presence of &lt;em&gt;C. burnetii&lt;/em&gt; in the milk produced in dairy sheep and goat farms. These computational prediction models were developed using field data collected during countrywide studies. The models showed excellent performance and thus can be employed as adjunct tools to support clinical decisions in the health management of dairy small ruminant farms and the production of saf","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"13 ","pages":"Article 101767"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A grain flow detection method based on an improved YOLOv8n model and dual-line counting with a multi-criterion discrimination mechanism","authors":"Jie Shi,&nbsp;Xinrui Zhang,&nbsp;Yantao Zhao,&nbsp;Zhi Li,&nbsp;Linlin Yang,&nbsp;Wencai Yang","doi":"10.1016/j.atech.2025.101728","DOIUrl":"10.1016/j.atech.2025.101728","url":null,"abstract":"<div><div>To support non-contact grain yield monitoring in smart agriculture, this study presents a deep learning-based method for grain flow detection. The system includes two core components: an enhanced YOLOv8n detection model and a dual-line counting algorithm guided by multiple decision rules. In real harvesting conditions, challenges such as occlusion, fast grain movement, poor lighting, and small target sizes often lead to missed detections when using standard YOLO models. To improve performance, the YOLOv8n structure is modified in three ways. A minimum point distance IoU loss (MPDIoU) is applied to better distinguish overlapping grains. A spatial-channel shared attention (SCSA) module is added to help the model focus on weak or small grain targets. A lightweight Spatial and Channel reconstruction Convolution (SCConv) module is inserted into the network to reduce computation while maintaining accuracy. As a result, the updated model reaches 90.6 % in precision, 91.4 % in recall, and 94.5 % in mAP, improving the original version by 2.7, 1.7, and 2.5 points. In the counting stage, a dual-line method is used, combining rules such as movement direction checking, two-line crossing validation, abnormal shape removal, and IoU-based filtering. The full system remains stable even when grains move quickly, supporting reliable detection under field conditions.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"13 ","pages":"Article 101728"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimation of pear tree leaf area index using fused UAV multispectral and RGB imagery","authors":"Ning Yan ,&nbsp;Juanjuan Ma ,&nbsp;Jingming Wu ,&nbsp;Qi Wang ,&nbsp;Xuedong Zhang ,&nbsp;Xu Li","doi":"10.1016/j.atech.2025.101717","DOIUrl":"10.1016/j.atech.2025.101717","url":null,"abstract":"<div><div>Leaf Area Index (LAI) is a key parameter representing canopy structure and photosynthetic capacity in fruit trees, playing a critical role in growth monitoring, precision water and nutrient management, and yield prediction. Considering that the canopy is most developed during the fruit expansion stage and that accurate LAI estimation is particularly needed at this stage, this study focused on pear orchards in the Seventh Regiment of southern Xinjiang and conducted precise LAI retrieval by integrating UAV-based multispectral and RGB imagery. Vegetation indices, color indices, texture features, and RGB wavelet features were extracted as multi-source variables to construct multispectral models, RGB models, and fused models, which were evaluated using multiple machine learning algorithms. The results demonstrated that: (1) multi-source features showed significant correlations with measured LAI, indicating strong potential for accurate retrieval; (2) the fusion of multispectral and RGB features substantially improved LAI estimation accuracy, exhibiting higher stability and generalization compared to single-source models; and (3) among all tested models, the optimized integrated algorithm (OIA) achieved the best performance. This approach constructs a performance-adaptive weighted fusion mechanism based on the prediction errors and stability of base learners including SVM, ELM, and XGBoost, enabling complementary advantages among multiple models through optimized weight allocation. Overall, the combination of vegetation indices, multispectral texture features, and RGB wavelet features in the OIA model achieved the highest accuracy, with a testing set R² of 0.744 and RMSE of 0.141 m²/m². This study innovatively establishes a UAV-based multi-source and adaptive weighted ensemble framework for LAI estimation, significantly enhancing spatial estimation accuracy during the fruit expansion stage and providing a scalable technical approach for precise monitoring, water and nutrient management, and yield assessment in fruit trees.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"13 ","pages":"Article 101717"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intra-farm dynamic of environmental conditions in commercial rabbitries with respiratory disorders using an innovative IoT device","authors":"Manuel Garbuio ,&nbsp;Benedetta Cordioli ,&nbsp;Elisabetta Riva ,&nbsp;Luca Bano ,&nbsp;Giorgio Provolo","doi":"10.1016/j.atech.2025.101725","DOIUrl":"10.1016/j.atech.2025.101725","url":null,"abstract":"<div><div>The microclimate inside rabbitries is a well-established predisposing factor for respiratory diseases, influencing animal growth and health. This study investigated the fluctuations of key environmental parameters in fattening rabbitries with and without respiratory disorders. To this purpose, six multifunctional IoT (Internet of Things) devices were placed at animal level in different locations within each barn. These devices can measure continuously various parameters, including temperature, relative humidity, light intensity, sound pressure, gas concentrations (ammonia, sulphuric acid, carbon dioxide) and particulate levels. Measurements were collected for 7 to 10 days in each farm, with external conditions simultaneously recorded from local meteorological stations. Considering the optimal welfare values, the continuous monitoring system provided valuable information regarding significant deviations of some parameters, irrespective of the external conditions.</div><div>The results highlighted that temperatures were consistently above 20 °C for 51%, 89%, and 100% of the time in three out of four problematic farms, whereas relative humidity was below the suggested value of 60% in all of them for 30% to 89% of the time. Moreover, multi-site measurements within the same building revealed a heterogeneous distribution of the parameters investigated. Differences in ammonia concentrations up to 11 ppm have been found in the same barn, indicating a low uniformity of air quality. Dustiness, sound pressure, and sulphuric acid concentrations never reached levels of concern.</div><div>The findings demonstrate the utility of this real-time evaluation method as a valuable tool for farmers' decision-making, specifically in efforts to mitigate the adverse impact of environmental conditions on animal health.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"13 ","pages":"Article 101725"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"bioWatch: a computer vision system for community-based identification and reporting of spotted lanternfly life stage","authors":"Yanqiu Yang ,&nbsp;Kittiphum Pawikhum","doi":"10.1016/j.atech.2025.101688","DOIUrl":"10.1016/j.atech.2025.101688","url":null,"abstract":"<div><div>Invasive species such as the Spotted Lanternfly (SLF, <em>Lycorma delicatula</em>) pose significant ecological and economic threats worldwide. This study introduces bioWatch, an AI-powered mobile application designed to address the challenges of invasive species detection, monitoring, and management. The application utilizes a YOLO11n deep learning model, trained on 1161 annotated images, to classify SLF across three life stages: adult, early nymph, and late nymph. In testing, the model achieved an overall precision of 0.854 and an mAP@50 of 0.589. Early and late nymph stages demonstrated strong classification performance, with mAP@50 values of 0.834 and 0.811, respectively. Although the recall for the early nymph stage was lower (0.600), the model exhibited robust performance for adults and late nymphs (recalls of 0.730 and 0.896, respectively). The egg stage, while included in training, showed limited detection performance (precision of 1.000, recall of 0.000) due to severe class imbalance in the dataset (only seven annotated instances), rather than limitations of the model architecture. This model serves as a baseline to support the deployment of the bioWatch application, which is designed for modular integration and future updates. bioWatch integrates geospatial mapping, augmented reality, explainable AI visualization, and gamification features to enhance user engagement, with early user feedback highlighting its potential for biodiversity monitoring and K–12 education.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"13 ","pages":"Article 101688"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A full end-to-end analytical framework for livestock behavior modeling and health assessment using wearable electronic recording system and machine learning","authors":"Guohao Ni ,&nbsp;Yuanzheng Jia ,&nbsp;Zhonghao Shi ,&nbsp;Fangyuan Chang ,&nbsp;Jinfeng Miao ,&nbsp;Jian Wang ,&nbsp;Gengping Ye ,&nbsp;Jie Wu ,&nbsp;Huifang Yin ,&nbsp;Wei Jiang ,&nbsp;Xiangan Han ,&nbsp;Wei Tang","doi":"10.1016/j.atech.2025.101686","DOIUrl":"10.1016/j.atech.2025.101686","url":null,"abstract":"<div><div>Precision Livestock Farming (PLF) aims to enhance animal management through technology, yet its progression is limited by a disconnect between discrete data collection tools and the practical requirement for unified, interpretable decision-support systems. While wearable sensors and machine learning offer potential for behavior monitoring, current solutions are often fragmented, focusing on isolated classification tasks rather than providing a complete, actionable pipeline from raw data to farm management insights. This lack of integration, alongside the technical challenges of model optimization, significantly hinders widespread practical adoption. This work presents a full end-to-end analytical framework that integrates wearable electronic recording system (WERS) hardware with intelligent analytical toolkit (IAT) software to form a fully automated workflow. The IAT incorporates automated model selection and hyperparameter tuning across twelve machine learning algorithms, three feature extraction methods, and six feature selection strategies, enabling flexible and customizable modeling pipelines for sequential data processing, behavior recognition and health evaluation, and visual feedback. The implemented system demonstrates high classification accuracy, strong adaptability, and robust support for cattle behavior sequence analysis and health assessment. The system has been empirically validated on eight dairy cattle over a six-day period, demonstrating its practical applicability in real-world conditions based on the real-time deployment platform built for the system. By providing a systematic and scalable solution for intelligent livestock monitoring, this work bridges the gap between fragmented sensing technologies and operational decision-support systems, ultimately contributing to improved decision-making and operational efficiency in PLF management.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"13 ","pages":"Article 101686"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}