Smart agricultural technology最新文献_第2页

Smart farming: Real-time rice yield forecasting on mobile devices using lightweight CNN-LSTM 智能农业：使用轻量级CNN-LSTM在移动设备上进行实时水稻产量预测

IF 5.7

Smart agricultural technology Pub Date : 2026-03-01 Epub Date: 2025-11-26 DOI: 10.1016/j.atech.2025.101664

Sakshi Gandotra, Rita Chhikara, Anuradha Dhull

{"title":"Smart farming: Real-time rice yield forecasting on mobile devices using lightweight CNN-LSTM","authors":"Sakshi Gandotra, Rita Chhikara, Anuradha Dhull","doi":"10.1016/j.atech.2025.101664","DOIUrl":"10.1016/j.atech.2025.101664","url":null,"abstract":"<div><div>This work presents a framework for accurate and punctual in-season crop yield estimation at high spatial resolution for Indian farmers through the utilisation of low-resource edge devices by reducing the CNN-LSTM model neural activations' memory requirements. We propose a new memory optimisation approach—Clustering and Compression (C²)—that is tailored to combating the large memory needs of CNN-LSTM architecture neural activations. Through combining spatial feature extraction and temporal learning, the model acquires efficient spatiotemporal representations. It is trained on high-resolution block-level yield data, satellite-delivered Normalized Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI), and Jammu region weather data. Optimized CNN-LSTM comprehensively surpasses performance of baseline CNN and LSTM models while minimising memory usage by orders of magnitude—especially in neural activations. This optimisation allows for cost-effective, cloud-independent on-device inference and routine model training, which are essential for handling the day-to-day environmental fluctuations in the dynamic climates. In summary, the proposed method allows for a novel neural activation memory optimisation technique that facilitates device-local high-resolution crop yield estimation, paving the way for sustainable and strong agriculture for smallholder farmers.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"13 ","pages":"Article 101664"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737825","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}

引用次数: 0

Vibration regimen of finger-clamped seed-metering device based on DEM-MBD 基于DEM-MBD的指夹式排种器振动规律

IF 5.7

Smart agricultural technology Pub Date : 2026-03-01 Epub Date: 2025-11-29 DOI: 10.1016/j.atech.2025.101683

Zhuanghong Ma , Junchang Zhang , Hu Shi , Yu Chen , Xinyu Zhao , Zhengdao Liu , Yuxiang Huang

{"title":"Vibration regimen of finger-clamped seed-metering device based on DEM-MBD","authors":"Zhuanghong Ma , Junchang Zhang , Hu Shi , Yu Chen , Xinyu Zhao , Zhengdao Liu , Yuxiang Huang","doi":"10.1016/j.atech.2025.101683","DOIUrl":"10.1016/j.atech.2025.101683","url":null,"abstract":"<div><div>This study quantifies how vibration affects the singulation performance of a finger-clamped seed-metering device using coupled discrete element method–multibody dynamics (DEM–MBD) simulation and bench validation. Maize kernels were modelled in EDEM, while the metering mechanism and prescribed excitations were represented in RecurDyn. A factorial and Box–Behnken design varied vibration direction, operating speed, frequency, and amplitude; response-surface models were fitted for the qualified index, multiple index, and leakage index. Vertical vibration exerted the dominant influence on discharge behaviour. Under weak excitation (frequency<15.38Hz; amplitude<3.42mm), increasing vibration intensity reduced multiple seeding and improved the qualified index, whereas stronger excitation (frequency>15.38Hz; amplitude>3.42mm) increased leakage and reduced the qualified index, delineating a usable–detrimental vibration regime boundary. Multi-objective optimisation predicted optimal parameters of 8.74km·h⁻¹, 15.38Hz, and 3.42mm, yielding qualified, multiple, and leakage indices of 90.68%, 7.19%, and 7.30%, respectively. Bench tests on a shaker table with high-speed imaging produced 87.61%, 8.03%, and 8.98% under the same settings, in agreement with simulation trends (absolute errors: 2.15%, 0.94%, and 1.34%). The results provide quantitative guidance for vibration management and structural optimisation of finger-clamped metering, showing that appropriately tuned excitation can aid seed clearing and filling, while excessive vibration degrades singulation.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"13 ","pages":"Article 101683"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685211","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}

引用次数: 0

UAV remote sensing imagery-based semantic segmentation approach for lodged rice region 基于无人机遥感影像的水稻倒伏区语义分割方法

IF 5.7

Smart agricultural technology Pub Date : 2026-03-01 Epub Date: 2025-12-01 DOI: 10.1016/j.atech.2025.101689

Qiang Chen , Chuang Xia , Yinyan Shi , Xiaochan Wang , Xuekai Huang , Lei Wang , Xiaolei Zhang , Enlai Zheng , Xiaojun Gao , Fei Liu

{"title":"UAV remote sensing imagery-based semantic segmentation approach for lodged rice region","authors":"Qiang Chen , Chuang Xia , Yinyan Shi , Xiaochan Wang , Xuekai Huang , Lei Wang , Xiaolei Zhang , Enlai Zheng , Xiaojun Gao , Fei Liu","doi":"10.1016/j.atech.2025.101689","DOIUrl":"10.1016/j.atech.2025.101689","url":null,"abstract":"<div><div>Rice is one of the major staple crops in China, and its yield is closely tied to national food security and farmers’ economic returns. Lodging in rice not only reduces the efficiency of mechanical harvesting but also severely impacts yield and grain quality. Therefore, accurately identifying lodged areas is of great importance. This study proposes a rice lodging detection method based on UAV-acquired multispectral remote sensing imagery. High-resolution, multi-temporal images were collected over paddy fields in Yuhang District, Zhejiang Province, using DJI Mavic 3 M and M300 UAVs. A dataset was constructed via image cropping and data augmentation. Two deep learning models—U-Net with a VGG-16 backbone and DeepLabv3+ with a MobileNetv2 backbone—were compared for semantic segmentation performance. Experimental results show that the U-Net model achieved superior performance on the validation set, with a mean Intersection over Union (MIoU) of 91.57 %, mean Pixel Accuracy (MPA) of 95.83 %, Precision of 95.27 %, Recall of 95.83 %, and training/validation losses of 0.106 and 0.151, respectively, outperforming the DeepLabv3+ model. Additionally, the impact of different training-validation data split ratios was examined. The U-Net model showed better generalization and stability when trained with a 9:1 split compared to an 8:2 split. Furthermore, based on the semantic segmentation results, the area of lodged rice was estimated and compared against ground-truth measurements. The U-Net model produced minimal relative error, with a maximum deviation of <3 %, demonstrating strong practical applicability. These findings suggest that the U-Net model not only offers high accuracy and stability but also provides a reliable technical foundation for agricultural disaster monitoring and precision management using high-resolution UAV imagery.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"13 ","pages":"Article 101689"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685214","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}

引用次数: 0

EPAF-DETR:Efficient transformer-based model for abnormal fish behavior detection under water quality anomalies EPAF-DETR：基于变压器的水质异常下鱼类异常行为检测的高效模型

IF 5.7

Smart agricultural technology Pub Date : 2026-03-01 Epub Date: 2025-12-05 DOI: 10.1016/j.atech.2025.101681

Xun Chen , Chuang Wang , Siyu Wu , Xinjian Ou

{"title":"EPAF-DETR:Efficient transformer-based model for abnormal fish behavior detection under water quality anomalies","authors":"Xun Chen , Chuang Wang , Siyu Wu , Xinjian Ou","doi":"10.1016/j.atech.2025.101681","DOIUrl":"10.1016/j.atech.2025.101681","url":null,"abstract":"<div><div>In aquaculture management, the prompt recognition of abnormal fish behaviors induced by external stimuli or diseases is crucial for enhancing breeding efficiency and securing economic returns for farmers. Nevertheless, monitoring such behaviors remains challenging owing to intricate aquatic environments, frequent occlusions, and considerable visual similarities across different behavioral categories. To tackle these issues, this study introduces an Efficient Parallel Attention Fusion Detection Transformer, designated as EPAF-DETR, which is developed to achieve high-precision and robust object detection. By integrating EfficientViT as the backbone network, the computational complexity of the model is significantly reduced. Combined with an adaptive sparse self-attention mechanism and a spatially enhanced feedforward network, an improved AIFI module is introduced to strengthen feature extraction capabilities. Furthermore, Multi-Level Hierarchical Attention Fusion module is designed to enhance the original cross-scale feature fusion component in RT-DETR, enhancing the salience of critical features and further improving detection accuracy. Finally, by incorporating Matchability-Aware Loss function, the model is guided to place greater emphasis on matching low-quality features.These architectural advancements considerably boost the model’s adaptability in demanding underwater settings and augment its capacity to discriminate fine-grained behavioral characteristics of fish. Experimental outcomes indicate that EPAF-DETR attains detection performance while reducing computational costs, achieving an average F1-score of 94 % and a mAP of 95.7 %. In conclusion, the proposed approach effectively addresses detection difficulties in complex aquaculture environments, enabling accurate and reliable identification of anomalous fish behaviors.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"13 ","pages":"Article 101681"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737823","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}

引用次数: 0

RAIL-WG : Robotic imitation learning for waypoint generation in agricultural autonomous driving RAIL-WG：农业自动驾驶中航路点生成的机器人模仿学习

IF 5.7

Smart agricultural technology Pub Date : 2026-03-01 Epub Date: 2025-12-04 DOI: 10.1016/j.atech.2025.101682

Sun Ho Jang, Yong Jun Lee, Woo Jin Ahn, Myo Taeg Lim

{"title":"RAIL-WG : Robotic imitation learning for waypoint generation in agricultural autonomous driving","authors":"Sun Ho Jang, Yong Jun Lee, Woo Jin Ahn, Myo Taeg Lim","doi":"10.1016/j.atech.2025.101682","DOIUrl":"10.1016/j.atech.2025.101682","url":null,"abstract":"<div><div>Waypoint generation is a critical component of autonomous navigation, directly affecting trajectory accuracy, operational efficiency, and system robustness. Traditional fixed-interval strategies are computationally simple but lack adaptability to dynamic environments, whereas reinforcement learning (RL) methods often face unstable training and limited generalization. To overcome these challenges, we introduce robotic imitation learning for waypoint generation in agricultural autonomous driving (RAIL-WG), an LSTM-based imitation learning framework trained on expert demonstrations. Using the GROW dataset, which contains large-scale, high-resolution GPS trajectories from real-world orchard operations, RAIL-WG learns curvature-adaptive waypoint placement that balances density between straight and curved paths. Extensive simulations and field experiments show that RAIL-WG consistently outperforms both fixed-interval and RL-based baselines in trajectory tracking accuracy, computational efficiency, and smoothness. Beyond agricultural applications, the proposed framework demonstrates strong potential as a generalizable AI model for waypoint optimization, applicable to diverse autonomous systems such as mobile robots, UAVs, and ground vehicles operating in unstructured environments. This versatility highlights RAIL-WG as a scalable solution for adaptive navigation across heterogeneous domains.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"13 ","pages":"Article 101682"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737821","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}

引用次数: 0

An optimization framework for intelligent irrigation system installation in fragmented paddy fields 破碎稻田智能灌溉系统安装优化框架

IF 5.7

Smart agricultural technology Pub Date : 2026-03-01 Epub Date: 2025-12-18 DOI: 10.1016/j.atech.2025.101740

Runze Tian , Kyoji Takaki , Toshiaki Iida , Keigo Noda , Yohei Asada

{"title":"An optimization framework for intelligent irrigation system installation in fragmented paddy fields","authors":"Runze Tian , Kyoji Takaki , Toshiaki Iida , Keigo Noda , Yohei Asada","doi":"10.1016/j.atech.2025.101740","DOIUrl":"10.1016/j.atech.2025.101740","url":null,"abstract":"<div><div>The fragmented structure of Japanese paddy fields increases labor requirements for patrol and irrigation management. While Intelligent Irrigation System (IIS) units can effectively reduce labor input, their benefits are influenced by the location of the installations. Consequently, determining optimal field combinations under varying installation conditions (varying numbers of IIS units and farmer datasets) has become a critical issue. This study proposes and validates a two-stage optimization framework for IIS unit installation that employs patrol-route distance reduction as the evaluation metric. In the first stage, Density-based Spatial Clustering of Applications with Noise (DBSCAN) with the Normalized Nearest-distance (NN-distance) method was applied to mitigate search space explosion under non-uniform densities. In the second stage, the 2-opt algorithm was used to optimize patrol routes and quantify labor reduction. Validation results showed that the framework compressed the candidate solution space and alleviated the computational complexity associated with the Non-deterministic Polynomial-time hard (NP-hard) nature of the problem. Furthermore, the NN-distance method maintained solution quality and outperformed the conventional k-distance approach by mitigating over-clustering and over-segmentation under non-uniform spatial distributions. Case analyses revealed that the benefits of IIS unit installation depend not only on the number of installed units but also strongly on the spatial distribution of fields. Overall, the proposed framework enhances the applicability of DBSCAN to non-uniform spatial data, provides guidance for differentiated installation strategies, and offers a reproducible methodological framework for deploying smart agricultural technologies in fragmented agricultural systems.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"13 ","pages":"Article 101740"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925408","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}

引用次数: 0

Discrimination of vineyard origin and grape cultivars using rapid mass spectrometry and machine learning 利用快速质谱和机器学习技术鉴别葡萄园产地和葡萄品种

IF 5.7

Smart agricultural technology Pub Date : 2026-03-01 Epub Date: 2025-12-30 DOI: 10.1016/j.atech.2025.101755

Khokan Kumar Saha , Rabin Dulal , Leigh Schmidtke , Lihong Zheng , Xiaodi Huang , Andrew Clark

{"title":"Discrimination of vineyard origin and grape cultivars using rapid mass spectrometry and machine learning","authors":"Khokan Kumar Saha , Rabin Dulal , Leigh Schmidtke , Lihong Zheng , Xiaodi Huang , Andrew Clark","doi":"10.1016/j.atech.2025.101755","DOIUrl":"10.1016/j.atech.2025.101755","url":null,"abstract":"<div><div>Identifying grape cultivars and vineyard origins is essential for ensuring wine quality, traceability, and authenticity. This study presents a rapid liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-qTOF MS) approach that substantially reduces analysis time compared with conventional methods. Mass spectral data of grape cultivars collected from 11 vineyards across New South Wales, Australia, within an <em>m</em>/<em>z</em> range of 45-1500 were analysed to discriminate cultivars based on their geographical origin. To address the high dimensionality of the dataset, feature extraction was performed using principal component analysis (PCA), supervised stacked autoencoder (SAE), and uniform manifold approximation and projection (UMAP). Among these dimensionality reduction techniques, PCA exhibited the most robust and consistent performance. Fifteen machine learning models were then evaluated to assess classification accuracy. The Random Forest (RF) model, when combined with PCA, achieved the highest accuracy (95.6%), effectively distinguishing grape cultivars from the 11 vineyard sites. Overall, these findings demonstrate that integrating rapid LC-qTOF MS with machine learning provides a powerful and efficient framework for authenticating grape cultivars and classifying vineyard origins, highlighting the potential of data-driven approaches for food provenance and quality assurance.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"13 ","pages":"Article 101755"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925631","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}

引用次数: 0

Corn stem diameter and ear orientation angle measurement method based on D3-YOLOv11 and RGB-D camera 基于D3-YOLOv11和RGB-D相机的玉米茎径和穗位角测量方法

IF 5.7

Smart agricultural technology Pub Date : 2026-03-01 Epub Date: 2025-12-27 DOI: 10.1016/j.atech.2025.101760

Fan Zhang , Qiankun Fu , Yang Li , Hengyi Wang , Jun Fu

{"title":"Corn stem diameter and ear orientation angle measurement method based on D3-YOLOv11 and RGB-D camera","authors":"Fan Zhang , Qiankun Fu , Yang Li , Hengyi Wang , Jun Fu","doi":"10.1016/j.atech.2025.101760","DOIUrl":"10.1016/j.atech.2025.101760","url":null,"abstract":"<div><div>The operational effect of the reverse ear picking device for fresh corn is affected by stem diameter and ear orientation angle. The existing devices lack the ability to sense these parameters in real-time, making it difficult to dynamically adjust operating parameters, which leads to a high damage rate and harvest loss. To this end, this study focuses on the visual perception aspect and proposes a recognition method based on a depth camera and an improved D3-YOLOv11 segmentation model, which provides reliable visual input for subsequent adaptive regulation. Specifically, this study proposes Dual-Domain Dynamic Gate Conv (D3GConv) to enhance the multi-scale feature extraction ability of the model. In the neck network, a bidirectional weighted pyramid structure with semantic detail injection is designed to improve the segmentation accuracy of small objects. Generalized Focal Loss V2 was used to optimize the detection head to enhance the accuracy of boundary localization in dense stem scenes. Finally, the depth information is fused to realize the real-time measurement of stem diameter and ear orientation angle. Experimental results show that the Mask-mAP50 of the D3-YOLOv11 model reaches 99.3% and 94.6% in stem and ear instance segmentation tasks, respectively. The Mean Absolute Error of stem diameter measurement based on depth information is only 0.16 cm, and the Coefficient of Determination of ear orientation angle reaches 0.95, which verifies the reliability and practicability of this method in the adaptive control of the ear harvesting device. It provides an effective visual perception basis for improving the intelligence level of equipment.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"13 ","pages":"Article 101760"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925636","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}

引用次数: 0

Research on a blockage detection method for the suction pipeline of the fan system in a pneumatic jujube picker 气动采枣机风机系统吸入管路堵塞检测方法的研究

IF 5.7

Smart agricultural technology Pub Date : 2026-03-01 Epub Date: 2025-12-24 DOI: 10.1016/j.atech.2025.101745

Keyi Jiang , Huizhe Ding , Hang Yin , Longpeng Ding , Zhentao Wang , Yanbin Zhang , Wu Guo , Hongfei Yang , Jingbin Li

{"title":"Research on a blockage detection method for the suction pipeline of the fan system in a pneumatic jujube picker","authors":"Keyi Jiang , Huizhe Ding , Hang Yin , Longpeng Ding , Zhentao Wang , Yanbin Zhang , Wu Guo , Hongfei Yang , Jingbin Li","doi":"10.1016/j.atech.2025.101745","DOIUrl":"10.1016/j.atech.2025.101745","url":null,"abstract":"<div><div>To address the problems of insufficient detection accuracy, poor real-time performance, and lack of long-term adaptability in blockage detection for the suction pipeline of the fan system in pneumatic jujube pickers, this study proposes an intelligent detection and operation–maintenance method that integrates multi-task learning and digital twin technology. A JujubePipe-BlockMTL-LightGBM multi-task learning model is constructed to simultaneously identify the blockage location (front, middle, rear) and determine the blockage area ratio (six levels) within a unified framework, thereby overcoming the limitation of traditional single-task models that ignore the physical correlation between tasks. Furthermore, the proposed model is embedded into a digital twin-based real-time monitoring system. Through cyber–physical mapping and closed-loop control, online fault diagnosis is achieved, and a model self-evolution mechanism is introduced to cope with data distribution drift, ensuring long-term stability and accuracy of the system. Experimental results show that, on the test set, the proposed model achieves 100% recognition accuracy for front and middle blockages and 98.18% for rear blockage, significantly outperforming traditional machine learning and deep learning baseline models. In a 72 h continuous test, the overall diagnostic accuracy of the digital twin system reaches 98.67%, with no false alarms for severe blockages, thereby verifying the comprehensive advantages of the proposed method in terms of high accuracy, strong robustness and continuous self-adaptation. This work provides an effective technical pathway for intelligent operation and maintenance of pneumatic conveying agricultural equipment.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"13 ","pages":"Article 101745"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925560","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}

引用次数: 0

A capability maturity model for assessing digital integration in smart farming 用于评估智能农业中数字集成的能力成熟度模型

IF 5.7

Smart agricultural technology Pub Date : 2026-03-01 Epub Date: 2025-12-24 DOI: 10.1016/j.atech.2025.101743

Emmanuel Ahoa , Ayalew Kassahun , Cor Verdouw , Bedir Tekinerdogan , Joep Tummers

{"title":"A capability maturity model for assessing digital integration in smart farming","authors":"Emmanuel Ahoa , Ayalew Kassahun , Cor Verdouw , Bedir Tekinerdogan , Joep Tummers","doi":"10.1016/j.atech.2025.101743","DOIUrl":"10.1016/j.atech.2025.101743","url":null,"abstract":"<div><div>The advancements of smart farming are noteworthy, largely driven by rapid developments in digital technologies such as the Internet of Things, Big Data, and AI. However, the mere availability of these technologies does not guarantee their effective integration into agricultural systems. Aligning the different digital components, such as sensors, platforms, data analytics, and decision-support tools, remains a complex task. This often prevents smart farming systems from reaching their full potential. Limited integration results in isolated data flows, interoperability problems, and inefficiencies across farm operations. This study presents a comprehensive Capability Maturity Model (CMM) for assessing the level of digital integration in smart farming from both technical and organisational perspectives. The model defines five maturity levels ranging from fragmented manual operations to a fully integrated and optimized level (Ad hoc, Managed, Integrated, Predictable, Innovative). It assesses the maturity of capabilities across six key dimensions: business processes, people and culture, strategy, technology, digital governance and data and analytics. A multi-case study of three smart farms in the Netherlands was conducted to validate the model. The findings indicate that the proposed model provides a holistic and practical framework for assessing digital integration maturity across different contexts. It not only supports strategic planning for interoperability but also identifies critical integration challenges and promotes a whole-farm approach to smart agriculture literature. As a decision-support tool, it provides agri-food practitioners with concrete and tailored guidance on which specific capabilities need to be improved to advance the maturity of smart farming.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"13 ","pages":"Article 101743"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925561","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}

引用次数: 0