Smart agricultural technology最新文献

{"title":"Deoxynivalenol prediction and spatial mapping in wheat based on online hyperspectral imagery scanning","authors":"Muhammad Baraa Almoujahed ,&nbsp;Orly Enrique Apolo-Apolo ,&nbsp;Mohammad Alhussein ,&nbsp;Marius Kazlauskas ,&nbsp;Zita Kriaučiūnienė ,&nbsp;Egidijus Šarauskis ,&nbsp;Abdul Mounem Mouazen","doi":"10.1016/j.atech.2025.100947","DOIUrl":"10.1016/j.atech.2025.100947","url":null,"abstract":"<div><div>Deoxynivalenol (DON), a harmful mycotoxin produced by several <em>Fusarium</em> species, poses critical challenges to wheat production and food safety. However, reducing risks of human toxicity requires pre-harvest detection of DON concentration across different zones of a field. This study investigates the potential of integrating hyperspectral imaging (HSI) in the 400–1000 nm range with machine learning (ML) models for online field detection and mapping of DON contamination in wheat (<em>Triticum aestivum)</em>. Using a tractor-mounted push-broom hyperspectral camera, spectral data were collected across four commercial fields in Lithuania and Belgium. A total of 76 wheat samples collected during crop scanning were analyzed for DON levels using liquid chromatography-mass spectrometry (LC-MS). Initial analysis of spectral data alone revealed relatively low classification accuracy, with light gradient boosting machine (LGBM) achieving 55.92 % and decision tree classifier (DTC) achieving 51.97 %. However, the inclusion of fusarium head blight (FHB) severity as an additional feature significantly improved performance, boosting accuracy to 90.79 % for LGBM (a 62.4 % increase) and 86.18 % for DTC (a 65.8 % increase). Moreover, the use of mutual information (MI) for feature selection enhanced model accuracy, achieving 93.42 % for LGBM and 90.13 % for DTC. Spatial mapping of DON contamination demonstrated fair to substantial agreement with ground truth maps, providing valuable tools for farmers to understand DON distribution and implement targeted harvesting strategy. This study highlights the potential of integrating online HSI, ML, and feature selection techniques, for pre-harvest DON detection and mapping, providing valuable information for reducing risks of human toxicity and improving the economic value of wheat grain.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100947"},"PeriodicalIF":6.3,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143828411","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":"Deep learning framework for fruit counting and yield mapping in tart cherry using YOLOv8 and YOLO11","authors":"Anderson L.S. Safre ,&nbsp;Alfonso Torres-Rua ,&nbsp;Brent L. Black ,&nbsp;Sierra Young","doi":"10.1016/j.atech.2025.100948","DOIUrl":"10.1016/j.atech.2025.100948","url":null,"abstract":"<div><div>Object detection for fruit counting has significant potential for orchard yield estimation. Tart cherries are mechanically harvested, creating opportunities for developing new yield mapping technologies. However, there is a lack of dedicated technologies for this purpose, motivating the evaluation of computer vision-based approaches in tart cherries. In this study, we compared the nano (n) and extra-large (x) configurations of YOLOv8 and YOLO11 for tart cherry detection and fruit counting on the harvester. The models demonstrated robust performance, even in high object density conditions, with YOLOv11x achieving a mAP50 of 0.92. While YOLOv8n and YOLO11n produced similar detection results, YOLOv8n had a faster inference time, making it more suitable for real-time applications. Fruit counting was performed using a combination of YOLO models and the BoT-SORT tracking algorithm. The resulting number of fruits was compared to the actual weights of harvested fruit from individual trees. The results indicated a linear relationship, with YOLO11x achieving an R² of 0.62 and an RMSE of 10 kg. To the best of our knowledge, this is the first study to evaluate object detection and fruit counting performance in tart cherries during harvest. Additionally, we introduce a new dataset with annotated cherries on the conveyor belt of the harvester which can support further research and development. This approach addresses the existing technology gap in yield monitoring for tart cherry orchards, facilitating the application of precision agriculture and site-specific management strategies in the industry.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100948"},"PeriodicalIF":6.3,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143834385","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":"Analysis and experiment on seed-filling performance of double seed-taking precision dibbler for cotton","authors":"Zibin Mao ,&nbsp;Luochuan Xu ,&nbsp;Mengyu Guo ,&nbsp;Junwei Li ,&nbsp;Xin Luo ,&nbsp;Bin Hu ,&nbsp;Xiyang Li","doi":"10.1016/j.atech.2025.100946","DOIUrl":"10.1016/j.atech.2025.100946","url":null,"abstract":"<div><div>Aiming at the problem that cottonseeds cannot be separated and migrated from the population in time due to disordered seed-filling, short filling time, and fast seed-filling speed in the filling process of the existing mechanical precision dibbler, this paper innovated a mechanical precision dibbler for double seed-taking, in which the seed-taking disc is provided with a finite migration space that can effectively disrupt the population and assist seed-filling. It can ensure the seed-filling performance of the cotton precision dibbler at high speed (&gt; 4 km·h^-1). A numerical simulation of the seed-filling process was conducted using EDEM software, and the effects of kinetic energy and velocity of the population, moving track, and velocity of a single cottonseed on filling performance were examined. A three-factor, five-level quadratic rotational orthogonal test with rotational speed, population height, and thickness as test factors, filling index, and missing index as test indices. When the rotational speed of the seed-taking disc was 42.3 r·min^-1, that is, the working speed of the planter was 4.06 km·h^-1, the population height was 0.165 kg, and the thickness of the seed-taking disc was 5.5 mm, the filling index and missing index were 96.65% and 3.35%, respectively. This study not only provides a reference for the high-speed seed-filling theory of the type hole in precision dibblers but also contributes to the local seed cluster formation of ellipsoidal materials in the relative rotation space and the accelerated migration of a single target material to fill type holes.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100946"},"PeriodicalIF":6.3,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143828409","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":"Design of tabletop hemispherical light transmittance characterization system for small scale samples","authors":"Chun-Ting Cho,&nbsp;Johan de Haas,&nbsp;Erik van der Kolk","doi":"10.1016/j.atech.2025.100936","DOIUrl":"10.1016/j.atech.2025.100936","url":null,"abstract":"<div><div>Greenhouse owners highly value the hemispherical light transmittance (T_HEM) of roofing materials because sunlight rarely projects at a perpendicular angle, especially in high-latitude regions. With growing interest in research of advanced multi-functional greenhouse roofing, a compact and efficient T_HEM characterization system for lab-scale samples is needed to promote research in the horticulture field. In this study, we developed a tabletop system capable of characterizing T_HEM of lab-scale samples with a size one-third of that required by the current characterization system. Key designing parameters, such as the beam cross-section area, port area, and port edge thickness were systematically varied to evaluate their impact on T_HEM characterization. The results indicated that the total port area should be limited to under 1% of the sphere surface area with minimized edge thickness since reflection from the edge area can not be corrected by a double-beam measurement. Furthermore, the collimated beam cross-section area should exceed the port area by a factor of 1.5 to ensure that T_HEM remains unaffected by the sphere rotation center. The system provides a consistent and reliable method for T_HEM measurement and offers essential guidelines for future users to construct a similar setup.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100936"},"PeriodicalIF":6.3,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817320","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":"Transformer-based similarity learning for re-identification of chickens","authors":"Christian Lamping,&nbsp;Gert Kootstra,&nbsp;Marjolein Derks","doi":"10.1016/j.atech.2025.100945","DOIUrl":"10.1016/j.atech.2025.100945","url":null,"abstract":"<div><div>Continuous animal monitoring relies heavily on the ability to re-identify individuals over time, essential for both short-term tracking, such as video analysis, and long-term monitoring of animal conditions. Traditionally, livestock re-identification is approached using tags or sensors, which require additional handling effort and potentially impact animal welfare. In response to these limitations, non-invasive vision-based approaches have emerged recently, with existing research primarily focusing on the re-identification of pigs and cows. Re-identification of chickens, which exhibit high uniformity and are housed in larger groups, remains challenging and has received less research attention. This study addresses this gap by exploring the feasibility of re-identifying individual laying hens within uncontrolled farm environments using images of their heads. It proposes the first similarity-learning approach based on a VisionTransformer architecture to re-identify chickens without requiring training images for each individual bird. In our experiments, we compared the transformer-based approach to traditional CNN architectures while assessing the impact of different model sizes and triplet mining strategies during training. Moreover, we evaluated practical applicability by analyzing the effects of the number of images per chicken and overall population size on re-identification accuracy. Finally, we examined which visual features of the chicken head were most relevant for re-identification. Results show Top-1 accuracies exceeding 80 % for small groups and maintaining over 40 % accuracy for a population of 100 chickens. Moreover, it was shown that the transformer-based architecture outperformed CNN models, with the use of semi-hard negative samples during training yielding the best results. Furthermore, it was revealed that the evaluated models learned to prioritize features such as the comb, wattles, and ear lobes, often aligning with human perception. These results demonstrate promising potential for re-identifying chickens even when recorded in an uncontrolled farm environment, providing a foundation for future applications in animal tracking and monitoring.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100945"},"PeriodicalIF":6.3,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143839466","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":"Investigating Pea (Pisum sativum L.) Flowering with High Throughput Field Phenotyping and Object Detection","authors":"Corina Oppliger ,&nbsp;Radek Zenkl ,&nbsp;Achim Walter ,&nbsp;Beat Keller","doi":"10.1016/j.atech.2025.100942","DOIUrl":"10.1016/j.atech.2025.100942","url":null,"abstract":"<div><div>Flowering is one of the most important and sensitive processes throughout a plant's life and marks the start of the reproductive phase. Flowering traits largely define yield potential and are therefore crucial for crop breeding. To observe flowering dynamics under field conditions, visual ratings have been a standard method for decades. Today, high-throughput field phenotyping (HTFP) methods provide opportunities for objective and efficient data collection. We developed an object detection approach (based on YOLOv8) that allows to collect detailed data about flower and pod density. RGB-images from 12 pea breeding lines were automatically acquired by the field phenotyping platform (FIP) of ETH Zürich in two years. The trained model reached high accuracy for open flower detection, which allowed to monitor flowering dynamics and flower density over time. Maximal flower density (Max.Fl.Dens) was highly correlated (R²= 0.967) to ground truth data taken in the field. Clear differences in timing of flowering and flower density were detected between breeding lines and years. Furthermore, a high correlation was observed between the maximal flower density and yield components. This automated, data-driven method of flower and pod detection proved itself as a reliable tool. Therefore, the results are promising for the use of RGB imaging methods to objectively assess not only flowering dynamics but also flower density and fruiting efficiency. Maximal flower density allows to predict seed amount and therefore has potential as selection trait in breeding programs. Fruiting efficiency could be used to identify stress-tolerant breeding lines.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100942"},"PeriodicalIF":6.3,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143843318","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":"An LSTM–AE–Bayes embedded gateway for real-time anomaly detection in agricultural wireless sensor networks","authors":"Jun Shu ,&nbsp;Yuanhua Quan ,&nbsp;Dengke Yang","doi":"10.1016/j.atech.2025.100944","DOIUrl":"10.1016/j.atech.2025.100944","url":null,"abstract":"<div><div>Agricultural wireless sensor networks have become a critical data source for agricultural big data analytics. However, due to factors such as network transmission errors and human interference, multiple sensor nodes in these networks may generate anomalous data. Such anomalies can lead to inaccurate analyses, adversely affecting crop growth, and result in unnecessary energy consumption during data transmission. To address these challenges, this paper proposes a greenhouse gateway incorporating an anomaly detection algorithm. By integrating the detection algorithm directly into the gateway, only data deemed normal are forwarded. The gateway, built around the STM32F407ZGT6 microcontroller, utilizes LoRa and 4 G modules for wireless data transmission, enabling real-time data visualization on a cloud server. Experimental results demonstrate that the proposed anomaly detection algorithm not only outperforms conventional methods but also remains practical and effective when embedded within the gateway. We further observed that our approach reduced unnecessary data traffic by over 9 % and improved F1-score by about 5 % compared to state-of-the-art methods, confirming its efficiency in both performance and resource utilization.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100944"},"PeriodicalIF":6.3,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143820770","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":"Numerical simulation of the effects of downwash airflow and crosswinds on the spray performance of quad-rotor agricultural UAVs","authors":"Qiwei Guo ,&nbsp;Yaozong Zhu ,&nbsp;Yu Tang ,&nbsp;Chaojun Hou ,&nbsp;Mingwei Fang ,&nbsp;Xiaobing Chen","doi":"10.1016/j.atech.2025.100940","DOIUrl":"10.1016/j.atech.2025.100940","url":null,"abstract":"<div><div>To elucidate the influence of rotor downwash airflow on droplet dynamics during agricultural UAV spraying, this study established a three-dimensional gas-liquid coupling numerical model. The synergistic effects of flight speed (1–5 m/s), operation altitude (2–4 m), and crosswinds (0–2 m/s) on droplet deposition and drift were systematically analyzed. Results demonstrated that increased UAV flight speed significantly tilted the downwash airflow backward, exacerbating drift losses for smaller droplets. Higher operation altitudes prolonged droplet residence times within airflow, further elevating drift risk. Crosswind velocity positively correlated with downwash airflow deflection angles, expanding airflow coverage under crosswinds; however, increasing crosswind velocities unexpectedly reduced droplet deflection angles. Experimental validation revealed a relative error between simulated and measured deposition of 27.2 % to 30 %, confirming the model's reliability. This study uniquely uncovers droplet drift patterns under crosswind conditions, offering new theoretical insights for optimizing UAV spray operations.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100940"},"PeriodicalIF":6.3,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143851938","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":"Crop row identification and plant cluster segmentation for stand count from UAS imagery based on profile and geometry","authors":"S. Sunoj ,&nbsp;C. Igathinathane ,&nbsp;J.P. Flores ,&nbsp;H. Sidhu ,&nbsp;E. Monono ,&nbsp;B. Schatz ,&nbsp;D. Archer ,&nbsp;J. Hendrickson","doi":"10.1016/j.atech.2025.100938","DOIUrl":"10.1016/j.atech.2025.100938","url":null,"abstract":"<div><div>Plant stand count is an important measure to determine the attainment of the target plant population and obtain seed emergence characteristics. Unmanned aerial system (UAS) imagery is generally analyzed with commercial software for estimating plant stand count. However, such software is expensive, crop-specific, and imposes limitations. The available literature, research, and applications typically use such software, whose underlying working principles are unknown. Therefore, a user-coded, open-source, computer vision ImageJ crop row identification and stand count plugin termed “CRISCO” was developed and validated. The plugin integrates profile and geometry-based approaches in a customized framework that perform automatic crop row orientation, row identification, plant cluster segmentation, and plant stand counting from the UAS imagery. The plugin was validated on sunflower field images from two datasets “Set I” and “Set II” representing different flight altitudes, field areas, image resolutions, and growth stages. Crop row identification in the CRISCO plugin accurately identified rows up to <figure><img></figure> (tested) and it could potentially work with rows even <figure><img></figure>, provided the rows are straight, which is the case with modern planting methods. The developed segmentation approach by combining profile and geometry termed “ProGeo” resolved the plant clusters. Comparing ProGeo and watershed segmentation, the former produced 89–<figure><img></figure> of correct segmentation, while the latter produced 51–<figure><img></figure>. The plant-stand count accuracy of the plugin ranged from 85–<figure><img></figure> with CPU time for analysis ranging from 2–<figure><img></figure> for the two datasets. The user-coded plugin, although developed and tested on sunflower, can be extended with appropriate modifications to accommodate other row crops (e.g., soybeans, cotton).</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100938"},"PeriodicalIF":6.3,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143834390","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":"Feature enhanced multistream RNN for growth phase prediction of Euglena sp. microalgae in an IoT-based outdoor cultivation environment","authors":"Harnan Malik Abdullah ,&nbsp;Jazi Eko Istiyanto ,&nbsp;Aufaclav Zatu Kusuma Frisky ,&nbsp;Eko Agus Suyono","doi":"10.1016/j.atech.2025.100939","DOIUrl":"10.1016/j.atech.2025.100939","url":null,"abstract":"<div><div>Accurately predicting growth phases in microalgae cultivation is crucial for optimizing biomass production. IoT systems provide convenience in monitoring the cultivation environment in real time. However, the specific challenge of predicting microalgae growth phases still needs to be effectively addressed using IoT-based sequential monitoring data. This study introduces a novel architecture, the Feature-Enhanced Multistream Recurrent Neural Network (FEM-RNN), integrated with an IoT microalgae monitoring system to predict the growth phase of cultured microalgae, especially <em>Euglena sp.</em> species. The proposed method utilizes a dual channel architecture of recurrent neural networks to assess temporal environmental data, i.e., turbidity, temperature, and light intensity, acquired by the IoT system. One channel leverages all input features, while the other is specified for turbidity data. The proposed model was evaluated using a primary dataset collected by an IoT monitoring system from microalgae cultivation in outdoor environments. Three versions of FEM-RNN, i.e., utilizing the base model of Vanilla RNN, LSTM, and GRU, have been assessed in various sizes of window data. The performance of the FEM-RNN models increases with expanding the window size. All the variant models demonstrate high performance at window size 60, and the LSTM-based FEM-RNN demonstrates outstanding performance and stability beginning at the window size. At the size of the window, model performance has been compared to the traditional model, namely Vanilla RNN, LSTM, and GRU models, as well as CNN, Transformer, and SVM. The results show that the proposed model outperforms the conventional models, with an accuracy of 0.978, 0.989, and 0.951 for FEM-RNN based on Vanilla RNN, LSTM, and GRU, respectively. The results indicate that the FEM-RNN effectively predicts the microalgae growth phase utilizing IoT-based monitoring data.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100939"},"PeriodicalIF":6.3,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817321","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}