Smart agricultural technology最新文献

{"title":"Harnessing machine learning for grain mycotoxin detection","authors":"Rakiba Rayhana ,&nbsp;Jatinder S. Sangha ,&nbsp;Yuefeng Ruan ,&nbsp;Zheng Liu","doi":"10.1016/j.atech.2025.100923","DOIUrl":"10.1016/j.atech.2025.100923","url":null,"abstract":"<div><div>Detecting mycotoxins such as deoxynivalenol, aflatoxins, and zearalenone in grains is crucial for ensuring crop safety and maintaining consumer health, both for humans and animals. These toxins pose serious health risks, affect the marketability of grains in international markets, and influence their economic value. Hence, this paper reviews the use of machine learning (ML) in detecting and managing grain mycotoxins to transform grain safety measures. The review will cover the common mycotoxins in grains, their adverse effects, and techniques for detecting mycotoxin data. It describes the latest ML models for detecting or predicting these toxins. The paper evaluates the effectiveness of these ML techniques, identifies research gaps, and suggests potential solutions. Overall, this review establishes a comprehensive baseline for future research on grain mycotoxin detection, assessing the extent to which various ML methodologies have been explored. This paper aims to create a foundational understanding for readers about the state-of-the-art techniques in ML. This area will further advance readers' knowledge of detecting and managing mycotoxins in grains.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100923"},"PeriodicalIF":6.3,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143758950","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":"Leveraging ultrasonic-derived phenotypes and estimated breeding value to improve abdominal fat weight prediction in chickens throughout the egg laying period","authors":"Penghao Li ,&nbsp;Zhengda Li ,&nbsp;Fan Ying ,&nbsp;Dan Zhu ,&nbsp;Dawei Liu ,&nbsp;Xianyi Song ,&nbsp;Jie Wen ,&nbsp;Guiping Zhao ,&nbsp;Bingxing An","doi":"10.1016/j.atech.2025.100912","DOIUrl":"10.1016/j.atech.2025.100912","url":null,"abstract":"<div><div>Abdominal fat (AF) in hens impacts egg production and may reflect poor feed efficiency, meaning that dynamic monitor of AF changes facilitate to optimize feeding management and production efficiency. This study estimated hens’ AF weight among whole laying period through fitting vivo phenotypes by ten machine learning techniques (including generalized linear model, GLM; multiple linear regression, MLR; ridge regression, RR; LASSO; elastic net, EN; k nearest neighbours, KNN; SVM biased linear and Gaussian kernel; Random forests, RF and XGBoost). Consistent protocols were applied to all phenotypic measurements to minimize batch effects across five laying stages (8 traits: abdominal fat thickness (AFT), keel length (KL), breast width (BW), pubic bone width (PBW), body slope length (BSL), live weight (LW), keel-pubic length (KPL), and abdominal fat weight (AFW). The stepwise backwards variables selection was conducted to rule out the possible bias of multicollinearity between independent variables and AFW. While, AFT measured by ultrasound improved the predictive ability of all the models (R² of KNN showed highest increase of 12.35 %). Moreover, we also evaluated the contribution of AF estimate breeding values (EBVs) to fitting model performance. When incorporating EBVs of AFW as extra independent variables, all methods’ predictive ability had increased by an average of 15.71 %, especially KNN (27.2 %). For muliple laying periods, the EN model showed the best performance at 26 and 35 weeks, with an average R² of 0.928, MAE of 6.709, RMSE of 9.862, and MAPE of 9.660. The KNN model performed best at 28, 31, and 43 weeks, with an average R² of 0.918, MAE of 9.273, RMSE of 12.348, and MAPE of 17.240. These findings underscore the feasibility of accurately prediction of hens'AF through fitting appropriate algorithms for different laying periods, that supporting delicacy feeding management in farm.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100912"},"PeriodicalIF":6.3,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747495","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":"Early-stage detection of maize seed germination based on RGB image and machine vision","authors":"Xiaohang Liu ,&nbsp;Zhao Zhang ,&nbsp;Yunxia Li ,&nbsp;C. Igathinathane ,&nbsp;Jiangfan Yu ,&nbsp;Zhaoyu Rui ,&nbsp;Afshin Azizi ,&nbsp;Xiqing Wang ,&nbsp;Alireza Pourreza ,&nbsp;Man Zhang","doi":"10.1016/j.atech.2025.100927","DOIUrl":"10.1016/j.atech.2025.100927","url":null,"abstract":"<div><div>Maize (corn) seed germination rate is an essential piece of information to reflect seed quality and its marketability. The widely used seed germination test is manual, inefficient, time-consuming (required 7 days), and error-prone. This study utilizes machine vision combined with characterization of sand deformation and crack formation during germination for early and automatic germination detection. Collected color (RGB) images of germination trays planted with maize seeds sown in preset patterns were preprocessed as regions of interest (RoI) for each seed for analysis. For each RoI, direct early germination prediction methods, namely, stripe band, boundary, and color were developed using different image processing operations. A total of 36 images (4 trays for 9 consecutive days) were used to test the three direct methods and their different combinations. Experimental results showed that the performance of stripe band + boundary + color combination was superior to each direct method, and the average precision, recall, and F1 value of germination detection were 73.5 %, 87.5 %, and 79.2 %, respectively. It was also found that the seed germination rate detected on the 4th day (92.4 %) of the germination test could determine whether it met the sowing requirements, significantly shortening (by 3 days) the standard germination procedure time. This study demonstrates that the stripe band + boundary + color method can be used as an efficient approach for automated germination rate detection of maize and other crop seeds.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100927"},"PeriodicalIF":6.3,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747496","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":"Optimizing image-based soil organic matter prediction: Effects of illumination type and intensity","authors":"Mojtaba Naeimi ,&nbsp;Maja Krzic ,&nbsp;Stacey Scott ,&nbsp;Prasad Daggupati ,&nbsp;Asim Biswas","doi":"10.1016/j.atech.2025.100922","DOIUrl":"10.1016/j.atech.2025.100922","url":null,"abstract":"<div><div>Image-based soil organic matter (SOM) prediction has emerged as a promising approach for rapid soil assessment, but illumination variations significantly impact measurement reliability. This study provides a comprehensive analysis of illumination effects on soil color measurements and develops an optimized framework for image acquisition across different devices and lighting conditions. Soil samples (<em>n</em> = 500) collected from southern Ontario, Canada were imaged under six illumination levels (100–900 lux) using both natural daylight (&gt; 5000 k), representing cool lighting condition, and warm lighting (2700–3000 K) to simulate typical indoor condition. Images were captured using a smartphone (iPhone 14 Pro) and digital camera (Sony α7 III), with systematic evaluation of color feature stability and prediction accuracy. Mixed-model analysis revealed device-specific optimal illumination ranges for effective image feature extraction, with smartphones performing best between 300–500 lux (RMSE=0.232, CCC=0.892) and digital cameras maintaining stability up to 600 lux (RMSE=0.173, CCC=0.931). Color features from opponent-based color spaces (CIE La*b* and CIE Lu*v*) demonstrated superior stability and consistency compared to those from additive color spaces (RGB and HSV), which encode color through separate channels rather that perceptual opponent relationship. Warm lighting provided more consistent results at lower illumination levels, while natural lighting showed greater stability at higher intensities. Random Forest machine learning models achieved optimal performance under moderate illumination levels (400–500 lux) for both devices. The findings establish quantitative relationships between illumination parameters and prediction accuracy, advancing the development of reliable image-based soil analysis methods by addressing critical gaps in illumination control and feature stability.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100922"},"PeriodicalIF":6.3,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143758949","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":"Monitoring leaf wetness dynamics in corn and soybean fields using an IoT (Internet of Things)-based monitoring system","authors":"Caden R. Wade ,&nbsp;Jill C. Check ,&nbsp;Martin I. Chilvers ,&nbsp;Younsuk Dong","doi":"10.1016/j.atech.2025.100919","DOIUrl":"10.1016/j.atech.2025.100919","url":null,"abstract":"<div><div>Food security is at an increased threat as plant diseases caused by pathogens continue to increase their global range, overcome plant tolerance, or develop resistance to fungicides. Leaf wetness is a critical component of disease development through the facilitation of microbial growth. The use of precision agriculture and IoT (Internet of Things) sensors can improve disease modeling and disease management by tracking a leaf's wetness duration. Weather variables including humidity, solar radiation, and precipitation can alter leaf wetness duration and vary among crop heights and canopy densities. The placement of humidity and leaf wetness sensors is in question based on canopy density which can alter these parameters. By using IoT in-field sensors at differing placements, the threshold that humidity must reach to initiate leaf wetness and their relation to a leaf wetness sensor were tracked. IoT sensors placed low in the corn canopy consistently showed lower wetness durations compared to a higher positioning, while the between or in-row placement in soybeans had no observable difference. High relative humidity and low temperature periods induced leaf wetness more often than other environmental factors. A humidity threshold of 85 % for all heights within the corn canopy and between or within soybean rows demonstrated strong correlations to sensor-observed wetness. Off-site weather stations underreported wetness events by 10 % for low-canopy corn, 17 % for upper-canopy corn, and 13 % for soybean. IoT in-field sensors accurately reported leaf wetness and weather factors, highlighting the potential of these technologies to provide accurate and easily culminated wetness information.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100919"},"PeriodicalIF":6.3,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747494","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":"Stakeholders' perspective on smart farming robotic solutions","authors":"George Papadopoulos ,&nbsp;Maria-Zoi Papantonatou ,&nbsp;Havva Uyar ,&nbsp;Konstantinos Nychas ,&nbsp;Vasilis Psiroukis ,&nbsp;Aikaterini Kasimati ,&nbsp;Ard Nieuwenhuizen ,&nbsp;Frits K. Van Evert ,&nbsp;Spyros Fountas","doi":"10.1016/j.atech.2025.100916","DOIUrl":"10.1016/j.atech.2025.100916","url":null,"abstract":"<div><div>This study examines agricultural stakeholders' perceptions of robotic farming technologies through feedback collection and analysis in the context of Robs4Crops project, employing a mixed-method approach that combines quantitative and qualitative questionnaires. Feedback was gathered from 104 participants, including farmers, researchers, tech providers, policy makers, and others in demonstrations across France, Greece, Spain, and The Netherlands, along with 54 agricultural students engaged in demonstrations at the Agricultural University of Athens. The findings highlight the potential of robotic solutions in agriculture. Agricultural stakeholders highlighted key benefits such as labour reduction, cost efficiency, and improved productivity, alongside challenges like high initial costs, technical skill gaps, and scepticism, particularly among older generations. Practical, hands-on demonstrations emerged as a pivotal factor in changing perceptions and fostering acceptance, with stakeholders advocating for user-friendly tools, financial incentives, and collaborative farming models. Agricultural students, as future practitioners, showed limited prior familiarity with technologies like Digital Twins and Farming Controller for remote monitoring of robots but demonstrated strong engagement and learning during demonstrations. Students valued automation, resource efficiency, and reduced chemical use as key benefits, while also stressed the importance of interactive learning experiences such as task simulations and real-world applications to bridge knowledge gaps effectively. The findings underscore the importance of tailored strategies to engage both current and future stakeholders. Addressing barriers through targeted education, financial support, and inclusive outreach can facilitate the integration of robotic solutions into sustainable agriculture. By fostering collaboration among farmers, students, researchers, and policymakers, this study outlines a roadmap for leveraging robotic innovations to advance agricultural efficiency, sustainability, and resilience.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100916"},"PeriodicalIF":6.3,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747498","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":"Winter wheat plant density determination: Robust predictions across varied agronomic conditions using multiscale RGB imaging","authors":"Jara Jauregui-Besó ,&nbsp;Adrian Gracia-Romero ,&nbsp;Constanza S. Carrera ,&nbsp;Marta da Silva Lopes ,&nbsp;José Luis Araus ,&nbsp;Shawn Carlisle Kefauver","doi":"10.1016/j.atech.2025.100921","DOIUrl":"10.1016/j.atech.2025.100921","url":null,"abstract":"<div><div>Cereal plant density is a crucial agronomic factor affecting resource management and yield. This study automated wheat density estimation using multiscale imaging from ground and Unmanned Aerial Vehicles (UAV) at 15, 30, and 50m Conducted over two agronomic seasons (2022 and 2023) with different water profiles, it analyzed three wheat genotypes (cv. Bologna, Hondia, and Marcopolo) sown at five densities ranging from 35 to 560 seeds m^-2. Images collected through RGB sensors across Haun's developmental stages 2.6 – 12.2 provided data for calculating 15 Vegetation Indexes (VIs), which, along with their Principal Components (PCs), were used as inputs for Ridge and Principal Component Regression (PCR) models. Training was conducted on the 2022 datasets using 4-fold, 10-repeated cross-validation to determine the most predictive growth stages, with Haun stages 5.3 to 7.3 yielding the best results, irrespective of resolution. Testing on 2023 datasets showed that Ridge models consistently outperformed PCR, especially for medium to high-density ranges (140–560 seeds m^-2), though they underperformed at lower densities, leading to their exclusion from the testing data. The top-performing Ridge model, trained on Haun stages 7.1–7.3 at 50 m (1.18 cm pixel-1), achieved Mean Absolute Percentage Error (MAPE) 17.91% – 28.54% (0.9 – 0.68 R²) values across various test sets, with stable performance throughout resolutions and stages (4.4 – 4.8). These findings show robust prediction capabilities across a broader developmental range and from the lowest resolution recorded, especially when vegetation coverage is abundant. The study highlights the practicality of high-throughput RGB imaging for scalable, flexible and affordable plant density estimation.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100921"},"PeriodicalIF":6.3,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747500","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 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}