Smart agricultural technology最新文献

{"title":"A comprehensive survey on AI-enabled secure social industrial Internet of Things in the agri-food supply chain","authors":"Sajal Halder ,&nbsp;Md Rafiqul Islam ,&nbsp;Quazi Mamun ,&nbsp;Arash Mahboubi ,&nbsp;Patrick Walsh ,&nbsp;Md Zahidul Islam","doi":"10.1016/j.atech.2025.100902","DOIUrl":"10.1016/j.atech.2025.100902","url":null,"abstract":"<div><div>The rapid evolution of Artificial Intelligence (AI) and the Social Industrial Internet of Things (SIIoT) has significantly impacted the agri-food supply chain, offering transformative solutions for security, efficiency, and sustainability. However, challenges related to data integrity, cyber threats, and system interoperability remain. This study provides a comprehensive analysis of AI-enabled secure SIIoT applications in the agri-food supply chain, addressing key security concerns and efficiency bottlenecks. It aims to develop a structured taxonomy of AI-driven security mechanisms, highlighting their roles in safeguarding SIIoT systems. A systematic literature review was conducted using reputable databases, including Google Scholar, ACM, DBLP, IEEE Xplore, SCOPUS, and Web of Science, focusing on peer-reviewed articles from the last six years. Additionally, multiple case studies were examined to validate the real-world application of AI-driven security frameworks in the agri-food industry. The findings indicate that AI-driven security solutions significantly enhance trust management, anomaly detection, and data privacy in SIIoT networks. The proposed taxonomy categorizes AI-enabled security mechanisms into five distinct areas, offering a structured reference for future research and practical implementations. Furthermore, case study analysis demonstrates the successful deployment of AI-driven security in real-world agri-food applications, emphasizing improved traceability and resilience against cyber threats. This study advances the field by identifying gaps in current research, proposing strategic recommendations, and outlining future directions for AI-enabled secure SIIoT systems in the agri-food research domain. The insights presented here provide a strong foundation for researchers, policymakers, and stakeholders in the agri-food sector to build more resilient and intelligent ecosystems.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100902"},"PeriodicalIF":6.3,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714557","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":"Integrating multi-source remote sensing data and machine learning for predicting tree density and cover in Argania spinosa","authors":"Mohamed Mouafik ,&nbsp;Fouad Mounir ,&nbsp;Ahmed El Aboudi","doi":"10.1016/j.atech.2025.100911","DOIUrl":"10.1016/j.atech.2025.100911","url":null,"abstract":"<div><div>This examination explores the application of remote sensing technologies, including Sеntinеl-2, Mohammed VI satellite imagery and Unmanned Aerial Vehicles (UAVs), to predict the cover and density of Argane forest stands in Morocco. The primary objective was to determine the most dependable dataset for estimating these parameters by assessing the performance of various machine learning models. We integrated multiple vegetation indices and compared algorithms such as XGBoost, LightGBM, GBDT, RF and ANN. XGBoost and LightGBM outperformed the other models in estimating tree density using UAV and Mohammed VI data, with XGBoost achieving an impressive R² of 0.99 and RMSE values of 0.05 and 2.85, respectively, demonstrating strong alignment between predicted and measured parameters. Sеntinеl-2 data was particularly effective in predicting vegetation cover for both algorithms, exhibiting an impressive R² of 0.99 and RMSE of 0.34, highlighting a strong correlation. XGBoost and LightGBM consistently delivered the best results for estimating Argane stands density and cover, followed by GBDT, RF, and ANN. Correlation analysis revealed strong positive relationships between vegetation indices (NDVI and SeLI) and Argane stands density and cover across all data sources. The research revealed substantial variability in tree density and cover across different studied regions, with XGBoost model results indicating that the highest density (76.01 trees/ha) was recorded in Essaouira, while the lowest density (43.03 trees/ha) was found in Tiznit/Aït Baha. These findings underscore the importance of selecting appropriate data sources and algorithms for precise ecological assessments and provide valuable insights into the dynamics and ecological status of Argane forest stands, supporting effective forest management and conservation strategies in the context of climate change and environmental degradation.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100911"},"PeriodicalIF":6.3,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747499","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":"Quinoa yield modeling revealed that conservative parameters of the AquaCrop model are not conservative: Evidences of planting methods and irrigation managements","authors":"Sayyed Mohammad Mirsafi ,&nbsp;Ali Reza Sepaskhah ,&nbsp;Seyed Hamid Ahmadi","doi":"10.1016/j.atech.2025.100913","DOIUrl":"10.1016/j.atech.2025.100913","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Water scarcity is the major significant environmental challenge affecting agricultural productivity, particularly in the semi-arid regions of the world. To cope with this challenge that threatens crop production, adapting proper field management is necessary to stream into sustainable crop production. In this route, using crop growth models is a strong and reliable approach to identifying the best management practices. Nevertheless, before use, crop models must be tuned and calibrated for the specific conditions of the location and field management. This study evaluates the accuracy of the water-driven AquaCrop model in simulating quinoa growth, yield, and soil water content (SWC) under varying planting methods and irrigation levels. The model was run under two conditions: globally recommended default conservative parameters and fine-tuned calibrated parameters. Field experiments were conducted in two growing seasons (2017 and 2018) considering different irrigation levels (I1:100 % of crop water requirement (WR), I2: 75 %WR, and I3: 50 %WR) and planting methods (P1: Basin, P2: on-ridge and P3: in-furrow planting method) in a semi-arid warm area. The results demonstrated AquaCrop's ability to simulate soil water content with good accuracy as normalized roots mean square error (NRMSE) and Willmott index of agreement (d) values were 12.2 % and 0.71 in the calibration step and 13.1 % and 0.75 in the validation step, respectively. AquaCrop could simulate the quinoa yield and biomass with reasonable accuracy at both validation and calibration steps with low NRMSE (9.4–14.1 %) and d values (0.61–0.67). The corresponding values for the validation step were 8–17.6 % and 0.93–0.94. Variations in deficit irrigation treatments introduced additional variability, particularly affecting SWC simulations&lt;strong&gt;.&lt;/strong&gt; In addition, AquaCrop demonstrated reasonable accuracy across both calibration and validation steps, with d values ranging from 0.7 to 0.97 and NRMSE values between 7 % and 25 % for in-season biomass, crop evapotranspiration (ET&lt;sub&gt;c&lt;/sub&gt;), water productivity (WP&lt;sub&gt;c&lt;/sub&gt;), and canopy cover. Canopy cover was underestimated by 12.2 %, especially in the 75 % and 50 % WR treatments, however, RMSE, NRMSE, and d, pooled over all treatments, were 9.18 %, 13.58 %, and 0.90, respectively, in the validation step. Despite slight overestimations in grain yield and biomass in-furrow planting methods, the model provided reliable output, underscoring the impact of planting techniques on water use efficiency. Furthermore, we evaluated the default AquaCrop conservative parameters for simulating quinoa yield and biomass, revealing significant inaccuracies. Biomass was underestimated by 10.7 %, while grain yield was overestimated by 60.8 %, largely due to differences in quinoa cultivars, growth periods, and assumptions about water stress tolerance. Adjusting crop parameters to reflect moderate water stress tolerance improved model accuracy, emphasi","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100913"},"PeriodicalIF":6.3,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747659","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":"Remote sensing and TerraClimate datasets for wheat yield prediction using machine learning","authors":"Alireza Araghi ,&nbsp;Andre Daccache","doi":"10.1016/j.atech.2025.100909","DOIUrl":"10.1016/j.atech.2025.100909","url":null,"abstract":"<div><div>Ensuring food security for the continuously growing global population has become one of the most significant challenges facing humanity today. This challenge is further exacerbated by the impacts of climate change and environmental degradation, much of which is associated with human activities. Yield prediction is vital for addressing food security challenges at local and regional levels. By anticipating crop production, we can better manage food distribution, mitigate the risks of shortages, and support sustainable agricultural practices. Using biophysical crop models to forecast yields is laborious and necessitates various, often unavailable, pedo-climatic, crop-specific, and management parameters. This study leverages satellite imagery and a gridded climate dataset (TerraClima) with machine learning (ML) to predict wheat yields in Mashhad County (Northeast Iran). The analysis spans over 22 years, from 2001 to 2022. Different ML models were developed and evaluated, including multiple linear regression (MLR), artificial neural network (ANN), random forest (RF), and a mean ensemble (ENS) of the outputs of all selected models. Findings showed that with reasonable accuracy, irrigated and rainfed wheat yields could be predicted using the MLR and ENS models up to 2 months before harvest. The Nash-Sutcliffe efficiency (NSE) values are 0.74 and 0.62, while correlation coefficients (r) are 0.93 and 0.80 for irrigated and rainfed wheat, respectively. The global coverage of the input dataset and its easy access make this approach applicable to various crop types and other regions, thus unlocking the limitation related to the lack of on-site data availability for traditional yield prediction models.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100909"},"PeriodicalIF":6.3,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143724108","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":"Multi-scale remote sensing for sustainable citrus farming: Predicting canopy nitrogen content using UAV-satellite data fusion","authors":"Dagan Avioz ,&nbsp;Raphael Linker ,&nbsp;Eran Raveh ,&nbsp;Shahar Baram ,&nbsp;Tarin Paz-Kagan","doi":"10.1016/j.atech.2025.100906","DOIUrl":"10.1016/j.atech.2025.100906","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Accurate monitoring of nitrogen (N) levels, while accounting for spatiotemporal variability is crucial for optimizing fertilization in citrus orchards. Traditional methods, such as frequent leaf and soil sampling followed by laboratory analysis, are costly, labor-intensive, and prone to human error. Remote sensing (RS) technologies, including unmanned aerial vehicles (UAVs) and satellite platforms, offer scalable and precise alternatives for N management. However, integrating these platforms poses challenges due to significant differences in spatial, temporal, and spectral resolution. This study presents a novel approach incorporating multispectral and temporal data from UAVs and Sentinel-2 satellites to estimate canopy N content (CNC) in citrus orchards. This method captures spatiotemporal variability across multiple citrus cultivars, aiming to enhance nitrogen use efficiency (NUE) while reducing environmental impact, ultimately promoting sustainable orchard management practices. The study was conducted in commercial citrus plots in the Hefer Valley, Israel, and spanned two phases. The first phase (May 2019 to April 2022) focused on four plots of the 'Newhall' cultivar, while the second phase expanded to twelve additional plots featuring five different citrus cultivars. The methodology consisted of six key steps: (1) Leaf samples from the study area were collected for laboratory nitrogen (N) analysis. (2) Acquiring and preprocessing bimonthly UAV multispectral images and Sentinel-2 satellite images to ensure data quality and consistency. (3) Segmenting individual trees using UAV imagery and extracting structural features through Structure-from-Motion (SfM) photogrammetry. (4) Processing images and extracting spectral and structural features relevant to N estimation. (5) Developing Random Forest (RF) models to estimate CNC using UAV-derived vegetation indices (VIs) and SfM data and combining these with Sentinel-2 VIs to generate canopy-scale CNC heatmaps. (6) Analyzing the relationship between CNC and yield to understand nitrogen dynamics and their impact on productivity. The integrated RF model, which combined UAV-VIs, Sentinel-2 VIs, and SfM-derived structural data, achieved superior performance (R² = 0.80, RMSE = 0.17 kg/m²) compared to models relying solely on UAV-VIs (R² = 0.68, RMSE = 0.23 kg/m²) or Sentinel-2 VIs (R² = 0.48, RMSE = 0.30 kg/m²). Additionally, CNC expressed as mass per tree demonstrated a strong positive correlation with yield (R² = 0.66), highlighting the relationship between nitrogen dynamics and orchard productivity. These results underscore the robustness of the integrated model and the clear advantage of multi-platform data fusion over single-source approaches. The study provides compelling evidence for the potential of combining UAV and Sentinel-2 data to improve CNC estimation and its correlation with yield in citrus orchards. The findings contribute to advancements in precision agriculture by offering a sca","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100906"},"PeriodicalIF":6.3,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143724109","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":"Visual navigation and crop mapping of a phenotyping robot MARS-PhenoBot in simulation","authors":"Zhengkun Li ,&nbsp;Rui Xu ,&nbsp;Changying Li ,&nbsp;Longsheng Fu","doi":"10.1016/j.atech.2025.100910","DOIUrl":"10.1016/j.atech.2025.100910","url":null,"abstract":"<div><div>Cultivating high-yield and high-quality crops is important for addressing the growing demand for food and fiber from an increasing population. In selective breeding programs, autonomous robotic systems have shown great potential to replace manual phenotypic trait measurements which are time-consuming and labor-intensive. In this paper, we presented a Robot Operating System (ROS)-based phenotyping robot, MARS (Modular Agricultural Robotic System)-PhenoBot, and demonstrated its visual navigation and field mapping capacities in the Gazebo simulation environment. MARS-PhenoBot was a solar-powered modular robotic platform with a four-wheel steering and four-wheel driving configuration. We developed a navigation strategy that fuses multiple cameras to guide the robot to follow crop rows and transition between them, enabling visual navigation across the entire field without relying on global navigation satellite system (GNSS) signals. Three row-detection algorithms, including thresholding-based, detection-based, and segmentation-based methods, were compared and evaluated in simulated crop fields with discontinuous and continuous crop rows, as well as with and without the presence of weeds. The results demonstrated that the segmentation-based method achieved the lowest average cross-track errors of 2.5 cm for discontinuous scenarios and 0.8 cm for continuous scenarios in row detection. Additionally, a field mapping workflow based on RTAB-MAP (Real-Time Appearance-Based Mapping) and V-SLAM (Visual Simultaneous Localization and Mapping) was developed. The workflow produced the 2D maps identifying crop and weed locations, as well as 3D models represented as point clouds for crop shapes and structures. Using this mapping workflow, the average crop localization error was measured at 6.4 cm, primarily caused by the visual odometry drift. The generated point clouds of crops could support further phenotyping analyses, such as crop height/diameter measurements and leaf counting. The methodology developed in this study could be transferred to real-world robots that are capable of automated robotic phenotyping for in-field crops, providing an effective tool for accelerating selective breeding programs.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100910"},"PeriodicalIF":6.3,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714669","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 modified spectral remote sensing index to map plastic greenhouses in fragmented terrains","authors":"Shanshan Chen ,&nbsp;Yijia Chen ,&nbsp;Song Gao ,&nbsp;Chun Li ,&nbsp;Ninglv Li ,&nbsp;Liding Chen","doi":"10.1016/j.atech.2025.100904","DOIUrl":"10.1016/j.atech.2025.100904","url":null,"abstract":"<div><div>Plastic greenhouse (PG), as a new type of modern agricultural measure, has been used widely due to its significant benefits for agricultural production. However, it also raises concerns about its potential environmental impact. Monitoring of PG is necessary for the agricultural sustainability. However, extracting PGs in fragmented terrains based on remote sensing images is difficult due to the variety of types of PGs and high environmental heterogeneity. In this study, a modified plastic greenhouse index (MPGI) was proposed to monitor PG based on the differences on spectral signatures using Landsat-8 Operational Land Imager. Four study sites, including Weifang (China), Nantong (China), Kunming (China), and Dalat (Vietnam), were selected for index applications. And the effectiveness and robustness of the MPGI were examined by comparing with the exiting PG indices. The results indicated that MPGI improved extraction accuracy in fragmented terrains. The F1 scores for MPGI classification accuracy ranged from 85.7 % to 87.9 %, while other PG indices demonstrated between 67.0 % and 86.4 %. The MPGI demonstrated its capability across various season and datasets, highlighting it has the potential for the PGs mapping in heterogeneous regions. This index is capable of effecting a transformation of greenhouses from \"vague agricultural facilities\" into computable and manageable spatial decision-making units. In establishing an underlying data foundation for smart agriculture development, it serves to reduce the workload of manual labor.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100904"},"PeriodicalIF":6.3,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714558","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":"Design of a low-cost gas accumulation chamber for general purpose environmental monitoring","authors":"Domenico Longo ,&nbsp;Serena Guarrera ,&nbsp;Delia Ventura ,&nbsp;Emanuele Cerruto ,&nbsp;Salvatore Roberto Maugeri ,&nbsp;Gaetano Giudice","doi":"10.1016/j.atech.2025.100907","DOIUrl":"10.1016/j.atech.2025.100907","url":null,"abstract":"<div><div>The need for accurate measurement of CO₂ emissions from surfaces arises from various fields, particularly in precision agriculture, irrigation water management, wastewater management, volcanology, geothermal exploration, environmental and climate monitoring. This study introduces a novel, cost-effective closed dynamic accumulation chamber system designed to measure CO₂ fluxes from soil and water surfaces. A short review of existing measurements techniques is provided, alongside a detailed explanation of key algorithms used for processing field data. The proposed system collects raw CO₂ concentration data via an internal data logger. A custom-developed software suite enables real-time first-approximation flux calculation through a user-friendly Javascript web application compatible with smartphones with any type of operating system and web browser. A freely available Matlab® tool allows for post-processing data analysis for a more accurate flux calculation. After calibration against the commercial PP Systems EGM-5, assumed as a reference, some case studies in agriculture, wastewater treatment and volcanic environments demonstrate the instrument's versatility, showcasing its potential for advance in agricultural field and environmental sustainability.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100907"},"PeriodicalIF":6.3,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143724110","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":"Improving soil moisture prediction using Gaussian process regression","authors":"Xiaomo Zhang,&nbsp;Xin Sun,&nbsp;Zhulu Lin","doi":"10.1016/j.atech.2025.100905","DOIUrl":"10.1016/j.atech.2025.100905","url":null,"abstract":"<div><div>Soil moisture plays a vital role in agriculture and hydrology, influencing key processes like plant growth and evaporation. Recent advancements in soil moisture monitoring have improved our ability to measure it at different scales, but challenges persist at intermediate scales that are crucial for precision agriculture. To address this research gap, innovative methods like machine learning (ML) are being explored to improve prediction accuracy, overcoming the limitations of traditional models. By leveraging an extensive dataset that spans multiple sites and seasons, we aim to improve predictions for both surface and root zone soil moisture. In this study, machine learning models including multilinear regression (MLR), support vector machine (SVM), and Gaussian process regression (GPR), were developed and compared for soil moisture predictions at different depths at 29 weather stations in the Red River Valley using features such as time, locations, meteorological data, soil physical properties, and remote sensing data. Our research showed that GPR with automatic relevant determination kernels had the best performance with <span><math><msup><mrow><mi>R</mi></mrow><mn>2</mn></msup></math></span> values greater than 0.95 at almost all depths when including all features. GPR (<span><math><msup><mrow><mi>R</mi></mrow><mn>2</mn></msup></math></span>=0.95–0.99, RMSE=0.0045–0.0224, MAE=0.0012–0.0139) outperformed MLR (<span><math><msup><mrow><mi>R</mi></mrow><mn>2</mn></msup></math></span>=0.69–0.93, RMSE=0.0328–0.0555, MAE=0.0197–0.0427) and SVM (<span><math><msup><mrow><mi>R</mi></mrow><mn>2</mn></msup></math></span>=0.49–0.85, RMSE=0.0648–0.0747, MAE=0.0442–0.0566) for soil moisture prediction. All models performed better when predicting moisture in subsoils (20–100 cm) than in topsoil (0–10 cm). Our research highlights the effectiveness of GPR as a powerful ML tool that enhances soil moisture management precision, ultimately contributing to more effective and smart agricultural practices.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100905"},"PeriodicalIF":6.3,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714556","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":"Finite element optimization of a flexible fin-ray-based soft robotic gripper for scalable fruit harvesting and manipulation","authors":"Finny Varghese ,&nbsp;Fernando Auat Cheein ,&nbsp;Maria Koskinopoulou","doi":"10.1016/j.atech.2025.100899","DOIUrl":"10.1016/j.atech.2025.100899","url":null,"abstract":"<div><div>On the path to achieving fully autonomous farming, the use of grasping devices for fruit picking and handling remains an open challenge. Current solutions are designed for specific fruits and robot manipulators, often without considering the intrinsic interaction between the gripper's fingers and the fruit. This work explores the use of fin-ray-based flexible grippers, which mimic human fruit-picking movements, for harvesting and pick-and-place operations involving medium-sized fruits. Optimal gripper characteristics were determined through a Finite Element Analysis methodology. To achieve the harvesting objective, the grippers were integrated into a vision-based system and a robotic manipulator, with testing conducted under laboratory conditions. The harvesting study focused on apples, while the manipulation task was tested with apples, oranges, and lemons. The findings indicate that while all grippers demonstrated a suitable performance, one particular design emerged as the most effective, meeting all criteria and outperforming the others in experiments and performance metrics.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100899"},"PeriodicalIF":6.3,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697317","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}