Artificial Intelligence in Agriculture最新文献

{"title":"Using UAV-based multispectral images and CGS-YOLO algorithm to distinguish maize seeding from weed","authors":"Boyi Tang ,&nbsp;Jingping Zhou ,&nbsp;Chunjiang Zhao ,&nbsp;Yuchun Pan ,&nbsp;Yao Lu ,&nbsp;Chang Liu ,&nbsp;Kai Ma ,&nbsp;Xuguang Sun ,&nbsp;Ruifang Zhang ,&nbsp;Xiaohe Gu","doi":"10.1016/j.aiia.2025.02.007","DOIUrl":"10.1016/j.aiia.2025.02.007","url":null,"abstract":"<div><div>Accurate recognition of maize seedlings on the plot scale under the disturbance of weeds is crucial for early seedling replenishment and weed removal. Currently, UAV-based maize seedling recognition depends primarily on RGB images. The main purpose of this study is to compare the performances of multispectral images and RGB images of unmanned aerial vehicle (UAV) on maize seeding recognition using deep learning algorithms. Additionally, we aim to assess the disturbance of different weed coverage on the recognition of maize seeding. Firstly, principal component analysis was used in multispectral image transformation. Secondly, by introducing the CARAFE sampling operator and a small target detection layer (SLAY), we extracted the contextual information of each pixel to retain weak features in the maize seedling image. Thirdly, the global attention mechanism (GAM) was employed to capture the features of maize seedlings using the dual attention mechanism of spatial and channel information. The CGS-YOLO algorithm was constructed and formed. Finally, we compared the performance of the improved algorithm with a series of deep learning algorithms, including YOLO v3, v5, v6 and v8. The results show that after PCA transformation, the recognition mAP of maize seedlings reaches 82.6 %, representing 3.1 percentage points improvement compared to RGB images. Compared with YOLOv8, YOLOv6, YOLOv5, and YOLOv3, the CGS-YOLO algorithm has improved mAP by 3.8, 4.2, 4.5 and 6.6 percentage points, respectively. With the increase of weed coverage, the recognition effect of maize seedlings gradually decreased. When weed coverage was more than 70 %, the mAP difference becomes significant, but CGS-YOLO still maintains a recognition mAP of 72 %. Therefore, in maize seedings recognition, UAV-based multispectral images perform better than RGB images. The application of CGS-YOLO deep learning algorithm with UAV multi-spectral images proves beneficial in the recognition of maize seedlings under weed disturbance.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 2","pages":"Pages 162-181"},"PeriodicalIF":8.2,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143512498","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":"Research on an orchard row centreline multipoint autonomous navigation method based on LiDAR","authors":"Chen Zhenyu ,&nbsp;Dou Hanjie ,&nbsp;Gao Yuanyuan ,&nbsp;Zhai Changyuan ,&nbsp;Wang Xiu ,&nbsp;Zou Wei","doi":"10.1016/j.aiia.2024.12.003","DOIUrl":"10.1016/j.aiia.2024.12.003","url":null,"abstract":"<div><div>Orchard intelligent equipment must perform autonomous navigation tasks along fruit tree row centrelines and headlands according to established operational requirements. The tree canopy obstructs satellite signals, limiting the accuracy and stability of the GNSS-based autonomous navigation system. This paper presents a multipoint autonomous navigation method with the orchard row centreline navigation capabilities by integrating light detection and ranging (LiDAR) and inertial measurement unit (IMU) data. The method begins by constructing a three-dimensional (3D) point cloud map of the orchard via the LIO_SAM algorithm, and a 3D point cloud-to-two-dimensional (2D) grid map algorithm is designed. This algorithm retains the tree trunk position information from the point cloud based on tree trunk features to obtain a 2D grid map for orchard navigation, and the navigation point coordinates were calculated based on tree trunk positions. A multipoint navigation method was designed, where the system automatically determines the completion status of the previous navigation point and sequentially issues navigation point coordinates, enabling autonomous navigation along the row centrelines and headlands during orchard operations. Row centreline navigation tests and headland turning tests were conducted, and the performances of 16-line and 32-line LiDAR with this method are compared. The research results reveal that the multipoint navigation method could achieve movement along orchard row centrelines and deploy autonomous turning. The 32-line LiDAR data demonstrated an average absolute lateral deviation of 1.83 cm, a standard deviation of 1.60 cm, and a maximum deviation of 10.30 cm at a 3-m navigation point interval, indicating greater precision. However, the turning time was longer, with increases of 8.11 % and 6.13 % with the two different turning methods compared to the 16-line LiDAR. The research results provide support for research on autonomous navigation technology for intelligent orchard equipment.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 2","pages":"Pages 221-231"},"PeriodicalIF":8.2,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143611175","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":"Identifying key factors influencing maize stalk lodging resistance through wind tunnel simulations with machine learning algorithms","authors":"Guanmin Huang,&nbsp;Ying Zhang,&nbsp;Shenghao Gu,&nbsp;Weiliang Wen,&nbsp;Xianju Lu,&nbsp;Xinyu Guo","doi":"10.1016/j.aiia.2025.01.007","DOIUrl":"10.1016/j.aiia.2025.01.007","url":null,"abstract":"<div><div>Climate change has intensified maize stalk lodging, severely impacting global maize production. While numerous traits influence stalk lodging resistance, their relative importance remains unclear, hindering breeding efforts. This study introduces an combining wind tunnel testing with machine learning algorithms to quantitatively evaluate stalk lodging resistance traits. Through extensive field experiments and literature review, we identified and measured 74 phenotypic traits encompassing plant morphology, biomass, and anatomical characteristics in maize plants. Correlation analysis revealed a median linear correlation coefficient of 0.497 among these traits, with 15.1 % of correlations exceeding 0.8. Principal component analysis showed that the first five components explained 90 % of the total variance, indicating significant trait interactions. Through feature engineering and gradient boosting regression, we developed a high-precision wind speed-ear displacement prediction model (R² = 0.93) and identified 29 key traits critical for stalk lodging resistance. Sensitivity analysis revealed plant height as the most influential factor (sensitivity coefficient: −3.87), followed by traits of the 7th internode including epidermis layer thickness (0.62), pith area (−0.60), and lignin content (0.35). Our methodological framework not only provides quantitative insights into maize stalk lodging resistance mechanisms but also establishes a systematic approach for trait evaluation. The findings offer practical guidance for breeding programs focused on enhancing stalk lodging resistance and yield stability under climate change conditions, with potential applications in agronomic practice optimization and breeding strategy development.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 2","pages":"Pages 316-326"},"PeriodicalIF":8.2,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704965","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 sugar beet yield and quality parameters using Stacked-LSTM model with pre-harvest UAV time series data and meteorological factors","authors":"Qing Wang ,&nbsp;Ke Shao ,&nbsp;Zhibo Cai ,&nbsp;Yingpu Che ,&nbsp;Haochong Chen ,&nbsp;Shunfu Xiao ,&nbsp;Ruili Wang ,&nbsp;Yaling Liu ,&nbsp;Baoguo Li ,&nbsp;Yuntao Ma","doi":"10.1016/j.aiia.2025.02.004","DOIUrl":"10.1016/j.aiia.2025.02.004","url":null,"abstract":"<div><div>Accurate pre-harvest prediction of sugar beet yield is vital for effective agricultural management and decision-making. However, traditional methods are constrained by reliance on empirical knowledge, time-consuming processes, resource intensiveness, and spatial-temporal variability in prediction accuracy. This study presented a plot-level approach that leverages UAV technology and recurrent neural networks to provide field yield predictions within the same growing season, addressing a significant gap in previous research that often focuses on regional scale predictions relied on multi-year history datasets. End-of-season yield and quality parameters were forecasted using UAV-derived time series data and meteorological factors collected at three critical growth stages, providing a timely and practical tool for farm management. Two years of data covering 185 sugar beet varieties were used to train a developed stacked Long Short-Term Memory (LSTM) model, which was compared with traditional machine learning approaches. Incorporating fresh weight estimates of aboveground and root biomass as predictive factors significantly enhanced prediction accuracy. Optimal performance in prediction was observed when utilizing data from all three growth periods, with <em>R</em>² values of 0.761 (rRMSE = 7.1 %) for sugar content, 0.531 (rRMSE = 22.5 %) for root yield, and 0.478 (rRMSE = 23.4 %) for sugar yield. Furthermore, combining data from the first two growth periods shows promising results for making the predictions earlier. Key predictive features identified through the Permutation Importance (PIMP) method provided insights into the main factors influencing yield. These findings underscore the potential of using UAV time-series data and recurrent neural networks for accurate pre-harvest yield prediction at the field scale, supporting timely and precise agricultural decisions.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 2","pages":"Pages 252-265"},"PeriodicalIF":8.2,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143643996","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":"Deep learning-based classification, detection, and segmentation of tomato leaf diseases: A state-of-the-art review","authors":"Aritra Das ,&nbsp;Fahad Pathan ,&nbsp;Jamin Rahman Jim ,&nbsp;Md Mohsin Kabir ,&nbsp;M.F. Mridha","doi":"10.1016/j.aiia.2025.02.006","DOIUrl":"10.1016/j.aiia.2025.02.006","url":null,"abstract":"<div><div>The early identification and treatment of tomato leaf diseases are crucial for optimizing plant productivity, efficiency and quality. Misdiagnosis by the farmers poses the risk of inadequate treatments, harming both tomato plants and agroecosystems. Precision of disease diagnosis is essential, necessitating a swift and accurate response to misdiagnosis for early identification. Tropical regions are ideal for tomato plants, but there are inherent concerns, such as weather-related problems. Plant diseases largely cause financial losses in crop production. The slow detection periods of conventional approaches are insufficient for the timely detection of tomato diseases. Deep learning has emerged as a promising avenue for early disease identification. This study comprehensively analyzed techniques for classifying and detecting tomato leaf diseases and evaluating their strengths and weaknesses. The study delves into various diagnostic procedures, including image pre-processing, localization and segmentation. In conclusion, applying deep learning algorithms holds great promise for enhancing the accuracy and efficiency of tomato leaf disease diagnosis by offering faster and more effective results.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 2","pages":"Pages 192-220"},"PeriodicalIF":8.2,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143520260","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":"Advancing precision agriculture: A comparative analysis of YOLOv8 for multi-class weed detection in cotton cultivation","authors":"Ameer Tamoor Khan ,&nbsp;Signe Marie Jensen ,&nbsp;Abdul Rehman Khan","doi":"10.1016/j.aiia.2025.01.013","DOIUrl":"10.1016/j.aiia.2025.01.013","url":null,"abstract":"<div><div>Effective weed management plays a critical role in enhancing the productivity and sustainability of cotton cultivation. The rapid emergence of herbicide-resistant weeds has underscored the need for innovative solutions to address the challenges associated with precise weed detection. This paper investigates the potential of YOLOv8, the latest advancement in the YOLO family of object detectors, for multi-class weed detection in U.S. cotton fields. Leveraging the CottonWeedDet12 dataset, which includes diverse weed species captured under varying environmental conditions, this study provides a comprehensive evaluation of YOLOv8's performance. A comparative analysis with earlier YOLO variants reveals substantial improvements in detection accuracy, as evidenced by higher mean Average Precision (mAP) scores. These findings highlight YOLOv8's superior capability to generalize across complex field scenarios, making it a promising candidate for real-time applications in precision agriculture. The enhanced architecture of YOLOv8, featuring anchor-free detection, an advanced Feature Pyramid Network (FPN), and an optimized loss function, enables accurate detection even under challenging conditions. This research emphasizes the importance of machine vision technologies in modern agriculture, particularly for minimizing herbicide reliance and promoting sustainable farming practices. The results not only validate YOLOv8's efficacy in multi-class weed detection but also pave the way for its integration into autonomous agricultural systems, thereby contributing to the broader goals of precision agriculture and ecological sustainability.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 2","pages":"Pages 182-191"},"PeriodicalIF":8.2,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143520252","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":"Automatic body condition scoring system for dairy cows in group state based on improved YOLOv5 and video analysis","authors":"Jingwen Li ,&nbsp;Pengbo Zeng ,&nbsp;Shuai Yue ,&nbsp;Zhiyang Zheng ,&nbsp;Lifeng Qin ,&nbsp;Huaibo Song","doi":"10.1016/j.aiia.2025.01.010","DOIUrl":"10.1016/j.aiia.2025.01.010","url":null,"abstract":"<div><div>This study proposes an automated scoring system for cow body condition using improved YOLOv5 to assess the body condition distribution of herd cows, which significantly impacts herd productivity and feeding management. A dataset was created by capturing images of the cow's hindquarters using an image sensor at the entrance of the milking hall. This system enhances feature extraction ability by introducing dual path networks and convolutional block attention modules and improves efficiency by replacing some modules from the standard YOLOv5s with deep separable convolution to reduce parameters. Furthermore, the system employs an automatic detection and segmentation algorithm to achieve individual cow segmentation and body condition acquisition in the video. Subsequently, the system computes the body condition distribution of cows in a group state. The experimental findings demonstrate that the proposed model outperforms the original YOLOv5 network with higher accuracy and fewer computations and parameters. The precision, recall, and mean average precision of the model are 94.3 %, 92.5 %, and 91.8 %, respectively. The algorithm achieved an overall detection rate of 94.2 % for individual cow segmentation and body condition acquisition in the video, with a body condition scoring accuracy of 92.5 % among accurately detected cows and an overall body condition scoring accuracy of 87.1 % across the 10 video tests.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 2","pages":"Pages 350-362"},"PeriodicalIF":8.2,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143705546","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":"Efficient one-stage detection of shrimp larvae in complex aquaculture scenarios","authors":"Guoxu Zhang ,&nbsp;Tianyi Liao ,&nbsp;Yingyi Chen ,&nbsp;Ping Zhong ,&nbsp;Zhencai Shen ,&nbsp;Daoliang Li","doi":"10.1016/j.aiia.2025.01.009","DOIUrl":"10.1016/j.aiia.2025.01.009","url":null,"abstract":"<div><div>The swift evolution of deep learning has greatly benefited the field of intensive aquaculture. Specifically, deep learning-based shrimp larvae detection has offered important technical assistance for counting shrimp larvae and recognizing abnormal behaviors. Firstly, the transparent bodies and small sizes of shrimp larvae, combined with complex scenarios due to variations in light intensity and water turbidity, make it challenging for current detection methods to achieve high accuracy. Secondly, deep learning-based object detection demands substantial computing power and storage space, which restricts its application on edge devices. This paper proposes an efficient one-stage shrimp larvae detection method, FAMDet, specifically designed for complex scenarios in intensive aquaculture. Firstly, different from the ordinary detection methods, it exploits an efficient FasterNet backbone, constructed with partial convolution, to extract effective multi-scale shrimp larvae features. Meanwhile, we construct an adaptively bi-directional fusion neck to integrate high-level semantic information and low-level detail information of shrimp larvae in a matter that sufficiently merges features and further mitigates noise interference. Finally, a decoupled detection head equipped with MPDIoU is used for precise bounding box regression of shrimp larvae. We collected images of shrimp larvae from multiple scenarios and labeled 108,365 targets for experiments. Compared with the ordinary detection methods (Faster RCNN, SSD, RetinaNet, CenterNet, FCOS, DETR, and YOLOX_s), FAMDet has obtained considerable advantages in accuracy, speed, and complexity. Compared with the outstanding one-stage method YOLOv8s, it has improved accuracy while reducing 57 % parameters, 37 % FLOPs, 22 % inference latency per image on CPU, and 56 % storage overhead. Furthermore, FAMDet has still outperformed multiple lightweight methods (EfficientDet, RT-DETR, GhostNetV2, EfficientFormerV2, EfficientViT, and MobileNetV4). In addition, we conducted experiments on the public dataset (VOC 07 + 12) to further verify the effectiveness of FAMDet. Consequently, the proposed method can effectively alleviate the limitations faced by resource-constrained devices and achieve superior shrimp larvae detection results.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 2","pages":"Pages 338-349"},"PeriodicalIF":8.2,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704748","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":"Comprehensive review on 3D point cloud segmentation in plants","authors":"Hongli Song ,&nbsp;Weiliang Wen ,&nbsp;Sheng Wu ,&nbsp;Xinyu Guo","doi":"10.1016/j.aiia.2025.01.006","DOIUrl":"10.1016/j.aiia.2025.01.006","url":null,"abstract":"<div><div>Segmentation of three-dimensional (3D) point clouds is fundamental in comprehending unstructured structural and morphological data. It plays a critical role in research related to plant phenomics, 3D plant modeling, and functional-structural plant modeling. Although technologies for plant point cloud segmentation (PPCS) have advanced rapidly, there has been a lack of a systematic overview of the development process. This paper presents an overview of the progress made in 3D point cloud segmentation research in plants. It starts by discussing the methods used to acquire point clouds in plants, and analyzes the impact of point cloud resolution and quality on the segmentation task. It then introduces multi-scale point cloud segmentation in plants. The paper summarizes and analyzes traditional methods for PPCS, including the global and local features. This paper discusses the progress of machine learning-based segmentation on plant point clouds through supervised, unsupervised, and integrated approaches. It also summarizes the datasets that for PPCS using deep learning-oriented methods and explains the advantages and disadvantages of deep learning-based methods for projection-based, voxel-based, and point-based approaches respectively. Finally, the development of PPCS is discussed and prospected. Deep learning methods are predicted to become dominant in the field of PPCS, and 3D point cloud segmentation would develop towards more automated with higher resolution and precision.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 2","pages":"Pages 296-315"},"PeriodicalIF":8.2,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704964","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 review of the application prospects of cloud-edge-end collaborative technology in freshwater aquaculture","authors":"Jihao Wang ,&nbsp;Xiaochan Wang ,&nbsp;Yinyan Shi ,&nbsp;Haihui Yang ,&nbsp;Bo Jia ,&nbsp;Xiaolei Zhang ,&nbsp;Lebin Lin","doi":"10.1016/j.aiia.2025.02.008","DOIUrl":"10.1016/j.aiia.2025.02.008","url":null,"abstract":"<div><div>This paper reviews the application and potential of cloud-edge-end collaborative (CEEC) technology in the field of freshwater aquaculture, a rapidly developing sector driven by the growing global demand for aquatic products. The sustainable development of freshwater aquaculture has become a critical challenge due to issues such as water pollution and inefficient resource utilization in traditional farming methods. In response to these challenges, the integration of smart technologies has emerged as a promising solution to improve both efficiency and sustainability. Cloud computing and edge computing, when combined, form the backbone of CEEC technology, offering an innovative approach that can significantly enhance aquaculture practices. By leveraging the strengths of both technologies, CEEC enables efficient data processing through cloud infrastructure and real-time responsiveness via edge computing, making it a compelling solution for modern aquaculture. This review explores the key applications of CEEC in areas such as environmental monitoring, intelligent feeding systems, health management, and product traceability. The ability of CEEC technology to optimize the aquaculture environment, enhance product quality, and boost overall farming efficiency highlights its potential to become a mainstream solution in the industry. Furthermore, the paper discusses the limitations and challenges that need to be addressed in order to fully realize the potential of CEEC in freshwater aquaculture. In conclusion, this paper provides researchers and practitioners with valuable insights into the current state of CEEC technology in aquaculture, offering suggestions for future development and optimization to further enhance its contributions to the sustainable growth of freshwater aquaculture.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 2","pages":"Pages 232-251"},"PeriodicalIF":8.2,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143620570","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}