Smart agricultural technology最新文献

{"title":"Structural optimization and performance evaluation of a sugarcane leaf mulching machine","authors":"Weihua Huang ,&nbsp;Shuo Wang ,&nbsp;Chang Ge ,&nbsp;Lijiao Wei ,&nbsp;Dongjie Du ,&nbsp;Zhaojun Niu ,&nbsp;Ming Li ,&nbsp;Zhenhui Zheng","doi":"10.1016/j.atech.2025.101116","DOIUrl":"10.1016/j.atech.2025.101116","url":null,"abstract":"<div><div>Existing sugarcane leaf mulching machines struggle to process high-fiber, tough sugarcane leaves, leading to incomplete mulching and uneven residue distribution. These limitations hinder subsequent farming operations and increase energy consumption. To address these challenges, this study presents a structural optimization and performance analysis of the 1GYF-150 sugarcane leaf mulching machine, introducing an enhanced, high-efficiency mulching mechanism. The operational principles of the machine were analyzed, and the effects of different blade types, including straight and hammer-shaped blades, on mulching performance were evaluated. Key parameters—such as blade structure, rotational speed, and arrangement—were optimized to improve mulching quality and pick-up efficiency. Further, a balance analysis of the cutter roller was conducted, incorporating MATLAB optimization algorithms and a fuzzy reliability function to enhance the roller’s structural integrity and reduce weight. Field tests under typical post-harvest conditions (leaf moisture content of 31.8 %, representing the average humidity of sugarcane leaves in tropical regions) demonstrated that the optimized machine achieved a pick-up rate of 98.4 % and a mulching rate of 94.4 % (≤20 cm), reflecting improvements of 0.8 % and 7.1 % over the previous design, respectively. This study provides a valuable reference for advancing sugarcane leaf mulching machine performance and offers insights into more effective utilization of sugarcane leaf resources.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"12 ","pages":"Article 101116"},"PeriodicalIF":6.3,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144364441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing the estimation of cotton leaf SPAD and LAI values via UAV multispectral imagery and LASSO regression","authors":"Chunli Wang ,&nbsp;Xiao Zhang ,&nbsp;Nannan Zhang ,&nbsp;Huaying Guo ,&nbsp;Hongxin Wu ,&nbsp;Xuanzhang Wang","doi":"10.1016/j.atech.2025.101098","DOIUrl":"10.1016/j.atech.2025.101098","url":null,"abstract":"<div><div>Cotton (Gossypium spp.) is a vital economic crop both globally and particularly in Xinjiang, China, where its growth status is closely linked to chlorophyll content and leaf area index (LAI). Chlorophyll content is commonly measured using the soil plant analysis development (SPAD) value. This study employed multispectral remote sensing data collected by a DJI Mavic 3 M unmanned aerial vehicle (UAV) to investigate the spectral responses of canopy SPAD and LAI in cotton fields affected by Verticillium wilt in southern Xinjiang. SPAD was strongly negatively correlated with the red band (r = –0.784) and positively correlated with the red-edge (REG) band (r = 0.498), while LAI showed the strongest correlation with the near-infrared (NIR) band (r = 0.673) and a moderate correlation with the REG band (r = 0.435). Among various vegetation indices (VIs), the photochemical reflectance ratio (PPR) exhibited the highest correlation with SPAD (r = 0.84), and the excess green (EXG) index showed the strongest correlation with LAI (r = 0.92). Inversion accuracy was highest during the boll stage. The least squares method (LSM) achieved coefficient of determination (<span><math><msup><mrow><mi>R</mi></mrow><mn>2</mn></msup></math></span>) values of 0.58 for SPAD and 0.57 for LAI, while combining VIs and texture features through least absolute shrinkage and selection operator (LASSO) regression improved accuracy to 0.711 and 0.751, respectively. Comparative modeling using LSM, grey wolf optimizer–support vector machine (GWO-SVM), and ant colony optimization–random forest (ACO-RF) revealed that ACO-RF consistently outperformed the other models, particularly in capturing nonlinear relationships and multi-feature interactions. The ACO-RF model achieved <span><math><msup><mrow><mi>R</mi></mrow><mn>2</mn></msup></math></span> values of 0.898 (root mean square error, RMSE = 1.523) for SPAD and 0.893 (RMSE = 3.308) for LAI. These findings demonstrate that integrating spectral and textural features with optimized machine learning models can significantly enhance the accuracy, scalability, and cost-effectiveness of Verticillium wilt monitoring in cotton, thereby supporting early disease detection and precision agricultural management.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"12 ","pages":"Article 101098"},"PeriodicalIF":6.3,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144320802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cattle weight estimation using 2D side-view images and estimated depth-based 3D modeling","authors":"Guilherme Botazzo Rozendo,&nbsp;Maichol Dadi,&nbsp;Annalisa Franco,&nbsp;Alessandra Lumini","doi":"10.1016/j.atech.2025.101099","DOIUrl":"10.1016/j.atech.2025.101099","url":null,"abstract":"<div><div>Weighing cattle is a vital practice in livestock farming, as it provides essential data for effective herd management. Recent advancements in computer vision and machine learning have led to the development of non-invasive techniques that estimate cattle weight using images. These methods offer a way to gauge weight without needing physical scales, which helps reduce stress on the animals and minimizes labor-intensive processes. However, existing techniques often rely on dorsal (top-down) views of cattle, which can be difficult to capture in practice. In this study, we propose a method for estimating cattle weight using only side-view images, which are more accessible and easier to obtain. We utilized public datasets to extract a comprehensive set of features, including body measurements and shape descriptors from the images. We also employed advanced techniques such as cattle pose estimation, segmentation, monocular depth estimation, and point cloud generation to derive volume and area features. Our goal was to extract as much relevant information as possible from the images to accurately predict the cattle's weight. We used both linear and non-linear regression models to forecast weight based on the extracted features. Our results indicate that the proposed method can accurately predict cattle weight from side-view images, providing valuable insights for livestock management and monitoring.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"12 ","pages":"Article 101099"},"PeriodicalIF":6.3,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144306457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dairy GPT: Empowering dairy farmers to interact with numerical databases through natural language conversations","authors":"Danillo Gontijo ,&nbsp;Douglas Rolins Santana ,&nbsp;Gustavo de Assis Costa ,&nbsp;Victor E. Cabrera ,&nbsp;Eduardo Noronha de Andrade Freitas","doi":"10.1016/j.atech.2025.101097","DOIUrl":"10.1016/j.atech.2025.101097","url":null,"abstract":"<div><div>Large language models (LLMs), like GPT-4, have revolutionized artificial intelligence by enabling intuitive text and voice interactions, simplifying complex tasks, and democratizing access to AI-driven tools. However, one of their primary limitations lies in their ability to effectively handle interactions with strictly numerical data. This limitation has led to innovative solutions such as Retrieval Augmented Generation (RAG) and Natural Language to SQL (NL2SQL), which enhance their applicability in data-intensive domains. This study investigated the possibility and feasibility of using large language models (LLMs) to allow natural language interactions of dairy farmers with purely numerical databases. To support the proposed study, we constructed a dataset consisting of 25,925 daily milk production records from 85 cows, derived from real data collected at the University of Wisconsin-Madison Agricultural Research Station. Three analyses pipelines were proposed to assess the effectiveness of LLMs handling of numerical databases: Prompt Engineering (zero-shot), Retrieval-Augmented Generation (RAG), and NL2SQL with Decomposition, evaluated using a set of quantitative (5) and qualitative (5) questions. Based on these 10 questions, the NL2SQL with Decomposition achieved 80% accuracy for quantitative questions and the Zero-shot achieved 100% for qualitative questions. These results demonstrate the potential of LLMs to enhance data utilization in dairy farming. Future work will focus on refining the proposed methods and expanding their applicability to other livestock purposes.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"12 ","pages":"Article 101097"},"PeriodicalIF":6.3,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144313167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MIF-YOLO: An Enhanced YOLO with Multi-Source Image Fusion for Autonomous Dead Chicken Detection","authors":"Jiapan Li ,&nbsp;Yan Zhang ,&nbsp;Yong Zhang ,&nbsp;Hongwei Shi ,&nbsp;Xianfang Song ,&nbsp;Chao Peng","doi":"10.1016/j.atech.2025.101104","DOIUrl":"10.1016/j.atech.2025.101104","url":null,"abstract":"<div><div>Addressing the paucity of automated systems for the detection of dead poultry within large-scale agricultural settings, characterized by the onerous and time-consuming manual inspection processes, this study introduces an enhanced YOLO algorithm with multi-source image fusion (MIF-YOLO) for the autonomous identification of dead chicken. The proposed approach commences with the application of progressive illumination-ware fusion (PIA Fusion) to amalgamate thermal infrared and visible-light imagery, thereby accentuating the salient features indicative of dead chickens and counteracting the impact of non-uniform illumination. To address the challenge of feature extraction under conditions of significant occlusion, the model incorporates the Rep-DCNv3 module, which augments the backbone network's capacity to discern subtle characteristics of dead chickens. Additionally, an exponential moving average (EMA) attention mechanism is strategically embedded within the YOLO algorithm architecture's neck region to bolster the model's ability to discern targets under low-light scenarios, enhancing both its accuracy rates and adaptability. The loss function of the model is refined through the implementation of Modified Partial Distance-IoU (MPDIoU), facilitating a more nuanced evaluation of the overlap of objects. Validated against a dataset comprising caged white-feathered chickens procured from a farm in Suqian, Jiangsu Province, the empirical findings indicate that the model attains a precision of 99.2% and a [email protected] metric of 98.9%, surpassing the performance of existing cutting-edge methodologies. The innovative detection methodology for dead chickens ensures not only rapid detection, but also marked improvement in detection fidelity, aligning with the demands of real-time monitoring in operational agricultural contexts.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"12 ","pages":"Article 101104"},"PeriodicalIF":6.3,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144291144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From the attitude towards digitalisation in agriculture to the acceptance of future agricultural technologies","authors":"Linda Reissig ,&nbsp;Michael Siegrist","doi":"10.1016/j.atech.2025.101095","DOIUrl":"10.1016/j.atech.2025.101095","url":null,"abstract":"<div><div>As agriculture undergoes a transformative phase propelled by technological innovations, the integration of digital farming tools is becoming increasingly prevalent in animal husbandry and arable farming. In animal husbandry, virtual fences, as a precision livestock farming technology, have emerged as a promising solution for managing livestock. Similarly, the rapid evolution of technology in arable farming continues to redefine the landscape of agricultural practices, with autonomous systems such as fully autonomous hacking robots playing a pivotal role. However, a limited understanding of the social and psychological factors and perceptions of risks and benefits influence farmers’ acceptance of these novel digital farming technologies in Switzerland. This study aimed to provide insights into farmers’ attitudes towards digital agriculture and to help understand the acceptance of digital farming technologies in the future. It sought to explore the drivers of and barriers to the acceptance of digital farming tools among family farm managers. A survey was conducted among 939 Swiss arable and animal farmers, and multiple linear regression models were used to determine robust predictors of attitude and acceptance of virtual fence technology and fully autonomous hacking robots. The results indicate that attitudes towards digital farming technologies depend on farmers’ characteristics, such as age, technology interaction affinity, education level, and digital competence, alongside their financial situation. Acceptance of virtual fences was influenced by farm characteristics (size, workforce), farmers’ perceptions (attitudes towards digital farming), digital competence, and risk–benefit perceptions. In contrast, the acceptance of fully autonomous hacking robots was influenced by farmers’ perceptions, education level, and risk–benefit perceptions. The results emphasise that the acceptance of specific technologies is driven by application-specific reasons and depends on risk–benefit assessments. The findings shed light on decision-making in digital agriculture for small-scale farms, highlighting the need for digital skill development and support for farmers in risk–benefit assessment. Recommendations include peer networks and research settings, such as model farms, to support farmers in adopting digital farming technologies.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"12 ","pages":"Article 101095"},"PeriodicalIF":6.3,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144320801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic swine body measurements via two-dimensional imaging system and correlation analysis with conformation scoring","authors":"Zhengxu Liu ,&nbsp;Wei Peng ,&nbsp;Chaoyuan Wang ,&nbsp;Guoming Li ,&nbsp;Boyu Ji ,&nbsp;David Casey ,&nbsp;Ning Lu ,&nbsp;Yunxiang Zhao","doi":"10.1016/j.atech.2025.101113","DOIUrl":"10.1016/j.atech.2025.101113","url":null,"abstract":"<div><div>The manual measurement of pig body dimension is not only labor-intensive and fallible but also causes stress to the animals. Moreover, the subjective and ambiguous visual assessment of pig body conformation by Chinese pig traders consistently impacted the sustainable profitability of pig farms. Considering long-term robustness, this study aimed to develop an automated measurement system for pig body measurements based on two-dimension (2D) machine vision. For the first time, pig traders from Chinese five regions (North, East, MidSouth, WestSouth, WestNorth) were invited to an objective survey of body conformation scoring. They were also invited to rate pig videos (Good-3, Medium-2, Bad-1) obtained by the system using specially developed software. Subsequently, correlation analysis and principal component analysis were conducted. The correlation coefficient for abdominal circumference (R²=0.85) and chest circumference (R²=0.84) by 2D imaging reflected strong agreements, improved by 0.13 (18.1 %) and by 0.15(21.7 %) respectively, compared with the similar 3D system. The 2D imaging also indicated a moderate discrepancy in the prediction of the above circumferences. The 2D imaging system should be more robust further considering the cost and maintenance difficulty in pig farm. In the survey, traders from all regions assigned consistently high importance on Body Length and Height. The variance was mainly from Fur&amp;Color and Belly, where the traders from Mid-South put a higher value to Fur&amp;Color, and East’s traders paid higher attention to Belly. Our study provided the first-ever objective data basis behind pig body conformation by Chinese traders. It was concluded that the belly_height by the 2D imaging showed the most significant positive impact on trader conformation scoring for most regions except West-South. Whereas the belly_y_size (diameter of the digitally defined belly part) by the 2D imaging exerted the most significant negative effect on conformation scoring in most regions, only that it did have negative effect in East and WestSouth though under the significant level. Figuratively, pigs perceived as ‘higher’ with ‘smaller' bellies showed stronger market preference among traders in China.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"12 ","pages":"Article 101113"},"PeriodicalIF":6.3,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144481624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection of insect-damaged sunflower seeds using near-infrared hyperspectral imaging and machine learning","authors":"Bright Mensah ,&nbsp;Jarrad Prasifka ,&nbsp;Brent Hulke ,&nbsp;Ewumbua Monono ,&nbsp;Xin Sun","doi":"10.1016/j.atech.2025.101110","DOIUrl":"10.1016/j.atech.2025.101110","url":null,"abstract":"<div><div>Insect damage can significantly affect seed germination rates and overall seed quality, resulting in notable economic losses. Detecting insect-damaged seeds is vital for upholding food safety standards and satisfying consumer expectations in confectionery sunflower markets. To tackle this issue, this study explores the potential of hyperspectral imaging combined with machine learning to accurately classify damaged and undamaged sunflower seeds. Spectral data were acquired and preprocessed using principal component analysis (PCA) to reduce dimensionality while retaining essential spectral information. Machine learning techniques, specifically multilayer perceptron (MLP), support vector machine (SVM), random forest (RF), light gradient boosting machine (LGBM), extreme gradient boosting (XGB), gradient boosting (GB), and partial least squares discriminant analysis (PLS-DA), were trained and evaluated based on the spectral features. The results showed that MLP achieved the highest classification performance with an accuracy of 0.91 and an F1-score of 0.91, followed by SVM with an accuracy of 0.89 and an F1-score of 0.89. LGBM and RF also performed well, both achieving an accuracy of 0.88 and an F1-score of 0.88, while XGB and GB recorded accuracies of 0.85 and 0.86, respectively. In contrast, PLS-DA demonstrated the lowest performance, with accuracy falling to 0.65 and an F1-score of 0.64. These findings underscore the effectiveness of machine learning in utilizing hyperspectral data for precise seed quality assessment. Its integration into the seed sorting process can enhance seed inspections, food safety, damage scoring for scientific investigations, and ensure that only high-quality seeds are chosen for planting.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"12 ","pages":"Article 101110"},"PeriodicalIF":6.3,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144306796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Collaboration of hyperspectral data and generative adversarial networks for improved nitrogen nutrition diagnosis and nitrogen requirement estimation in winter wheat","authors":"Changchun Li ,&nbsp;Bo Yang ,&nbsp;Guangsheng Zhang ,&nbsp;Le Xu ,&nbsp;Yinghua Jiao ,&nbsp;Taiyi Cai ,&nbsp;Longfei Zhou","doi":"10.1016/j.atech.2025.101112","DOIUrl":"10.1016/j.atech.2025.101112","url":null,"abstract":"<div><div>Nitrogen nutrient diagnosis and nitrogen requirement (NR) estimation are key components for accurate and precise crop fertilizer management. Owing to the limitations of field data collection, the number of measured samples is usually small and unbalanced, resulting in errors in model estimation accuracy. Challenges remain in accurately obtaining nitrogen nutrient diagnostics and estimating nitrogen fertilizer requirements. In this study, hyperspectral canopy data and measured data of winter wheat were acquired. The generative adversarial networks (GAN) was used to generate the winter wheat canopy hyperspectral dataset, and the original dataset, the GAN balanced dataset and the GAN hybrid dataset were constructed. The nitrogen concentration and biomass were estimated by combining partial least squares regression (PLSR), Gaussian process regression (GPR) and one-dimensional convolutional neural network (1D-CNN) models. Based on the estimation results, the nitrogen nutrient index (NNI) was calculated via the critical nitrogen dilution curve, and the NR estimation model was established with integrated consideration of days after sowing, nitrogen recovery efficiency, and the NNI. The results show that the GAN can meet the extension needs of small sample datasets, and the quality of the generated data is reliable enough at epoch=2000 and performs best when the amount of generated data reaches two times the original amount of data. Among the three models, GPR had the highest accuracy in estimating nitrogen concentration, whereas the 1D-CNN performed best in estimating biomass. Compared with the original dataset (R² = 0.88 for nitrogen concentration and R² = 0.82 for biomass), the R² values for nitrogen concentration and biomass estimation were 0.94 and 0.91 on the GAN balanced dataset and 0.97 and 0.92 on the GAN hybrid dataset. Compared with those of the original dataset, the R² values for estimating nitrogen concentration and biomass improved by 10.2 % and 12.1 %, respectively. R²=0.90 and RMSE=0.11 for the estimation of the winter wheat NNI based on nitrogen concentration and biomass were further obtained, with R²=0.80 and RMSE=22.86 for the estimation of NR. This study demonstrated the potential of the GAN application in hyperspectral data generation, which provides strong support for the precise management of nitrogen fertilization in winter wheat.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"12 ","pages":"Article 101112"},"PeriodicalIF":6.3,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144320798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computer simulation of pesticide deposition and drift by conventional and intelligent air-assisted sprayers in apple orchards","authors":"Matthew J. Herkins ,&nbsp;Se-Woon Hong ,&nbsp;Lingying Zhao ,&nbsp;Heping Zhu ,&nbsp;Hongyoung Jeon","doi":"10.1016/j.atech.2025.101111","DOIUrl":"10.1016/j.atech.2025.101111","url":null,"abstract":"<div><div>To enhance pesticide sprayer performance, a laser guided variable-rate spraying system was developed to efficiently deliver spray outputs to a variety of plants across different growth stages. However, evaluating the performance of this system using field experiments is challenging and resource intensive. The Simulation of Air-Assisted Sprayers (SAAS), a cost-effective and user-friendly computational fluid dynamics (CFD) simulation program, was used to evaluate pesticide deposition and drift in apple orchards under varying spray and weather conditions. Results indicated that pesticide deposition efficiency was highest when very fine droplets were applied to apple trees under low wind speeds (&lt; 1.79 m <em>s</em>⁻¹), low relative humidity (&lt; 30 %), and high ambient air temperatures (&gt; 20 °C). Ground deposition losses were highest when spray nozzles producing very coarse droplets were applied at low travel speeds (0.89 m <em>s</em>⁻¹), low wind speeds, and high ambient air temperatures. Airborne drift was highest when a sprayer discharged very fine droplets under low travel speeds, high wind speeds (&gt; 3.58 m/s), high relative humidity (&gt; 70 %), and low ambient air temperatures (10 °C). The simulation results showed the intelligent sprayer was expected to reduce pesticide usage by 38.4 % to 51.9 % and improve average spray efficiency by 1.6 to 3.3 times depending on the nozzle type compared to a conventional spray system. This research demonstrated the SAAS could be used to optimize pesticide applications, improve spray efficiency, and reduce environmental impact.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"12 ","pages":"Article 101111"},"PeriodicalIF":6.3,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}