Smart agricultural technology最新文献

{"title":"Precise profiling of behavioral time budgets of finishing Wagyu steers using linear mixed model","authors":"Nanako Mochizuki ,&nbsp;Takahiro Aoki ,&nbsp;Yasuhiro Morita ,&nbsp;Mitsunori Kayano","doi":"10.1016/j.atech.2025.101768","DOIUrl":"10.1016/j.atech.2025.101768","url":null,"abstract":"<div><div>The aim of this study was to identify behavioral characteristics of Wagyu beef cattle recorded by neck collar sensors. Although understanding Wagyu-specific traits is essential for precision livestock farming, data from behavioral monitoring sensors are difficult to interpret due to their latent variability. Moreover, since the Wagyu industry makes use of unique management strategies to promote highly marbled beef, Wagyu traits cannot be inferred from dairy cows or other beef breeds, requiring validation at the breed level. This study aimed to overcome these problems through a robust statistical methodology, linear mixed model, applying to a neck collar dataset in a Japanese commercial fattening farm with 229 healthy Wagyu steers during their finishing period (21–30 months of age). Daily time of six behaviors (eating, activity, standing, lying, standing-rumination, and lying-rumination) was recorded for each animal. A linear mixed model was applied to the monthly individual average time of each behavior for precise profiling of Wagyu-specific behavioral time budgets. The model included age, season (colder and warmer) and their interaction as the fixed effects, and animal IDs as a random effect to minimize the individual variability. The results showed behavioral alterations associated with age in non-feeding behaviors, including activity, standing and lying. Significant seasonal effects were also identified, depending on the age and behavior. This study can be the foundation for accurate detection of individual disorders and improving herd management practices in the Wagyu industry.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"13 ","pages":"Article 101768"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925728","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":"Multi branch model based on cross scale feature fusion for wheat seedling variety recognition","authors":"Zhang Wenbo ,&nbsp;Zhang Ziyang ,&nbsp;Xi Chengyu ,&nbsp;Zhang Qingshan","doi":"10.1016/j.atech.2026.101785","DOIUrl":"10.1016/j.atech.2026.101785","url":null,"abstract":"<div><div>Accurate identification of wheat varieties at the seedling stage is crucial for maintaining seed purity and optimizing field management. However, the subtle phenotypic variations among seedlings present a significant challenge for visual recognition. To address this, we propose SeedlingNet, a novel deep learning model specifically designed for fine-grained wheat seedling variety classification. The core innovations of SeedlingNet include: The Kolmogorov-Arnold-based Convolutional Attention (KCA) mechanism, which dynamically enhances feature representation by replacing static activation functions with learnable, adaptive ones; A multi-scale feature fusion architecture that integrates hierarchical features to capture both global and local characteristics. We established a comprehensive image dataset of 13,600 images representing 17 wheat varieties at the early growth stage. Experimental results demonstrate that SeedlingNet achieves a remarkable classification accuracy of 99.26 %, outperforming traditional machine learning methods and mainstream deep learning models. Ablation studies confirm the significant impact of the KCA module and the multi-scale fusion structure on the model's performance. This research provides an effective, non-destructive tool for early-stage variety identification, with strong potential for precision agriculture applications.The dataset is licensed in Zhang, Wenbo (2025), \"Seedings\", Mendeley Data, V1, doi: 10.17632/f8ykx4sz6w. 1.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"13 ","pages":"Article 101785"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925729","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 mathematical model for estimation of leaf wetness for use in European agricultural decision support systems","authors":"Anne-Grete Roer Hjelkrem ,&nbsp;Brita Linnestad ,&nbsp;Ingerd Skow Hofgaard ,&nbsp;Berit Nordskog","doi":"10.1016/j.atech.2025.101723","DOIUrl":"10.1016/j.atech.2025.101723","url":null,"abstract":"<div><div>Web-based decision support systems (DSSs) are essential tools for precision crop protection, guiding farmers and advisors in implementing need-based control measures against pests, diseases, and weeds. These systems rely heavily on weather-driven models, where data accuracy and availability are critical. Leaf wetness is a key factor for infection and reproduction of many fungal plant pathogens, making it an important input in DSSs. However, the availability of leaf wetness data from weather services is variable, leading to the development of numerous estimation models without a universally accepted standard model. This study aimed to develop a robust mathematical model for estimating leaf wetness across diverse European climate zones and to integrate the model for enabling real-time leaf wetness estimates as inputs to web based DSSs. Hourly weather data, including leaf wetness, temperature, precipitation, relative humidity and wind speed were collected from automatic weather stations. Training data came from five Norwegian sites, while testing data covered 17 locations across Europe. Five machine learning based models (decision tree (DT), random forest, K-Nearest neighbour, multi-layer perception, long short-term memory (LSTM)) were trained and their performance compared with five existing empirical models (RH87, RH90, extRH, DPD, CART/SLD) from the literature. LSTM and DT achieved best performance, demonstrating strong robustness across climate zones. The LSTM model, required fewer data inputs and well suited for time-series data, was integrated into a Flask-based service for automatic use in forecasting models within the web-based IPM Decisions DSS platform, thereby enhancing the precision of this DSS.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"13 ","pages":"Article 101723"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925833","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":"Real-time estimation of soil pulverization ratio after tillage using a camera mounted on agricultural machinery","authors":"Shintaro Noda,&nbsp;Hidefumi Saito,&nbsp;Kanako Kusa,&nbsp;Kunio Tateishi,&nbsp;Wataru Iijima,&nbsp;Junichi Nakagawa","doi":"10.1016/j.atech.2025.101687","DOIUrl":"10.1016/j.atech.2025.101687","url":null,"abstract":"<div><div>Accurate measurement of the soil pulverization ratio is essential for automated tillage operations. We developed and implemented a real-time system for estimating the pulverization ratio using a camera mounted on agricultural machinery. Then, we verified the accuracy of the system under field conditions. First, we evaluated a conventional method that classifies soil clods based on brightness. The brightness threshold was calibrated to match the pulverization ratio, which was measured directly in a field. Real-time measurements were conducted in three regions of Japan: Akita, Saga, and Ibaraki. The estimation accuracy, expressed as the root mean square error (RMSE), was 10.3% for training data and 16.1% for testing data. Using the 131 data obtained from these experiments, we then evaluated the estimation performance of a convolutional neural network (CNN) model. The results were as follows: (1) When cross-validated by stratifying the data by region, the CNN achieved 14.5% RMSE with a 95% confidence interval (CI) of 11.8 ∼ 16.0, demonstrating improved performance through multi-region training. (2) When data were randomly divided for cross-validation, the CNN further improved to 11.4% RMSE with a 95% CI of 10.8 ∼ 11.8, indicating that a larger, more diverse dataset enhances prediction accuracy. Furthermore, the computational efficiency of the system was substantially improved. Using the CNN reduced the required image resolution from 1392 to just 100 pixels without compromising estimation performance.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"13 ","pages":"Article 101687"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737828","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 UAV-based tobacco plant detection model integrating NDVI and multi-scale feature fusion for precision agriculture","authors":"Xinbao Chen,&nbsp;Junqi Lei,&nbsp;Yaohui Zhang,&nbsp;Xianzhao Liu,&nbsp;Xiangyue Chen","doi":"10.1016/j.atech.2025.101703","DOIUrl":"10.1016/j.atech.2025.101703","url":null,"abstract":"<div><h3>Context</h3><div>Accurate and early detection of tobacco plants is essential for optimizing field management and ensuring stable yield in precision agriculture. Yet, achieving reliable detection at the transplanting stage using Unmanned Aerial Vehicles (UAVs) is particularly challenging due to complex soil backgrounds and the prevalence of small, obscure targets.</div></div><div><h3>Objective</h3><div>This study aims to develop a robust UAV-based detection framework that integrates vegetation indices with deep learning to enhance discrimination between crops and non-crops during the critical transplanting stage.</div></div><div><h3>Methods</h3><div>We propose YOLO<img>NTD, an innovative detection framework that incorporates NDVI (Normalized Difference Vegetation Index) spectral information into a deep learning pipeline. The architecture integrates three dedicated modules: (i) Small Object Enhanced Pyramid (SOEP) for capturing fine-grained features, (ii) Feature Complementary Mapping (FCM) for enriching multi-scale contextual information, and (iii) Fusion and Pyramid Spatial Channel (FPSC) for optimized feature fusion. Additionally, the Normalized Wasserstein Distance (NWD) metric is introduced to reduce localization sensitivity in small-object detection.</div></div><div><h3>Results and conclusions</h3><div>Experimental results show that YOLO<img>NTD achieves state-of-the-art performance, reaching 69.9 % mAP@50–95 and 54.6% APtiny, significantly surpassing the baseline model while maintaining low computational overhead. These findings confirm the efficacy of combining vegetation indices with deep learning for enhanced small-object detection.</div></div><div><h3>Significance</h3><div>This study provides a reliable and efficient solution for UAV-based crop monitoring, demonstrating that the integration of spectral indices with advanced detection models can substantially improve precision agriculture practices, particularly in early-stage crop management.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"13 ","pages":"Article 101703"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737824","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":"Smart energy supply for smart farms: A hybrid ground source heat pump in seasonal greenhouses for growing cherries","authors":"Biao Zhou,&nbsp;Rumeng Zhang,&nbsp;Zhiming Zhang,&nbsp;Kaidi Li,&nbsp;Jianye Wang,&nbsp;Jiufa Chen,&nbsp;George Papadakis,&nbsp;Bin Guo","doi":"10.1016/j.atech.2025.101709","DOIUrl":"10.1016/j.atech.2025.101709","url":null,"abstract":"<div><div>Growing off-season cherries in seasonal greenhouses is beneficial for increasing the income of growers. The traditional compressed air conditioners commonly used in seasonal greenhouses have the problem of high operating costs. Therefore, in this paper, the TRNSYS simulation software was used to evaluate the operation of a hybrid ground source heat pump system (HGSHP) composed of ground source heat pumps, photovoltaic thermal panels (PV/T), and cooling towers (CT) in seasonal greenhouses. The system features a cooling tower with a heat dissipation capacity of 70.4 kW and PV/T components covering an area of 40 m². The operational performance and feasibility of this system in the Shandong region were investigated.</div><div>The results show that compared to a system using only a ground-source heat pump (GSHP), after five years of operation, the soil temperature increased by 6.3 °C for the GSHP, while the HGSHP system resulted in an increase of 0.2 °C. The addition of the cooling tower reduced the number of vertical ground-source heat exchangers from 30 to 22, resulting in a reduction of 46,959 CNY in energy consumption over five years of operation. Compared to GSHP systems, the HGSHP system has an additional payback period of approximately 6.0 years, yielding equivalent annual cost savings of 5981 CNY and reducing carbon dioxide emissions by 13.1 tons. These findings highlight the HGSHP system's potential for providing efficient heating and cooling in seasonal greenhouses, along with its value in terms of high economic viability and long-term sustainability.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"13 ","pages":"Article 101709"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790646","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":"Addressing challenges in agriculture: Exploring smart farming technologies to support future farming practices","authors":"Sebastian Pöchtrager ,&nbsp;Nadine Bachmann ,&nbsp;Manuel Brunner ,&nbsp;Shailesh Tripathi ,&nbsp;Herbert Jodlbauer","doi":"10.1016/j.atech.2025.101694","DOIUrl":"10.1016/j.atech.2025.101694","url":null,"abstract":"<div><div>Climate change, declining public perception, and growing sustainability demands are transforming the agricultural sector. Smart farming technologies (SFTs) offer solutions by leveraging digital tools and data-driven applications to enhance efficiency, sustainability, and decision-making. However, their adoption remains limited because of high costs, data privacy concerns, and insufficient training and support. This qualitative study explores the challenges farmers face when adopting SFTs, and the capabilities that agricultural technology providers must develop to support them. Semi-structured interviews were conducted with 15 farmers and farm managers from Germany, Austria, and Switzerland. The interview guide was iteratively designed using the jobs-to-be-done framework and dynamic capabilities theory, focusing on nine farmer jobs in SFT adoption and five key provider capabilities. The findings show that farmers struggle with system integration, data management, and decision-making complexities. They require support for platformization, data acquisition, decision support systems, knowledge transfer, and data security. Providers must strengthen these capabilities to address farmers’ needs and foster trust. This study contributes methodologically by aligning customer activities with organizational capabilities. Managerial implications emphasize customer-centric innovation, whereas future research should address universal data standards, stronger data security, and the transition toward Agriculture 5.0.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"13 ","pages":"Article 101694"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790767","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":"Behaviour recognition of tail and ear biting in pigs using AI-based computer vision","authors":"Qinghua Guo ,&nbsp;Clémence A.E.M. Orsini ,&nbsp;Patrick P.J.H. Langenhuizen ,&nbsp;Yue Sun ,&nbsp;Shoujun Huo ,&nbsp;Lisette E. van der Zande ,&nbsp;Inonge Reimert ,&nbsp;J. Elizabeth Bolhuis ,&nbsp;Piter Bijma ,&nbsp;Peter H.N. de With","doi":"10.1016/j.atech.2025.101713","DOIUrl":"10.1016/j.atech.2025.101713","url":null,"abstract":"<div><div>Damaging behaviours in pigs, such as tail biting and ear biting, compromise animal welfare and farm productivity. Continuous monitoring of these behaviours is essential to intervene before escalation, gain insights into underlying causes and develop breeding programs to select pigs with lower genetic propensity for such behaviours. However, manual observations are impractical at a large-scale. To address this challenge, we propose a video-based behaviour recognition model that facilitates the automated monitoring of individual pigs. Two state-of-the-art video-based methods are investigated: SlowFast and Improved Multiscale Vision Transformers (MViTv2) for recognizing tail and ear biting in pigs, by exploiting spatiotemporal domain features. Data are collected on a commercial pig farm. In total, 532 tail-biting events (63,815 frames) and 750 ear-biting events (78,132 frames) are annotated across seven pens of tail-docked pigs. Tail biting and ear biting are defined as nibbling, sucking, chewing, or biting the tail or the ear of a pen mate. The best-performing method is based on the MViTv2-S model, which enables efficient spatiotemporal modeling. The detection accuracies obtained for tail and ear biting are 72.22 % and 72.37 %, respectively. An important and novel aspect to our knowledge is that for the first time, behaviour detection is developed without a posture requirement on the biter or the victim. The conducted experiments demonstrate the feasibility of computer-vision-based models for the recognition of damaging behaviours on commercial pig farms. This study is a crucial step towards the development of an automated early-warning approach and breeding programs to reduce tail biting and ear biting.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"13 ","pages":"Article 101713"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790774","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":"An efficient and low-cost 3D phenotyping framework for tomato seedlings via neural radiance fields and PointNet++","authors":"Hongduo Zhang,&nbsp;Kenichi Furuhashi,&nbsp;Yutaka Kaizu","doi":"10.1016/j.atech.2025.101722","DOIUrl":"10.1016/j.atech.2025.101722","url":null,"abstract":"<div><div>Accurate and efficient plant phenotyping is essential for modern precision agriculture. as it provides reliable information for seedling quality evaluation, early detection of plant stress, and data support for crop breeding and yield prediction. Traditional three-dimensional (3D) reconstruction and analysis methods are often costly and time-consuming, because they usually depend on expensive laser scanning devices or require many input images. Even with these resources, they often fail to capture fine plant structures such as leaves and branches, which limits their application in seedling monitoring. To address these challenges, we propose an integrated framework that combines neural radiance fields (NeRFs) for high-fidelity 3D reconstruction, PointNet++ for robust semantic segmentation, and a customized algorithm for extracting key morphological parameters of tomato seedlings. The proposed framework can be used to reconstruct detailed 3D models at a low computational cost using only ordinary cameras and a limited number of 2D images. We validate the framework on the basis of a tomato seedling dataset and show that our approach outperforms traditional multiview stereo scanners and simple commercial 3D scanners in terms of both detail and efficiency. The accuracy of plant part segmentation reaches 90 %, and the extracted parameters (e.g., leaf area, stem height, branch angle, and internode distance) are highly correlated with the manual measurements (e.g., <span><math><mrow><msup><mrow><mi>R</mi></mrow><mn>2</mn></msup><mo>=</mo><mn>0.875</mn></mrow></math></span> for the leaf area). This study provides a low-cost and scalable solution for 3D plant analysis, with direct benefits for automated monitoring of seedling quality in nursery production. Moreover, the proposed framework can be extended to other crops with complex structures, thus supporting wider applications in smart agriculture.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"13 ","pages":"Article 101722"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791404","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":"Machine learning to incorporate root morphology with UAV multispectral imaging for yield and nitrogen prediction in cereals","authors":"George Papadopoulos ,&nbsp;Borja Espejo-Garcia ,&nbsp;Ioannis Zafeiriou ,&nbsp;Evgenia Georgiou ,&nbsp;Antonia Oikonomou ,&nbsp;Antonios Mavroeidis ,&nbsp;Panteleimon Stavropoulos ,&nbsp;Ioanna Kakabouki ,&nbsp;Spyros Fountas ,&nbsp;Dimitris Bilalis","doi":"10.1016/j.atech.2025.101732","DOIUrl":"10.1016/j.atech.2025.101732","url":null,"abstract":"<div><div>Early-season prediction of yield and nitrogen‐related performance is essential for enabling timely agronomic interventions yet remains challenging in crops with limited prior digital phenotyping research, such as <em>Tritordeum</em>. Root traits, although fundamental to early nutrient uptake and crop establishment, remain largely absent in ML prediction frameworks. This study evaluated how root morphological traits, in combination with UAV-derived multispectral indices and proximal agronomic measurements, contribute to predicting yield and nitrogen efficiency indices under Mediterranean field conditions. Measurements were collected during the first three phenological stages, and a diverse set of machine-learning algorithms, including Random Forest, ExtraTrees, AdaBoost, Support Vector Regression, regularised linear models and the foundation model TabPFN, were benchmarked using a rigorous 10×5 cross-validation scheme. Yield emerged as the most reliably predictable trait, reaching an R² of 0.90 in the best multivariate configuration, while nitrogen-efficiency indices (NUE, NHI, NUtE) showed substantially higher variability and limited early-season predictability. Root diameter at the tillering stage consistently ranked among the most informative predictors, and its combination with SPAD at stem elongation, MCARI at tillering, or plant height at tillering produced the strongest yield models. These findings highlight the importance of integrating early-season below-ground information with spectral and agronomic traits to enhance prediction accuracy. Overall, the study demonstrates that accurate early-season yield forecasting in <em>Tritordeum</em> can be achieved using a minimal set of measurements, supporting cost-efficient monitoring and enabling actionable in-season adjustments to nitrogen management. The results also show the potential of foundation models such as TabPFN for limited agronomic datasets, providing a basis for developing scalable, data-driven decision-support tools for sustainable cereal production.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"13 ","pages":"Article 101732"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925410","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}