Smart agricultural technology最新文献

{"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}

{"title":"Prediction of the bed expansion and pressure drop in microirrigation media filter backwashing using artificial neural networks and comparison with other machine learning techniques","authors":"Paulino José García-Nieto ,&nbsp;Esperanza García-Gonzalo ,&nbsp;Jonathan Graciano-Uribe ,&nbsp;Gerard Arbat ,&nbsp;Miquel Duran-Ros ,&nbsp;Toni Pujol ,&nbsp;Jaume Puig-Bargués","doi":"10.1016/j.atech.2025.100900","DOIUrl":"10.1016/j.atech.2025.100900","url":null,"abstract":"<div><div>The filtration capacity of media filters, which are widely used in drip irrigation systems to prevent emitter clogging, must be periodically restored by backwashing, which fluidizes the media bed and removes those trapped particles. Bed expansion (BE) and pressure drop (PD) are the key parameters for assessing the hydraulic performance of backwashing, but the available equations and models frequently fall short of their prediction. An experiment with three medium types, four filter underdrain designs, two bed heights and different backwashing superficial velocities as input variables was conducted to measure both BE and PD. A dataset of 705 backwashing runs was obtained and with 80 % of data for training and 20 % for testing, a machine learning-based model that uses Artificial Neural Networks (ANN) to predict both BE and PD was developed and compared with the Ridge, Elastic-net, and Lasso regression models. With coefficients of determination of 0.9932 and 0.9988 for BE and PD, respectively, the results demonstrated that the ANN model not only ranked the importance of the input variables and showed strong agreement with experimental data but also attained superior predictive accuracy regarding the Lasso, Elastic-net, and Ridge models. This study presents a novel and optimized approach for predicting bed expansion and pressure drop, enhancing the reliability of media filter backwashing performance assessments in smart irrigation systems.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100900"},"PeriodicalIF":6.3,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697312","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":"Automated detection and segmentation of baby kale crowns using grounding DINO and SAM for data-scarce agricultural applications","authors":"Gianmarco Goycochea Casas ,&nbsp;Zool Hilmi Ismail ,&nbsp;Mohd Ibrahim Shapiai ,&nbsp;Ettikan Kandasamy Karuppiah","doi":"10.1016/j.atech.2025.100903","DOIUrl":"10.1016/j.atech.2025.100903","url":null,"abstract":"<div><div>This research addresses the significant challenge of data scarcity in agriculture by introducing an automatic pipeline for plant detection and segmentation. The primary objective was to detect and segment the crown area of baby kale (Brassica oleracea var. sabellica) during its early growth stages without relying on extensive data training or manual annotations, providing an alternative for scenarios with insufficient data. A dataset comprising aerial images of baby kale plants was gathered over a three-week period in a controlled environment. The model was processed using the NVIDIA GeForce RTX 4060 GPU. Grounding DINO was employed for plant detection based on textual prompts, and bounding boxes were generated to locate the central plant in each image. The detected regions were then processed using SAM to extract precise segmentation masks of the plant crown. The segmentation results were validated by comparing the automated method with manually annotated ground truth using statistical metrics, including Spearman's correlation, RMSE%, and the Wilcoxon signed-rank test. The automated approach demonstrated a strong correlation (ρ = 0.956) with manual annotations across all weeks, with RMSE% decreasing as plants matured. While Week 1 exhibited lower agreement (ρ = 0.581, RMSE% = 56.246 %) due to segmentation challenges at early growth stages, performance improved significantly in Week 2 (ρ = 0.945, RMSE% = 24.834 %) and Week 3 (ρ = 0.996, RMSE% = 11.733 %). The statistical validation confirmed a significant difference between manual and automated annotations; however, the automated method consistently captured the growth trend of the plants. In conclusion, while the pipeline offers a promising approach for plant detection and segmentation in data-scarce environments, its limitations, especially in early growth stages, should be considered. The study contributes by demonstrating a practical approach to overcoming data scarcity in agriculture using multimodal AI models capable of zero-shot and few-shot learning. This approach paves the way for more adaptive AI-driven agricultural monitoring systems, addressing data scarcity challenges in precision farming.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100903"},"PeriodicalIF":6.3,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143685754","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":"Impact of artificial light on photosynthesis, evapotranspiration, and plant growth in plant factories: Mathematical modeling for balancing energy consumption and crop productivity","authors":"Mohammad Hossein Amirshekari,&nbsp;Mohammad Fakhroleslam","doi":"10.1016/j.atech.2025.100901","DOIUrl":"10.1016/j.atech.2025.100901","url":null,"abstract":"<div><div>The impact of artificial light conditions on plants is multifaceted and depends on various influencing factors. Toward optimized energy consumption, understanding the specific requirements of the plant species and tailoring artificial lighting to that, may lead to optimized growth, evapotranspiration (ET), and photosynthetic processes in controlled environments such as indoor farming or plant factories. In this study, an integrated mathematical model has been established to describe relationships between lighting conditions and plants’ growth, ET, and photosynthesis. The developed model also includes the calculation of lamps energy loss, which affects the temperature of the plant factory, and an empirical model for leaf area index (LAI). Additionally, an empirical relationship between plant weight and LAI was developed using experimental data for lettuce plants (<em>Lactuca sativa</em> L.). Key parameters related to photosynthesis and ET for lettuce plants were also accurately adjusted, and the validation results were discussed. Based on the developed model, the effects of light intensity and photoperiod on photosynthesis, LAI, plant weight, and ET were analyzed. Results demonstrate that the effect of the photoperiod on photosynthesis and ET is significantly greater than its effect on plant weight and LAI. However, the impact of light intensity on photosynthesis, ET, plant weight, and LAI is approximately the same. The proposed integrated model can be used to simulate microclimate conditions, optimize resource use, and improve the control of plant factories.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100901"},"PeriodicalIF":6.3,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143685755","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}