Smart agricultural technology最新文献

{"title":"Optimization of irrigation and fertigation in smart agriculture: An IoT-based micro-services framework","authors":"Tommaso Adamo ,&nbsp;Danilo Caivano ,&nbsp;Lucio Colizzi ,&nbsp;Giovanni Dimauro ,&nbsp;Emanuela Guerriero","doi":"10.1016/j.atech.2025.100885","DOIUrl":"10.1016/j.atech.2025.100885","url":null,"abstract":"<div><div>Efficient management of water and fertilizer resources is crucial for achieving sustainability and productivity in agriculture. This paper presents an AI-powered microservices solution that optimizes irrigation and fertigation practices. The proposed system integrates IoT nodes for real-time data collection on environmental conditions, soil moisture levels, and nutrient crop needs. Fertigation and irrigation decision-making are modeled as a data-driven sequential decision problem. At each decision stage, real-time data serve as input to an AI planning model aimed at satisfying nutrient and water demands while minimizing water and fertilizer waste. The system allows supervision by the farmer through a mobile app and a Digital Twin, enabling the design of crop planting layouts and providing detailed information on real-time decisions implemented in the field, as well as water and fertilizer consumption. The proposed solution manages diverse crop species with distinct water and nutrient requirements. Efficient data exchange is facilitated through a push-pull communication paradigm between the IoT nodes and cloud services. This approach offers several benefits, including greater control over data flow, energy savings, and increased flexibility in resource management.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100885"},"PeriodicalIF":6.3,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143620831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"WeedsSORT: A weed tracking-by-detection framework for laser weeding applications within precision agriculture","authors":"Tao Jin,&nbsp;Kun Liang,&nbsp;Mengxuan Lu,&nbsp;Yingshuai Zhao,&nbsp;Yangrui Xu","doi":"10.1016/j.atech.2025.100883","DOIUrl":"10.1016/j.atech.2025.100883","url":null,"abstract":"<div><div>In precision agriculture, the application of artificial intelligence and high-power laser technology for weed control offers significant efficiency and accuracy advantages. However, it still encounters numerous challenges in the detection and tracking of weed targets. In terms of object detection, the variability in the size and specifications of weeds can result in the missed detection of smaller weed targets. Regarding tracking prediction, the similarity in weed shapes may result in reduced pose estimation accuracy, and the random motion of cameras within laser weeding systems further increases the risk of tracking failures. To address these challenges, this study introduces a spatial attention mechanism to enhance weed detection accuracy. It employs optimized multi-feature layer extraction and optimal feature matching algorithms to derive motion estimation results. Ultimately, an adaptive extended Kalman filtering algorithm is integrated to establish a weed tracking algorithm that correlates temporal and spatial information, ultimately achieving rapid and precise detection and tracking of weeds in laser weeding scenarios. The detection accuracy of the optimized algorithm was tested on both publicly available datasets and self-collected detection datasets, achieving a mean Average Precision (mAP) of 97.29% and 85.83%, respectively. Furthermore, tracking performance was evaluated using the LettuceMOT dataset and the self-collected WeedsMOT dataset, demonstrating improvements in Higher-Order Tracking Accuracy (HOTA) accuracy of 12.01% and 8.75% when compared to the ByteTrack and DeepOCSORT algorithms. The experimental findings substantiate the efficacy of the proposed weed detection and tracking algorithm, offering a valuable reference for the progression of laser weeding technology within precision agriculture.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100883"},"PeriodicalIF":6.3,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143610602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detecting tasseling rate of breeding maize using UAV-based RGB images and STB-YOLO model","authors":"Boyi Tang ,&nbsp;Jingping Zhou ,&nbsp;XiaoLan Li ,&nbsp;Yuchun Pan ,&nbsp;Yao Lu ,&nbsp;Chang Liu ,&nbsp;Kai Ma ,&nbsp;Xuguang Sun ,&nbsp;Dong Chen ,&nbsp;Xiaohe Gu","doi":"10.1016/j.atech.2025.100893","DOIUrl":"10.1016/j.atech.2025.100893","url":null,"abstract":"<div><div>In the regions with limited light and temperature, detecting the tasseling rate of maize is crucial to optimize water and fertilizer management, adjusting harvest schedule and screen suitable varieties. Unmanned Aerial Vehicle (UAV) imaging technology offers a rapid method for detecting the maize tasseling rate. This study proposes a new detection model, STB-YOLO, based on YOLOv8 for detecting maize tasseling rate. At first, we introduced Swin Transformer blocks in the downsampling process to enhances the ability of semantic feature extraction from UAV-based RGB images. Subsequently, the Bidirectional Feature Pyramid Network is employed during the Concat fusion process. This enhances the model's ability to accurately detect and robustly handle targets of varying scales in images with different tasseling rate. Finally, a series of deep learning algorithms are compared and analyzed. Additionally, the model is analyzed in detail by ablation experiment. The results show that at imaging heights of 15 meter and 30 meter, STB-YOLO achieved a precision of 76.2 % and 72.1 %, respectively. This shows an improvement of 6.5 and 11.7 percentage over YOLOv8 and YOLOv6, respectively. The precision of tasseling rate in the test datasets reaches 78.48 % and 73.22 %, with R² of 0.71 and 0.69, respectively. The precision increases as the tasseling rate increases. When the tasseling rate exceeds 80 %, the precision reaches 93.44 % and 87.01 %, respectively. Therefore, applying the STB-YOLO deep learning algorithm to UAV imagery facilitates accurate detection of tasseling rates of breeding maize.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100893"},"PeriodicalIF":6.3,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143685752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transfer and deep learning models for daily reference evapotranspiration estimation and forecasting in Spain from local to national scale","authors":"Yu Ye,&nbsp;Aurora González-Vidal,&nbsp;Miguel A. Zamora-Izquierdo,&nbsp;Antonio F. Skarmeta","doi":"10.1016/j.atech.2025.100886","DOIUrl":"10.1016/j.atech.2025.100886","url":null,"abstract":"<div><div>Accurate estimation and forecasting of Reference Evapotranspiration (<span><math><mi>E</mi><msub><mrow><mi>T</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span>) is critical for almost all agricultural activities and water resource management. However, the most commonly used Penman-Monteith model (FAO56-PM) requires a large amount of input data and it is difficult to compute for general users. Machine Learning (ML) techniques can be used to address this shortcoming. Nevertheless, most studies are site-specific and lack generalizability. This study compares standard ML and Deep Learning (DL) algorithms for estimating and forecasting daily <span><math><mi>E</mi><msub><mrow><mi>T</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span> at different spatial scales in Spain. While Transfer Learning (TL) is a well-established ML technique, its application in <span><math><mi>E</mi><msub><mrow><mi>T</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span> computation remains largely unexplored. We applied TL in a novel approach to retrain DL models, enabling adaptation to diverse local climatic conditions, which is particularly important in this domain. All possible combinations of FAO56-PM inputs were evaluated. The results showed that with three or more climatic variables, the TL process can consistently reduce errors by using an appropriate amount of new data to retrain the models. In estimation, with 20% (120 days) of new data, TL models can provide the same performance as if they were trained with local data, both regionally and nationally (improvement of MAE from 26.4% to 99.5%). During forecasting, we used predicted weather data as input, and despite inherent biases in some variables, the TL models successfully adapted using 9-36 days of new data, significantly improving predictive performance (reducing MAE from -1.1% to 134.3%). Thus, the TL process is highly recommended as a promising methodology for increasing the generalization capability of DL models in both daily <span><math><mi>E</mi><msub><mrow><mi>T</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span> estimation and forecasting under diverse climatic conditions with limited local data.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100886"},"PeriodicalIF":6.3,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new, low-cost ground-based NDVI sensor for manual and automated crop monitoring","authors":"Reena Macagga ,&nbsp;Geoffroy Sossa ,&nbsp;Yvonne Ayaribil ,&nbsp;Rinan Bayot ,&nbsp;Pearl Sanchez ,&nbsp;Jürgen Augustin ,&nbsp;Sonoko Dorothea Bellingrath-Kimura ,&nbsp;Mathias Hoffmann","doi":"10.1016/j.atech.2025.100892","DOIUrl":"10.1016/j.atech.2025.100892","url":null,"abstract":"<div><div>Ground-based normalized difference vegetation index (NDVI) sensors are vital for accurate, localized crop condition and growth assessments, but their high cost and labor-intensive operation limit accessibility. To close this gap, this study presents a low-cost NDVI sensor priced under €250, offering an affordable yet high-accuracy crop monitoring tool. The device has dual functionality, operating in both manual (handheld) and automatic (standalone) modes, enabling continuous crop monitoring with higher temporal resolution and reduced labor costs. This study also identified and corrected the underestimation of measurements at higher NDVI values through sensor calibration. Subsequent field validation proved the accuracy of the low-cost sensor, showing a generally good overall agreement with results obtained with the reference sensor (<em>r²</em> = 0.99) after applying the derived calibration function. Extended field trials in Benin and the Philippines demonstrated the reliability of the device to adequately monitor treatment differences in various crop development and biomass accumulation. Further customization into automatic mode enabled continuous, high-frequency NDVI measurements, showing its ability to monitor crop phenological changes, such as senescence, in an additional field testing in Germany. Overall, this study demonstrates that the developed NDVI sensor device, made from affordable, off-the-shelf components, can be adapted into a scientifically usable NDVI sensor that is accurate, reliable, and cost-effective. It offers a viable alternative to expensive in-field monitoring systems and promotes accessibility to ground-based crop monitoring solutions, especially for research in the Global South.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100892"},"PeriodicalIF":6.3,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143620829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hyperspectral indicators and characterization of glyphosate-induced stress in common lambsquarters (Chenopodium album L.)","authors":"Mario Soto ,&nbsp;Aurelie M. Poncet ,&nbsp;Nilda Roma-Burgos ,&nbsp;O. Wesley France ,&nbsp;Juan C. Velasquez ,&nbsp;Amanda J. Ashworth ,&nbsp;Kristofor R. Brye ,&nbsp;Cengiz Koparan","doi":"10.1016/j.atech.2025.100890","DOIUrl":"10.1016/j.atech.2025.100890","url":null,"abstract":"<div><div>Hyperspectral sensors are increasingly used to develop optimized vegetation indices (VIs) that capture plant spectral response to specific stressors. The project goal was to develop quantitative metrics for characterization of weed response to herbicide application. This work applied hyperspectral sensing to describe and predict the spectral response of common lambsquarters (<em>Chenopodium album</em> L., CHEAL) to glyphosate application. Thirteen treatments, including one glyphosate rate used alone or in combination with eleven adjuvants plus one nontreated control, were applied to CHEAL seedlings cultivated in a greenhouse. Visible injury ratings and non-imaging hyperspectral data were collected 14 days after treatment application. Sensor data processing included cleaning, normalization, smoothing, and spectral reduction. The treatments resulted in a significant (<em>P</em> &lt; 0.001) gradient of injury ranging from 0 to 98 %, with visible differences in leaf spectral signatures. Thirty-one key wavelengths were identified using principal component analysis, relief-f feature selection, and Bayesian discriminant analysis and used to create 45,732 VIs. No single VI accurately described CHEAL injury (minimum mean absolute error (MAE) = 14.0 %). A random forest algorithm developed using four VIs adequately described CHEAL injury with an MAE of 7.7 %. Post-calibration was not needed to improve the random forest model performance (P ≥ 0.05). Therefore, hyperspectral sensing could be used to quantify weed response to herbicide application and overcome the limitations of visual methods current in use. Further development of this method and validation will allow development of a platform for high-throughput phenotyping of weed response to herbicide application and screening for herbicide resistance.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100890"},"PeriodicalIF":6.3,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143620830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unmanned aerial systems (UAS)-based field high throughput phenotyping (HTP) as plant breeders’ toolbox: A comprehensive review","authors":"Ittipon Khuimphukhieo ,&nbsp;Jorge A. da Silva","doi":"10.1016/j.atech.2025.100888","DOIUrl":"10.1016/j.atech.2025.100888","url":null,"abstract":"<div><div>It is projected that food demand will exceed its supply in 2050 due to global population growth if the production rate remains the same. Replacement of natural vegetation by cropland is unsustainable as it could cause global warming worse. Increasing the rate of genetic gain through artificial selection, also known as plant breeding, is a sustainable approach. Phenotyping, a process of measuring plant characteristics (traits), is unavoidable in plant breeding regardless of which methods (molecular or conventional) being used. Traditional phenotyping of a complex trait has been a bottleneck due to its labor-intensive and time-consuming nature. In recent years, there has been a massive scientific investigation on utilizing an unmanned aerial system (UAS) for agricultural application, as well as high throughput phenotyping (HTP) platform. Although there have been existing literature reviews on UAS-based HTP, a review discussing the pipeline of implementing this tool and in what situations or applications plant breeders could utilize it as a tool is still limited. Consequently, this paper overviews (1) a potential bottleneck in plant breeding pipeline, (2) necessary equipment and regular pipeline for implementing UAS-based HTP, (3) various plant phenotyping tasks that could be accomplished by using UAS-based HTP, including a trait-direct measurement, predictive breeding, application of UAS-based HTP as a marker and, identification of quantitative trait loci (QTLs), (4) contributions of UAS-based HTP on improving the rate of genetic gain, and (5) an outline of the future direction of plant breeding in the high throughput era alongside with artificial intelligence. This comprehensive review would be beneficial to plant breeders, especially those who are considering adopting this technology to their programs.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100888"},"PeriodicalIF":6.3,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143591895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research progress of non-destructive testing techniques in moisture content determination","authors":"Song Daihao ,&nbsp;Wang Min ,&nbsp;Li Yanjun ,&nbsp;Xu Lei ,&nbsp;Lou Zhichao","doi":"10.1016/j.atech.2025.100878","DOIUrl":"10.1016/j.atech.2025.100878","url":null,"abstract":"<div><div>Accurate measurement of moisture content is crucial in various industries for improving product quality, ensuring safety, optimizing production processes, and protecting the environment. However, traditional moisture measuring methods often damage the sample and are complex and time-consuming, making it challenging to meet the high demands of modern industries for efficiency, precision, and real-time monitoring. Non-destructive testing (NDT), an advanced technology, can rapidly and accurately assess the characteristics and conditions of materials without damaging their structure, morphology, chemical components, and physical properties, making it suitable for moisture content testing. This paper provides an overview of traditional drying methods for moisture content detection and a comprehensive review of the theoretical basis of electrical resistive, capacitive, and microwave methods, including their applicable detection materials. Moreover, we analyze the advantages and disadvantages of each method. Additionally, the paper highlights that combining non-destructive moisture content detection with machine learning can significantly improve both detection efficiency and accuracy. Finally, we address the challenges associated with non-destructive moisture content detection and explore potential future developments to support the further advancement and adoption of NDT technologies.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100878"},"PeriodicalIF":6.3,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143601304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A non-linear dynamic model for agricultural vehicles constructed in digital space","authors":"Yue Yu ,&nbsp;Yong-joo Kim ,&nbsp;Noboru Noguchi","doi":"10.1016/j.atech.2025.100891","DOIUrl":"10.1016/j.atech.2025.100891","url":null,"abstract":"<div><div>In response to the shortage of agricultural labor due to an aging population, the concept of \"smart agriculture\" has emerged, which attaches great importance to the accurate modeling of real agricultural information in digital space, to realize higher-level intelligent management and control. As an important smart agricultural technology, accurate simulation of agricultural off-road vehicles in digital space can help enhance agricultural productivity, such as optimizing farming task schedule. To achieve this smart agriculture technology, it is necessary to construct high-precision agricultural vehicle models suitable for various agricultural environments in digital space. However, constructing highly precise, realistically performing dynamic models for agricultural vehicles in digital space remains a challenge. The performance of simple kinematic models and traditional linear dynamic models of agricultural vehicles is very limited: these models are only accurate under small side slip conditions, but not suitable for environments that would cause large side slip of agricultural vehicles, such as wet or soft soil. To solve this problem, we here propose a non-linear dynamic model for agricultural vehicles in digital space. First, we combine a simplified non-linear tire model and side slip angle estimation method to make a lateral force-estimation method. We then use the lateral force estimation and the Unity physics engine to construct a non-linear dynamic model for agricultural vehicles in digital space. The validation tests of both digital space and real-world experiments prove that: (1) The proposed model can accurately simulate the status of real tractors even with a simplified set of parameters. (2) The proposed non-linear model has a wider range of environmental applicability than that of traditional linear model, especially for those environments that may cause large side slip. (3) The proposed non-linear model has strong practicality and can cope with the changing agricultural environments by simply tuning the model parameters.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100891"},"PeriodicalIF":6.3,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143610600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic growth tomato inflorescence modeling with elastic mechanics data","authors":"Siyao Liu ,&nbsp;Subo Tian ,&nbsp;Zhen Zhang ,&nbsp;Lingfei Liu ,&nbsp;Tianlai Li","doi":"10.1016/j.atech.2025.100884","DOIUrl":"10.1016/j.atech.2025.100884","url":null,"abstract":"<div><div>Current crop modeling methods can not simulate crop dynamic growth containing physical characteristics data, while the study of physical production management such as pollination of tomato requires the elastic force and dynamic growth process analysis of the inflorescence. Therefore, this study proposes a tomato inflorescence modeling method to meet the modeling demands of combining the growth process and elastic mechanics data. With the model promoted in this study, after measuring the size of tomato inflorescence key structures, the growth progress of inflorescence can be predicted, and elastic mechanics data of inflorescence at various sizes can be obtained. Firstly, based on plant topology theory, tomato inflorescence was divided into structural skeleton and tomato flower (or fruit). Where the structural skeleton was divided into four parts, that are peduncle, primary flower stalk, secondary flower stalk, and pedicel. Then, the diameters, lengths, growth coefficients and elastic coefficients of tomato inflorescence key structures were measured at different growth stages, the principle between key structures size and the elastic coefficient was established. Finally, a software interface is designed based on the MFC framework with the OpenGL library, which can generate dynamically growing inflorescence model, which contain the elastic mechanics data of inflorescence model. The experimental results show that the average prediction error of inflorescence size in the established model is 8.35 %, and the average estimation error of elasticity coefficient is 7.41 %. The study result lays the foundation for the establishment of tomato inflorescence modeling method, which can help to achieve the study of tomato physical production management. The modeling method proposed in this study also provides new ideas and methods for plant modeling that simultaneously simulate crop dynamic growth and contain physical characteristics data.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100884"},"PeriodicalIF":6.3,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143578483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}