Smart agricultural technology最新文献_第5页

Innovative applications of internet of things and machine learning in sustainable agricultural irrigation management: Benefits and challenges 物联网和机器学习在可持续农业灌溉管理中的创新应用：收益与挑战

IF 5.7

Smart agricultural technology Pub Date : 2026-03-01 Epub Date: 2025-11-20 DOI: 10.1016/j.atech.2025.101661

Abdennabi Morchid , Zafar Said , Hamid Tairi

{"title":"Innovative applications of internet of things and machine learning in sustainable agricultural irrigation management: Benefits and challenges","authors":"Abdennabi Morchid , Zafar Said , Hamid Tairi","doi":"10.1016/j.atech.2025.101661","DOIUrl":"10.1016/j.atech.2025.101661","url":null,"abstract":"<div><div>Challenges in the agricultural sector include those relating to climate, demographics, as well as managing water resources. Using the Internet of Things (IoT), as well as the concept of artificial intelligence (AI), particularly machine learning (ML), offer technological solutions to optimize agricultural irrigation. However, the adoption of these technologies in irrigation systems remains limited due to associated risks, system complexity, and technical constraints. This article analyzes smart irrigation by integrating IoT and ML technologies in optimizing crop water use in agriculture. Effective water management is now essential to address challenges such as urbanization, climate change, and food security. IoT- and ML-based solutions offer a promising way to support more efficient and sustainable agricultural practices. This is because IoT systems provide real-time information about environmental and soil conditions. Moreover, ML techniques provide tools that will ensure that this instantaneous information obtained is of much use in making appropriate decisions in carrying out the irrigation process of agricultural products. This work provides a systematic review of the existing literature using the Preferred Reporting Items for Systematic Reviews (PRISMA) methodology. From an initial set of 1,340 publications, 108 studies met the inclusion criteria and were analyzed in depth. These studies offer a comprehensive understanding of IoT- and ML-based smart irrigation systems, highlighting their benefits in improving water conservation, irrigation efficiency, and agricultural productivity</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"13 ","pages":"Article 101661"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145645985","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

MRLCM-YOLO: A lightweight and multi scale enhanced model for detecting cowpea pests in complex field environments MRLCM-YOLO：一种用于复杂田间环境下豇豆害虫检测的轻量级、多尺度增强模型

IF 5.7

Smart agricultural technology Pub Date : 2026-03-01 Epub Date: 2025-11-27 DOI: 10.1016/j.atech.2025.101676

Chunshan Wang , Yifei Dai , PeiPei Sun , Lijie Zhang , Bianyin Wang , Lijuan Duan , Jianchun Wang

{"title":"MRLCM-YOLO: A lightweight and multi scale enhanced model for detecting cowpea pests in complex field environments","authors":"Chunshan Wang , Yifei Dai , PeiPei Sun , Lijie Zhang , Bianyin Wang , Lijuan Duan , Jianchun Wang","doi":"10.1016/j.atech.2025.101676","DOIUrl":"10.1016/j.atech.2025.101676","url":null,"abstract":"<div><div>Insect pests significantly threaten both the yield and quality of crops, particularly in high value varieties such as cowpeas. To address key challenges including dense small object detection, complex and cluttered field environments, class imbalance, and limitations in deployment resources, this study introduces a lightweight yet high accuracy object detection model MRLCM-YOLO based on the YOLOv11 framework. The model incorporates RepViT, a reparameterized vision transformer as its backbone, to enhance both feature expressiveness and inference speed. To improve multi scale contextual representation, a novel feature fusion mechanism termed CGRFPN is proposed. Additionally, the LSKM module, based on large separable kernel attention, is employed to strengthen attention on target regions. A decoupled detection head, MultiSEAMHead, is further integrated to enhance model robustness by disentangling the classification and localization tasks. For training and validation purposes, a high-resolution cowpea pest dataset was curated, comprising 3855 annotated images spanning 19 pest categories. Experimental findings demonstrate that MRLCM-YOLO achieves 87.9 % mAP<sub>50</sub> and 57.2 % mAP<sub>50–95</sub>, representing gains of 0.5 % and 0.9 %, respectively, over YOLOv11. With a model size of just 9.1 million parameters, MRLCM-YOLO achieves an effective trade-off between detection performance and computational cost, rendering it particularly well suited for deployment in practical agricultural scenarios.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"13 ","pages":"Article 101676"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685208","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

Deep learning-based holstein face recognition in real-world farming conditions 基于深度学习的真实农业条件下的霍尔斯坦人脸识别

IF 5.7

Smart agricultural technology Pub Date : 2026-03-01 Epub Date: 2025-12-02 DOI: 10.1016/j.atech.2025.101690

Hang Shu , Zhongming Jin , Gang Guo

{"title":"Deep learning-based holstein face recognition in real-world farming conditions","authors":"Hang Shu , Zhongming Jin , Gang Guo","doi":"10.1016/j.atech.2025.101690","DOIUrl":"10.1016/j.atech.2025.101690","url":null,"abstract":"<div><div>Non-contact biometric identification of cattle using visual facial features is now feasible due to recent advances in computer vision and deep learning. However, the lack of publicly available datasets continues to prevent the development and fair evaluation of robust models. This study attempts to address this gap by proposing Holstein2025, a benchmark dataset of individual cow face images captured using fixed-position side-view surveillance cameras in a commercial dairy farm. Holstein2025 reflects real-world environmental variability and supports both face detection and identification tasks. A systematic evaluation of state-of-the-art backbones and face recognition loss functions was conducted. The final pipeline integrates YOLO11n with an oriented bounding box head for cow face detection and alignment (average precision of 0.995, processing time of 11.2 ms), and a ConvNeXt-Tiny-based face identification network trained with an optimised ArcFace-based loss incorporating two auxiliary terms (accuracy of 0.97, processing time of 0.6 ms per face). Overall, the system runs in real time and achieved a precision of 0.98 in open set testing at a similarity threshold of 0.807. To promote reproducibility and practical application, all code and data are open-sourced and released with this paper.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"13 ","pages":"Article 101690"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685207","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

FEM-Net:Feature-Enhanced Vision Mamba Network for camellia fruit segmentation FEM-Net：用于山茶果实分割的特征增强视觉曼巴网络

IF 5.7

Smart agricultural technology Pub Date : 2026-03-01 Epub Date: 2025-12-20 DOI: 10.1016/j.atech.2025.101738

Yang Li , Hongwei Deng , Songling Xia , Ming Yao , Wenli Fu

{"title":"FEM-Net:Feature-Enhanced Vision Mamba Network for camellia fruit segmentation","authors":"Yang Li , Hongwei Deng , Songling Xia , Ming Yao , Wenli Fu","doi":"10.1016/j.atech.2025.101738","DOIUrl":"10.1016/j.atech.2025.101738","url":null,"abstract":"<div><div>The natural growth conditions of camellia fruits are complex, and factors such as being obscured by branches and leaves, overlapping, and uneven light exposure can significantly affect the harvesting process, resulting in low efficiency. This paper focuses on the camellia fruit in natural settings and proposes a novel image segmentation algorithm for camellia fruits, called FEM-Net. First, we introduce the Mamba Attention Fusion block (MAF block) to enhance feature extraction, helping the model focus on the target areas and more accurately identify the morphological features of the camellia fruit. Additionally, we design the Global-Local Aggregation Module (GLA) to improve the model’s ability to perceive both local and global information, thus enhancing its capacity to capture the fine details of the camellia fruit. Experimental validation shows that FEM-Net achieves Precision, Recall, and F1 scores of 93.48 %, 95.76 %, and 94.52 %, respectively, on the camellia fruit dataset. Compared to the baseline model, the Precision, Recall, and F1 scores improved by 3.74 %, 3.60 %, and 3.85 %, respectively, outperforming other segmentation models.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"13 ","pages":"Article 101738"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925409","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

GAPose-GS: Globally adaptive pose-optimized gaussian splatting for plant 3D reconstruction towards more precise phenotyping GAPose-GS：全球自适应姿态优化高斯喷溅植物三维重建更精确的表型

IF 5.7

Smart agricultural technology Pub Date : 2026-03-01 Epub Date: 2025-12-24 DOI: 10.1016/j.atech.2025.101746

Minke Hong , Jinghua Xu , Zhangtong Sun , Xudong Hu , Yifei Zhao , Pan Gao , Yuntao Ma , Jin Hu , Shijie Tian

{"title":"GAPose-GS: Globally adaptive pose-optimized gaussian splatting for plant 3D reconstruction towards more precise phenotyping","authors":"Minke Hong , Jinghua Xu , Zhangtong Sun , Xudong Hu , Yifei Zhao , Pan Gao , Yuntao Ma , Jin Hu , Shijie Tian","doi":"10.1016/j.atech.2025.101746","DOIUrl":"10.1016/j.atech.2025.101746","url":null,"abstract":"<div><div>High-precision plant phenotyping requires efficient 3D reconstruction with high fidelity, yet existing methods such as MVS and NeRF all have problems of feature dependence and error accumulation during 3D reconstruction, which leads to geometric distortion in reconstruction and restricts the reconstruction efficiency. To address this bottleneck, this study first determined the multi-view image acquisition strategy. Further, based on the self-built multi-view dataset of chili peppers, it proposed an algorithm for efficient and high-fidelity 3D reconstruction of complex plant structures through global adaptive pose optimization and gaussian splash rendering technology, referred to as the GAPose-GS algorithm. Experimental results indicate that the Peak Signal-to-Noise Ratio (<em>PSNR</em>) improves by 52.0 %, 26.4 %, and 4.2 % compared to NeRF, Instant-NGP, and 3D Gaussian Splatting respectively. Additionally, the Structural Similarity Index Measure (<em>SSIM</em>) increases by 22.9 %, 12.8 %, and 4.3 % respectively over above methods. The point cloud data reconstructed based on this algorithm also has advantages in the measurement of phenotypic parameters. Compared with the actual measured values, the <em>R²</em> of the phenotypic parameters such as pepper plant height, canopy width, and leafstalk angle obtained in this study are 0.997, 0.954 and 0.978 respectively, and the <em>RMSE</em> are 0.236 cm, 1.082 cm and 2.344° respectively, and the <em>MAE</em> are 0.209 cm, 0.880 cm and 1.965° respectively. The accuracy was significantly better than that of the existing phenotypic calculation methods. Verification across different growth stages of wheat and maize was performed universally, with all errors remaining below 1.1 %, providing new ideas and technologies for high-precision, low-cost, and high-throughput crop phenotypic research.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"13 ","pages":"Article 101746"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925562","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

Intelligent recognition of basic camel behaviors based on DAF-Net for free-grazing camels 基于DAF-Net的自由放牧骆驼基本行为智能识别

IF 5.7

Smart agricultural technology Pub Date : 2026-03-01 Epub Date: 2025-12-29 DOI: 10.1016/j.atech.2025.101763

Fang Zhou , Yuting Xian , Leifeng Guo , Cheng Peng , Yanhong Liu , Xiang Liu , Jing Xiao , Min Tian

{"title":"Intelligent recognition of basic camel behaviors based on DAF-Net for free-grazing camels","authors":"Fang Zhou , Yuting Xian , Leifeng Guo , Cheng Peng , Yanhong Liu , Xiang Liu , Jing Xiao , Min Tian","doi":"10.1016/j.atech.2025.101763","DOIUrl":"10.1016/j.atech.2025.101763","url":null,"abstract":"<div><div>With the rapid development of the camel milk industry and tourism, camel farming is gradually emerging. The basic behaviors of camels (such as standing, walking, grazing, and resting) are important indicators of their health status and welfare level. Timely monitoring and analysis of these behaviors are crucial for assessing the physiological state of camels and optimizing their management. To achieve automated recognition of camel behaviors, this paper constructed a dataset of basic camel behaviors under free-grazing conditions and designed DAF-Net, a network built upon the Yolov12 framework. In the feature extraction stage, DAF-Net employs Dfe-Net (Dynamic feature extraction Net) for efficient representation learning. Three dynamically adaptive modules (C3BA, A2Dy, and AGU) were integrated to further enhance overall performance. In addition, during the data processing stage, The G-Edge SSIM algorithm is proposed in this paper to address the issue of excessive similarity between consecutive frames, and it operates without the need for GPU computational resources. Experimental results demonstrate that the proposed method achieved excellent recognition accuracy for four basic behaviors—resting, grazing, standing, and walking—at 99.0%, 96.8%, 89.5%, and 89.2%, respectively, with an overall accuracy of 93.6%. Moreover, the method enables multi-camel behavior recognition in video sequences, providing a feasible approach for camel health assessment and welfare monitoring. This study offers new insights into intelligent camel farming management and biodiversity development in free-grazing pastures of arid regions.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"13 ","pages":"Article 101763"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925566","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

Two-stage rainfall forecasting and crop classification using puma-optimized ANFIS–SVM 基于puma优化anfiss - svm的两阶段降雨预报与作物分类

IF 5.7

Smart agricultural technology Pub Date : 2026-03-01 Epub Date: 2025-12-13 DOI: 10.1016/j.atech.2025.101719

Thandra Jithendra , A Divya , Ibrahim Aljubayri , T Haritha , Mohammad Zubair Khan

{"title":"Two-stage rainfall forecasting and crop classification using puma-optimized ANFIS–SVM","authors":"Thandra Jithendra , A Divya , Ibrahim Aljubayri , T Haritha , Mohammad Zubair Khan","doi":"10.1016/j.atech.2025.101719","DOIUrl":"10.1016/j.atech.2025.101719","url":null,"abstract":"<div><div>The objective of this research is to design a sophisticated agricultural recommendation framework that combines the adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM), and Puma optimizer (PO). This framework is known to as ANFIS-SVM-PO. The study adopted the agricultural dataset of India with 48 samples (<span><span>https://www.kaggle.com/datasets/atharvaingle/crop-recommendation-dataset</span><svg><path></path></svg></span>), which encompasses weather features such as potassium, phosphorous, nitrogen, pH value of soil, temperature, humidity and rainfall. Then, the ANFIS-SVM-PO framework is assessed by dividing the data into two sets: training (70 %), and testing (30 %). To begin, the model forecasts optimal rainfall by leveraging the input features. In the next step, the model determines the optimal crop by analysing the soil's characteristics and estimated rainfall. Moreover, a comparative analysis is performed to highlight the robustness of ANFIS-SVM-PO in comparison to ANFIS-SVM, ANFIS-SVM-PSO, ANFIS-SVM-COOT, ANFIS-SVM-RSA, and ANFIS-SVM-COA. The experimental evidence has demonstrated the exact metrics of ANFIS-SVM-PO, which achieved RMSE (6.9312), MAE (5.7083), MAPE (0.0595), and 99.15 % accuracy in rainfall prediction and 90 % accuracy in the classification of the finest crops (rice, maize, watermelon, and coffee). Thus, the insights gained from the work can help inform the selection of the best possible crop based on ecological constraints using ANFIS-SVM-PO.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"13 ","pages":"Article 101719"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925721","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

Early detection of pre-symptomatic downy mildew (Pseudoperonospora cubensis) infection in cucumbers by using deep learning tools based on hyperspectral imagery 利用基于高光谱图像的深度学习工具早期检测黄瓜症状前霜霉病（cubensis伪operonospora）感染

IF 5.7

Smart agricultural technology Pub Date : 2026-03-01 Epub Date: 2025-12-15 DOI: 10.1016/j.atech.2025.101726

Jose A. Fernandez-Gallego , Guillem Segarra , Mª Isabel Trillas , Maria Barceló , Patricia Gjakoni , Shawn C. Kefauver , José L. Araus

{"title":"Early detection of pre-symptomatic downy mildew (Pseudoperonospora cubensis) infection in cucumbers by using deep learning tools based on hyperspectral imagery","authors":"Jose A. Fernandez-Gallego , Guillem Segarra , Mª Isabel Trillas , Maria Barceló , Patricia Gjakoni , Shawn C. Kefauver , José L. Araus","doi":"10.1016/j.atech.2025.101726","DOIUrl":"10.1016/j.atech.2025.101726","url":null,"abstract":"<div><div>Downy mildew is one of the most severe diseases affecting cucumber cultivation in both field and greenhouse environments. Early detection is crucial for implementing effective management strategies to minimize or prevent disease spread. Hyperspectral imaging (HSI) offers a powerful non-invasive approach for plant disease detection by capturing detailed spectral information. In this study, a deep learning-based detection system was developed for the pre-symptomatic identification of downy mildew in cucumber. Hyperspectral images were acquired using a Specim IQ camera at 24- and 48-hours post-inoculation, including both control (healthy) and infected samples to construct the dataset. Preprocessing steps included Minimum Noise Fraction (MNF) transformation, Principal Component Analysis (PCA), and data augmentation. The Hyper3DNet deep neural network (DNN) architecture was employed for classification, and cross-validation (CV) was conducted to ensure model reliability. Model performance was evaluated using subsets of spectral bands derived from PCA. The coefficient of variation was calculated to support the selection of the optimal number of PCA bands, with the lowest value obtained at 12 bands. PCA loadings were used to assess the contribution of the original bands to the principal components, highlighting influential wavelengths in the 750–1000 nm range, as well as around 400 nm and 560 nm. The results demonstrate robust classification performance, with the mean accuracy, precision, recall, and F1-score values for 24-hours, 48-hours and combined (24h + 48h) groups being above 81.50, 71.61 and 73.28, respectively, across the evaluated metrics. This highlighted the potential of deep learning and hyperspectral imaging for early disease detection in cucumber.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"13 ","pages":"Article 101726"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925723","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

TraitDiscover: An automated high-throughput platform for multimodal plant phenotyping with real-time trait analysis TraitDiscover：一个自动化的高通量平台，用于多模式植物表型分析和实时性状分析

IF 5.7

Smart agricultural technology Pub Date : 2026-03-01 Epub Date: 2025-12-03 DOI: 10.1016/j.atech.2025.101691

Lingli Xu , Jiafei Zhang , Shijie Li , Guanlin Zhu , Hongbing Zhao , Jingjing Yang , Wenjing Li , Hanyu Nie , Wenqi An , Bingpeng Yang , Weijuan Hu , Zhiguo Han

{"title":"TraitDiscover: An automated high-throughput platform for multimodal plant phenotyping with real-time trait analysis","authors":"Lingli Xu , Jiafei Zhang , Shijie Li , Guanlin Zhu , Hongbing Zhao , Jingjing Yang , Wenjing Li , Hanyu Nie , Wenqi An , Bingpeng Yang , Weijuan Hu , Zhiguo Han","doi":"10.1016/j.atech.2025.101691","DOIUrl":"10.1016/j.atech.2025.101691","url":null,"abstract":"<div><div>Plant phenotyping is essential for elucidating genotype–environment interactions, yet conventional methods remain labor-intensive and low-throughput. TraitDiscover transcends these constraints by uniting multimodal sensing with tightly coupled hardware-software orchestration in a single, end-to-end phenotyping platform. Aligned with the ”Plant Phenotyping Trinity” framework, the system comprises a millimetre-accurate triaxial automation unit, a modular sensor array–RGB imaging, three-dimension laser scanner or LiDAR (3D), infrad (IR) thermal imaging, hyperspectral imaging (HSI), and photosynthesis (PS) imaging–and the dedicated software TraitNavigator suite into one cohesive system. A unified spatiotemporal synchronization mechanism enables robust time-series analysis and fusion of multisource phenotypic data across the entire crop growth period, while the DepthCropSeg algorithm and a night-time imaging module enhance trait extraction under complex conditions, providing G × E × P-ready, multimodal phenotypic datasets. Validation across soybean, maize, and rice trials demonstrated high sensitivity—detecting drought stress four days before visible symptoms, identifying glyphosate injury 24 hours ahead of manual scoring, and quantifying local adaption patterns across ecological gradients. While challenges remain in scaling to complex open-field conditions, TraitDiscover offers a scalable, data-driven approach to accelerate stress phenotyping and breeding decisions and is readily poised for deeper integration with AI to advance sustainable agriculture.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"13 ","pages":"Article 101691"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737829","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

Forecasting spring frost events in agriculture using machine learning: A case study from southeastern Massachusetts, United States 利用机器学习预测农业春季霜冻事件：以美国马萨诸塞州东南部为例

IF 5.7

Smart agricultural technology Pub Date : 2026-03-01 Epub Date: 2025-12-13 DOI: 10.1016/j.atech.2025.101720

Sandeep Bhatti , Peter Jeranyama , Casey D. Kennedy , Anthony R. Buda , David J. Millar , Adrian R.H. Wiegman , Juan E. Zalapa

{"title":"Forecasting spring frost events in agriculture using machine learning: A case study from southeastern Massachusetts, United States","authors":"Sandeep Bhatti , Peter Jeranyama , Casey D. Kennedy , Anthony R. Buda , David J. Millar , Adrian R.H. Wiegman , Juan E. Zalapa","doi":"10.1016/j.atech.2025.101720","DOIUrl":"10.1016/j.atech.2025.101720","url":null,"abstract":"<div><div>Agricultural crops are frequently exposed to severe frost events in the spring, which can greatly impact yield quantity and quality. To manage frost effectively, early warning systems are often used to forecast potential spring frost in advance of an event. For the Massachusetts cranberry industry, the majority of growers use a frost forecasting model developed in the 1940s called the “Franklin model”, but many growers have questioned the accuracy of the model and are eager for a more robust and accurate approach. In this study, we aimed to improve spring frost forecasting in cranberries by combining machine learning techniques with field observations of canopy-level air temperature data from cranberry farms in southeastern Massachusetts. Nine random forest models were developed to predict daily minimum canopy-level air temperature 12 h in advance. We combined mesoscale forecasts from the High-Resolution Rapid Refresh model with meteorological observations from local weather stations to develop our models. Compared to the Franklin model, the random forest models reduced the root mean square error by 5.2–6.2 °C and achieved 91–95 % accuracy in frost classification. The top-performing random forest model achieved a Peirce’s skill score of 0.88, significantly outperforming the Franklin model (0.68) in identifying frost events. The Franklin model exhibited an inherent cold bias which was addressed by reducing false alarms between 56 and 89 % using random forest models. The random forest modeling framework offers a computationally efficient and easily transferable solution in cranberries to reduce frost-related losses of water, nutrients, and crop yields. The methods described in this paper can be adapted for other frost-sensitive crops and integrated with real-time sensor networks for dynamic updates.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"13 ","pages":"Article 101720"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791274","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