Computers and Electronics in Agriculture最新文献

{"title":"The influence of a seeding plate of the air-suction minituber precision seed-metering device on seeding quality","authors":"Zhiming Zhao ,&nbsp;Yining Lyu ,&nbsp;Jinqing Lyu ,&nbsp;Xiaoxin Zhu ,&nbsp;Jicheng Li ,&nbsp;Deqiu Yang","doi":"10.1016/j.compag.2024.109680","DOIUrl":"10.1016/j.compag.2024.109680","url":null,"abstract":"<div><div>The existing seed-metering device has the problems of low qualified index and high multiple index of minituber mechanized seeding. In this work, a seed-metering device suitable for precision seeding of minituber was designed to solve the above problems and improve the seeding efficiency. By analyzing the motion mechanism of minituber on the seeding plate, it is determined that the diameter of the suction seeding hole, the rotation speed and tilt angle of the seeding plate and the negative pressure value are the main factors affecting the seeding performance of the seed-metering device. The steady-state airflow in the negative pressure chamber was analyzed by computational fluid dynamics. When the diameter of the suction seeding hole is 8 mm and the rotation speed of the seeding plate is 40 r/min, the highest negative pressure value is reached at the suction seeding hole. The CFD-DEM coupling simulation method was used to investigate the stress of minituber and the effect of adsorption of minituber by suction seeding hole under different tilt angles of seeding plate and negative pressures. The coupling simulation results were verified and optimized by bench test, and the movement of the minituber on the seeding plate was observed by a high-speed camera. Design Expert was used to optimize the test results, and it is found that when the tilt angle is 20° and the negative pressure is −6000 Pa, the working effect of the seed-metering device could achieve the multiple index is below 3.5 %, the miss seeding index no more than 1.5 %, the qualified index remained above 94.5 %, and the coefficient of variation is kept under 11 %. This work puts forward new ideas in improving the seeding quality of high-speed precision seed-metering device, and also provides a new design idea for the development of seeding device.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109680"},"PeriodicalIF":7.7,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142699880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Crop canopy volume weighted by color parameters from UAV-based RGB imagery to estimate above-ground biomass of potatoes","authors":"Yang Liu ,&nbsp;Fuqin Yang ,&nbsp;Jibo Yue ,&nbsp;Wanxue Zhu ,&nbsp;Yiguang Fan ,&nbsp;Jiejie Fan ,&nbsp;Yanpeng Ma ,&nbsp;Mingbo Bian ,&nbsp;Riqiang Chen ,&nbsp;Guijun Yang ,&nbsp;Haikuan Feng","doi":"10.1016/j.compag.2024.109678","DOIUrl":"10.1016/j.compag.2024.109678","url":null,"abstract":"<div><div>Current techniques to estimate crop aboveground biomass (AGB) across the multiple growth stages mainly used optical remote-sensing techniques. However, this technology was limited by saturation of the canopy spectrum. To meet this problem, this study used digital images obtained by an unmanned aerial vehicle to extract the spectral and structural indicators of the crop canopy in three key potato growth stages. We took the color parameters (CP) of assorted color space transformations as the canopy spectral information, and crop height (CH), crop coverage (CC), and crop canopy volume (CCV) as the canopy structural indicators. Based on the complementary advantages of CP and CCV, we proposed a new metric: the color parameter-weighted crop-canopy volume (CCV_CP). Results showed that the CH, CCV, and CCV_CP correlated more strongly with potato AGB during the multi-growth stages than do CP and CC. The hue-weighted crop-canopy volume (CCV_H) correlated most strongly with the potato AGB among all structural indicators. Using CH was more accurate in estimating potato AGB compared to CP and CC. Combining indicators (CP + CC/CH, CP + CC + CH) improved the accuracy of potato AGB estimation over the multi-growth stages. Except for the CP + CC + CH model, other AGB estimation models produced inaccurate AGB estimation than the models based on CCV and CCV_H. The AGB estimation accuracy produced by the univariate-based CCV_H model (R² = 0.65, RMSE = 281 kg/hm², and NRMSE = 23.61 %) was comparable to that of the complex model [CP + CC + CH using random forest (RF) or multiple stepwise regression (MSR)]. Compared with CP + CC + CH using RF and MSR, the RMSE decreased and increased by 0.35 % and 4.24 %, respectively. Compared with CP, CP + CC, CP + CH, and CCV, the use of CCV_H to estimate AGB decreased the RMSE by 10.24 %, 7.42 %, 6.36 %, and 6.33 %, respectively. Meanwhile, the performance of CCV_H was verified at different stages and among varieties. Thus, this indicator can be used for monitoring potato growth to help guide field production management.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109678"},"PeriodicalIF":7.7,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142699903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a pumpkin fruits pick-and-place robot using an RGB-D camera and a YOLO based object detection AI model","authors":"Liangliang Yang,&nbsp;Tomoki Noguchi,&nbsp;Yohei Hoshino","doi":"10.1016/j.compag.2024.109625","DOIUrl":"10.1016/j.compag.2024.109625","url":null,"abstract":"<div><div>It is a hard job for farmers to harvest heavy fruits such as pumpkin fruits because of the aging problem of farmers. To solve this problem, this study aims to develop an automatic pick-and-place robot system that alleviates labor demands in pumpkin harvesting. We proposed a system capable of detecting pumpkins in the field and obtaining their three-dimensional (3D) coordinate values using artificial intelligence (AI) object detection methods and RGB-D camera, respectively. The harvesting system incorporates a crawler-type vehicle as the base platform, while a collaborative robot arm is employed to lift the pumpkin fruits. A newly designed robot hand, mounted at the end of the robot arm, is responsible for grasping the pumpkins. In this paper, we utilized various versions of YOLO (from version 2 to 8) for pumpkin fruit detection, and compare the results obtained from these different versions. The RGB-D camera, that was mounted at the root of the robot arm, captures the position of the pumpkin fruits in camera coordinates. We proposed a calibration method can simply transform the position to the coordinates of robot arm. In addition, we finished all the software and hardware of the pumpkin fruits pick-and-place robot system. Field experiments were conducted at an outdoor pumpkin field. The experiments demonstrate the fruits detection accuracy rate exceeding 99% and a picking success rate surpassing 90%. However, fruits that were surrounded by excessive vines could not be successfully grasped.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109625"},"PeriodicalIF":7.7,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142699950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unmanned Aerial Vehicle-based Autonomous Tracking System for Invasive Flying Insects","authors":"Jeonghyeon Pak ,&nbsp;Bosung Kim ,&nbsp;Chanyoung Ju ,&nbsp;Hyoung Il Son","doi":"10.1016/j.compag.2024.109616","DOIUrl":"10.1016/j.compag.2024.109616","url":null,"abstract":"<div><div>The Asian hornet or yellow-legged hornet, <em>Vespa velutina nigrithorax</em>, is a global predator of honeybees (<em>Apis mellifera</em> L.) that has become widespread owing to rapid climate change. Herein, we propose a localization system for tracking radio-tagged hornets and discovering hornet hives by combining unmanned aerial vehicles with a trilateration system. By leveraging the homing instinct of hornets, we systematically structured our experiments as a behavioral experiment, ground-truth experiment, and localization experiment. According to the experimental results, we successfully discovered the hives of two of the five hornets tested. Additionally, a comprehensive analysis of the experimental outcomes provided insights into hornet flight patterns and behaviors. The results of this research demonstrate the efficacy of integrating UAVs with radio telemetry for precision object tracking and ecosystem management, offering a robust tool for mitigating the impacts of invasive species on honeybee populations.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109616"},"PeriodicalIF":7.7,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142699879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing plant disease classification: A robust and generalized approach with transformer-fused convolution and Wasserstein domain adaptation","authors":"Muhammad Hanif Tunio ,&nbsp;Jian ping Li ,&nbsp;Xiaoyang Zeng ,&nbsp;Awais Ahmed ,&nbsp;Syed Attique Shah ,&nbsp;Hisam-Uddin Shaikh ,&nbsp;Ghulam Ali Mallah ,&nbsp;Imam Abdullahi Yahya","doi":"10.1016/j.compag.2024.109574","DOIUrl":"10.1016/j.compag.2024.109574","url":null,"abstract":"<div><div>Plant diseases pose significant threats to agricultural productivity and food security. Owing to a scarcity of field environment datasets, the prevailing plant disease classification approaches, trained on laboratory-controlled datasets, often grapple with achieving optimal performance in real-world environments. We proposed a novel and robust framework for Unsupervised Domain Adaptation (UDA), employing an adversarial learning approach with a Wasserstein distance-informed algorithm to learn domain invariant feature representations capable of generalizing more diverse features. This approach incorporates insights from a labeled source domain and adopts an unlabeled target domain by minimizing the distribution discrepancies between domains. Recently, mobile vision transformer (MViT)-based methods have been applied to UDA due to their ability to capture long-distance feature dependencies. However, these methods overlook the fact that MViT lacks effectiveness in extracting local feature details. The proposed framework combines the advantages of convolutional neural networks (CNNs) and MViTs, integrating local features extracted by CNNs with global features captured by MViTs. This fusion of local and global representations enhances transferability and feature discriminability within the domains. Furthermore, we incorporate a feature-fusing method to align channel dimensions and enhance the local details of the global representation. Extensive experiments using three plant disease datasets demonstrate the effectiveness and efficiency of our approach, yielding significant improvements in classification performance with 13.67%, compared to state-of-the-art (SOTA) and baseline methods. Our framework offers a promising solution for robust and efficient plant disease classification, providing valuable insights for sustainable agriculture and crop management.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109574"},"PeriodicalIF":7.7,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142700046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and experimental analysis of real-time detection system for The seeding accuracy of rice pneumatic seed metering device based on the improved YOLOv5n","authors":"He Xing ,&nbsp;Yikai Wan ,&nbsp;Peng Zhong ,&nbsp;Junjiang Lin ,&nbsp;Mingtao Huang ,&nbsp;Ru Yang ,&nbsp;Ying Zang","doi":"10.1016/j.compag.2024.109614","DOIUrl":"10.1016/j.compag.2024.109614","url":null,"abstract":"<div><div>The acquisition of rice seeding accuracy information could provide adequate support for the operational status of the rice pneumatic seed metering device and field management in the later stages. However, this task proved difficult due to the high speed of rice seeding and the occurrence of non-single seed seeding. In order to achieve real-time detection of seeding accuracy during the rice pneumatic seed metering device operation, a real-time detection system for the seeding accuracy of the device was designed. This paper introduced the system’s main components and working principles in detail and proposed a rice seed accuracy detection algorithm based on the improved YOLOv5n.The algorithm utilised the Faster-Net neural network, replacing the CSPDarknet53 network that served as the backbone of the original algorithm. Additionally, it incorporated the CARAFE operator and introduced the Soft-NMS-CIOU technique, a form of soft non-maximum suppression, along with integrating the CBAM attention mechanism module. These enhancements improved the model’s feature extraction capability on rice seed images, enabling real-time detection of small rice seeds in the dark environment within the rice pneumatic seed metering device. This improved accuracy in recognising small rice seed images and reduced the probability of false detections. Through comparative analysis with different algorithms, test results demonstrated that this algorithm exhibited a higher pass rate and faster response time compared to others. A verification test was conducted to evaluate identification accuracy at various seed sucking plate rotational speeds. The detection accuracies were 96 %, 96 %, 98.65 %, 88.8 % and 91 %, respectively, at seed sucking plate rotational speeds of 10, 20, 30, 40, and 50 r/min, with a suction negative pressure of 1.6 kPa. Based on the experimental findings, the algorithm met the requirements for seeding detection and could serve as a foundation for further research into seeding accuracy detection algorithms for rice pneumatic seed metering devices.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109614"},"PeriodicalIF":7.7,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142699907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sustainable smart system for vegetables plant disease detection: Four vegetable case studies","authors":"Ahmed M. Ali ,&nbsp;Adam Słowik ,&nbsp;Ibrahim M. Hezam ,&nbsp;Mohamed Abdel-Basset","doi":"10.1016/j.compag.2024.109672","DOIUrl":"10.1016/j.compag.2024.109672","url":null,"abstract":"<div><div>Agriculture is the backbone of the country’s economy. People depend on agriculture for food and exporting to generate income. However, agriculture faces various diseases that affect the quantity and quality of vegetables. Therefore, it is important to propose a model for detecting vegetable diseases. This study proposed a sustainable smart system for vegetable disease detection and classification. This system detects early vegetable diseases in common vegetables such as tomato, potato, lettuce, and cucumber. The study employed deep learning (DL) models to detect and classify vegetable diseases. Convolutional neural networks (CNN) are a type of DL model used for image classification. This study utilizes CNN and other extensions, such as VGG16 and MobileNet, for plant image classification. Three DL models were trained on four datasets for tomato disease classification, potato disease classification, lettuce disease classification, and cucumber disease classification. The results show that the three models achieved 84.49% accuracy on the tomato disease dataset, 97.65% accuracy on the cucumber disease dataset, 97% accuracy on the potato disease dataset, and 99.9% accuracy on the lettuce disease dataset. The proposed system can assist farmers in the early detection of vegetable diseases before they spread, and it can enhance agriculture by improving both the quality and quantity of products.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109672"},"PeriodicalIF":7.7,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142699904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A high-throughput method for monitoring growth of lettuce seedlings in greenhouses based on enhanced Mask2Former","authors":"Xiao Wei ,&nbsp;Yue Zhao ,&nbsp;Xianju Lu ,&nbsp;Minggang Zhang ,&nbsp;Jianjun Du ,&nbsp;Xinyu Guo ,&nbsp;Chunjiang Zhao","doi":"10.1016/j.compag.2024.109681","DOIUrl":"10.1016/j.compag.2024.109681","url":null,"abstract":"<div><div>Monitoring plant growth is crucial for cultivation management. Agronomists can assess the health status of lettuce seedlings based on monitoring results to implement relevant management measures for improving the quality and yield of lettuce seedlings. This study developed a non-destructive, high-throughput growth monitoring method suitable for large-scale assessment of lettuce seedling quality in nurseries. The method utilizes a plant high-throughput phenotyping platform to acquire 10-day time-series imagery data. An Mask2Former network model enhanced by multidimensional collaborative attention mechanism, combined with sliding window and morphological operations, achieves precise recognition and localization of seedling trays, varieties, and individual seedling plants in a progressive manner. Based on individual seedling localization and segmentation results, the method estimates emergence numbers and rates for each variety, and further achieves instance segmentation and counting of individual seedling leaves, innovatively constructing leaf segmentation results of different varieties across the entire seedling tray. Applied to time-series images, the method automatically monitored seedling emergence changes and growth trends for 1,086 lettuce varieties. In monitoring these varieties, the method achieved a coefficient of determination (<em>R²</em>) of 0.96 for emergence number estimation. The extraction of all six key phenotypic parameters demonstrated exceptionally high correlations: projected area, projected perimeter, convex hull area, and convex hull perimeter all showed <em>R²</em> above 0.99, while leaf compactness <em>R²</em> was 0.9698, and leaf count <em>R²</em> was 0.91. Results demonstrate that this high-throughput, reliable method can effectively monitor the growth status of large-scale lettuce seedlings and provide technical support for lettuce nursery quality assessment.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109681"},"PeriodicalIF":7.7,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142699881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rethinking lightweight sheep face recognition via network latency-accuracy tradeoff","authors":"Xiaopeng Li,&nbsp;Yichi Zhang,&nbsp;Shuqin Li","doi":"10.1016/j.compag.2024.109662","DOIUrl":"10.1016/j.compag.2024.109662","url":null,"abstract":"<div><div>Deep learning has greatly improved the performance of sheep face recognition, but existing recognition methods usually adopt deeper and wider networks to obtain better performance, resulting in heavy computational burden and slow inference speed. This paper proposes a very lightweight sheep face recognition network, referred to as VLFaceNet, which achieves state-of-the-art (SOTA) latency-accuracy tradeoff. The basic module of VLFaceNet is VL, which uses inexpensive linear operations to complement redundant features and reduces the model size and computational complexity through structural re-parameterization during inference, improving inference speed. VLDBlock is formed by concatenating VL and ECA channel attention to enhance the effectiveness of channel-level feature extraction. VLFaceNet is formed by stacking VL and VLDBlock. By fusing features of different scales of VLFaceNet, sheep faces of different scales can be recognized, improving the recognition performance of the model. To address the problem of high similarity and difficulty in distinguishing white sheep faces, this paper proposes a scaling feature enhancement method SFE, which changes the color distribution and texture of sheep face images, improving the distinguishability between sheep face images and thus the recognition performance of VLFaceNet. The recognition performance gains of multiple recognition models demonstrate the effectiveness of SFE. On a self-built dataset, VLFaceNet achieves the best latency-accuracy tradeoff with an inference latency of 2.58 ms and a recognition accuracy of 97.75 %. This research is expected to promote the application of deep learning-based recognition methods in livestock breeding.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109662"},"PeriodicalIF":7.7,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142699951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A review of the current status and common key technologies for agricultural field robots","authors":"Lei Liu ,&nbsp;Fan Yang ,&nbsp;Xiangyi Liu,&nbsp;Yuefeng Du,&nbsp;Xiaoyu Li,&nbsp;Guorun Li,&nbsp;Du Chen,&nbsp;Zhongxiang Zhu,&nbsp;Zhenghe Song","doi":"10.1016/j.compag.2024.109630","DOIUrl":"10.1016/j.compag.2024.109630","url":null,"abstract":"<div><div>The arrival of the Industrial Revolution 4.0 and the quick advancement of artificial intelligence (AI), cloud computing, and internet of things (IoT) have led to the beginning of a new technological revolution in agriculture. As an important branch of agricultural robots, field robots have an irreplaceable role in guaranteeing food security, seizing the global agricultural high ground, and realizing sustainable agricultural development. In this study, we first clarified the definition of field robots and review the different development stages of agricultural robots. Secondly, we searched and screened 678 relevant literature from electronic databases such as Science Direct, Web of Science, etc., and quantitatively analyzed all the literature in terms of three dimensions: year of publication, country, and keywords. Then, we systematically provided the current research status of ploughing-seeding, weeding, monitoring, harvesting, and information-gathering, crop protection and agricultural fly robots. Furthermore, the global development of field robots shows a trend of rapid growth and technology upgrading, and we proposed the common key technologies of field robots, including intelligent sensing, smart decision-making and control, dexterous arm and hand operation, autonomous and stable walking, and Cloud-Edge-End fusion intelligent operation. Finally, we summarized the challenges faced by robots, such as low sensing accuracy, poor operability, and low operating efficiency. Therefore, we attempted to provide new opportunities for the future development of field robots, such as new technologies such as unstructured information analysis, 5G, AI, digital twins, robots swarms collaboration, and other technologies, which enable the realization of field operation scenarios with fully autonomous perception.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109630"},"PeriodicalIF":7.7,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142699946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}