Computers and Electronics in Agriculture最新文献_第4页

The influence of a seeding plate of the air-suction minituber precision seed-metering device on seeding quality 气吸式微型单粒播种器的播种板对播种质量的影响

IF 7.7 1区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Computers and Electronics in Agriculture

Pub Date : 2024-11-22 DOI: 10.1016/j.compag.2024.109680

Zhiming Zhao , Yining Lyu , Jinqing Lyu , Xiaoxin Zhu , Jicheng Li , Deqiu Yang

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.

现有的种子计量装置存在微型旋耕机机械化播种合格率低、复种指数高的问题。为解决上述问题，提高播种效率，本研究设计了一种适用于微型推杆精量播种的种子计量装置。通过分析微型推杆在播种板上的运动机理，确定吸种孔直径、播种板转速和倾斜角度以及负压值是影响种子计量装置播种性能的主要因素。通过计算流体动力学分析了负压室内的稳态气流。当吸气排种孔直径为 8 mm、排种板转速为 40 r/min 时，吸气排种孔处的负压值最高。采用 CFD-DEM 耦合模拟方法研究了不同播种板倾斜角度和负压条件下微型微管的应力和吸种孔对微型微管吸附的影响。通过台架试验对耦合模拟结果进行了验证和优化，并用高速摄像机观察了微型吸盘在播种板上的运动。利用 Design Expert 对试验结果进行了优化，发现当倾斜角为 20°、负压为 -6000 Pa 时，种子计量装置的工作效果可以达到复种指数低于 3.5%，漏种指数不超过 1.5%，合格指数保持在 94.5%以上，变异系数保持在 11%以下。这项工作为提高高速精密种子计量装置的播种质量提出了新思路，也为播种装置的开发提供了新的设计思路。

{"title":"The influence of a seeding plate of the air-suction minituber precision seed-metering device on seeding quality","authors":"Zhiming Zhao , Yining Lyu , Jinqing Lyu , Xiaoxin Zhu , Jicheng Li , 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}

引用次数: 0

Crop canopy volume weighted by color parameters from UAV-based RGB imagery to estimate above-ground biomass of potatoes 利用基于无人机的 RGB 图像中的颜色参数对作物冠层体积进行加权，以估算马铃薯的地上生物量

IF 7.7 1区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Computers and Electronics in Agriculture

Pub Date : 2024-11-22 DOI: 10.1016/j.compag.2024.109678

Yang Liu , Fuqin Yang , Jibo Yue , Wanxue Zhu , Yiguang Fan , Jiejie Fan , Yanpeng Ma , Mingbo Bian , Riqiang Chen , Guijun Yang , Haikuan Feng

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.

目前估算作物多个生长阶段的地上生物量（AGB）的技术主要采用光学遥感技术。然而，这种技术受到冠层光谱饱和度的限制。针对这一问题，本研究利用无人机获取的数字图像提取了马铃薯三个关键生长阶段作物冠层的光谱和结构指标。我们将各种色彩空间变换的色彩参数（CP）作为冠层光谱信息，将作物高度（CH）、作物覆盖率（CC）和作物冠层体积（CCV）作为冠层结构指标。基于 CP 和 CCV 的互补优势，我们提出了一种新指标：颜色参数加权作物冠层体积（CCVCP）。结果表明，与 CP 和 CC 相比，CH、CCV 和 CCVCP 与马铃薯多生长期 AGB 的相关性更强。在所有结构指标中，色调加权作物冠层体积（CCVH）与马铃薯 AGB 的相关性最强。与 CP 和 CC 相比，使用 CH 估算马铃薯 AGB 更准确。组合指标（CP + CC/CH、CP + CC + CH）提高了马铃薯在多个生长阶段AGB估算的准确性。除 CP + CC + CH 模型外，其他 AGB 估算模型的 AGB 估算结果均低于基于 CCV 和 CCVH 的模型。基于单变量的CCVH模型（R2 = 0.65，RMSE = 281 kg/hm2，NRMSE = 23.61 %）的AGB估计精度与复合模型[使用随机森林（RF）或多元逐步回归（MSR）的CP + CC + CH]相当。与使用 RF 和 MSR 的 CP + CC + CH 相比，均方根误差分别减少了 0.35 % 和增加了 4.24 %。与 CP、CP + CC、CP + CH 和 CCV 相比，使用 CCVH 估算 AGB 的均方根误差分别降低了 10.24 %、7.42 %、6.36 % 和 6.33 %。同时，CCVH 的性能在不同阶段和不同品种之间都得到了验证。因此，该指标可用于监测马铃薯生长，帮助指导田间生产管理。

{"title":"Crop canopy volume weighted by color parameters from UAV-based RGB imagery to estimate above-ground biomass of potatoes","authors":"Yang Liu , Fuqin Yang , Jibo Yue , Wanxue Zhu , Yiguang Fan , Jiejie Fan , Yanpeng Ma , Mingbo Bian , Riqiang Chen , Guijun Yang , 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<sub>CP</sub>). Results showed that the CH, CCV, and CCV<sub>CP</sub> correlated more strongly with potato AGB during the multi-growth stages than do CP and CC. The hue-weighted crop-canopy volume (CCV<sub>H</sub>) 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<sub>H</sub>. The AGB estimation accuracy produced by the univariate-based CCV<sub>H</sub> model (R<sup>2</sup> = 0.65, RMSE = 281 kg/hm<sup>2</sup>, 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<sub>H</sub> to estimate AGB decreased the RMSE by 10.24 %, 7.42 %, 6.36 %, and 6.33 %, respectively. Meanwhile, the performance of CCV<sub>H</sub> 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}

引用次数: 0

Development of a pumpkin fruits pick-and-place robot using an RGB-D camera and a YOLO based object detection AI model 利用 RGB-D 摄像机和基于 YOLO 的物体检测人工智能模型开发南瓜水果拾放机器人

IF 7.7 1区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Computers and Electronics in Agriculture

Pub Date : 2024-11-22 DOI: 10.1016/j.compag.2024.109625

Liangliang Yang, Tomoki Noguchi, Yohei Hoshino

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.

由于农民老龄化问题，采收南瓜等重型水果是一项艰巨的工作。为解决这一问题，本研究旨在开发一种自动拾放机器人系统，以减轻南瓜收获时的劳动力需求。我们提出了一种能够检测田间南瓜并分别利用人工智能（AI）物体检测方法和 RGB-D 摄像头获取其三维（3D）坐标值的系统。该收获系统以履带式车辆为基础平台，采用协作机械臂提升南瓜果实。新设计的机械手安装在机械臂的末端，负责抓取南瓜。在本文中，我们使用了不同版本的 YOLO（从第 2 版到第 8 版）来检测南瓜果实，并比较了这些不同版本的检测结果。安装在机械臂根部的 RGB-D 摄像机以摄像机坐标捕捉南瓜果实的位置。我们提出的校准方法可以简单地将位置转换为机械臂的坐标。此外，我们还完成了南瓜果实拾放机器人系统的所有软件和硬件。我们在室外南瓜地进行了现场实验。实验表明，水果检测准确率超过 99%，摘取成功率超过 90%。但是，被过多藤蔓包围的果实无法成功抓取。

{"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, Tomoki Noguchi, 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}

引用次数: 0

Unmanned Aerial Vehicle-based Autonomous Tracking System for Invasive Flying Insects 基于无人飞行器的入侵飞虫自主追踪系统

IF 7.7 1区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Computers and Electronics in Agriculture

Pub Date : 2024-11-22 DOI: 10.1016/j.compag.2024.109616

Jeonghyeon Pak , Bosung Kim , Chanyoung Ju , Hyoung Il Son

The Asian hornet or yellow-legged hornet, Vespa velutina nigrithorax, is a global predator of honeybees (Apis mellifera 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.

亚洲大黄蜂或黄腿大黄蜂（Vespa velutina nigrithorax）是蜜蜂（Apis mellifera L.）的一种全球性天敌，由于气候变化迅速而变得广泛传播。在此，我们提出了一种定位系统，通过将无人驾驶飞行器与三坐标系统相结合，跟踪带有无线电标签的大黄蜂并发现大黄蜂蜂巢。利用大黄蜂的归巢本能，我们将实验系统地分为行为实验、地面实况实验和定位实验。根据实验结果，我们成功地发现了所测试的五只大黄蜂中的两只的蜂巢。此外，通过对实验结果的综合分析，我们对大黄蜂的飞行模式和行为有了更深入的了解。这项研究成果证明了将无人飞行器与无线电遥测技术整合在一起进行精确目标跟踪和生态系统管理的有效性，为减轻入侵物种对蜜蜂种群的影响提供了一种强有力的工具。

引用次数: 0

Advancing plant disease classification: A robust and generalized approach with transformer-fused convolution and Wasserstein domain adaptation 推进植物病害分类：利用变压器融合卷积和瓦瑟斯坦域自适应的稳健通用方法

IF 7.7 1区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Computers and Electronics in Agriculture

Pub Date : 2024-11-21 DOI: 10.1016/j.compag.2024.109574

Muhammad Hanif Tunio , Jian ping Li , Xiaoyang Zeng , Awais Ahmed , Syed Attique Shah , Hisam-Uddin Shaikh , Ghulam Ali Mallah , Imam Abdullahi Yahya

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.

植物病害对农业生产力和粮食安全构成重大威胁。由于缺乏田间环境数据集，目前在实验室控制数据集上训练的植物病害分类方法往往难以在真实世界环境中实现最佳性能。我们提出了一种新颖、稳健的无监督领域适应（UDA）框架，采用一种对抗学习方法和瓦瑟斯坦距离信息算法来学习领域不变特征表征，这种表征能够泛化更多不同的特征。这种方法通过最大限度地减少域之间的分布差异，将来自标记源域的洞察力融入到未标记的目标域中。最近，基于移动视觉转换器（MViT）的方法因其捕捉远距离特征依赖性的能力而被应用于 UDA。然而，这些方法忽略了一个事实，即 MViT 在提取局部特征细节方面缺乏有效性。所提出的框架结合了卷积神经网络（CNN）和 MViT 的优势，将 CNN 提取的局部特征与 MViT 捕捉的全局特征整合在一起。这种局部和全局表征的融合增强了域内的可转移性和特征可辨别性。此外，我们还采用了一种特征融合方法来调整通道维度并增强全局表征的局部细节。使用三个植物病害数据集进行的广泛实验证明了我们方法的有效性和效率，与最先进的方法（SOTA）和基线方法相比，我们的分类性能显著提高了 13.67%。我们的框架为稳健高效的植物病害分类提供了一个前景广阔的解决方案，为可持续农业和作物管理提供了有价值的见解。

{"title":"Advancing plant disease classification: A robust and generalized approach with transformer-fused convolution and Wasserstein domain adaptation","authors":"Muhammad Hanif Tunio , Jian ping Li , Xiaoyang Zeng , Awais Ahmed , Syed Attique Shah , Hisam-Uddin Shaikh , Ghulam Ali Mallah , 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}

引用次数: 0

Design and experimental analysis of real-time detection system for The seeding accuracy of rice pneumatic seed metering device based on the improved YOLOv5n 基于改进型 YOLOv5n 的水稻气动种子计量装置播种精度实时检测系统的设计与实验分析

IF 7.7 1区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Computers and Electronics in Agriculture

Pub Date : 2024-11-21 DOI: 10.1016/j.compag.2024.109614

He Xing , Yikai Wan , Peng Zhong , Junjiang Lin , Mingtao Huang , Ru Yang , Ying Zang

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.

水稻播种精度信息的获取可为水稻气动种子计量装置的运行状态和后期的田间管理提供充分支持。然而，由于水稻播种速度较快，且存在非单粒播种的情况，因此这项任务很难完成。为了在水稻气动种子计量装置运行过程中实现对播种精度的实时检测，设计了该装置播种精度的实时检测系统。本文详细介绍了该系统的主要组成部分和工作原理，并提出了一种基于改进型 YOLOv5n 的水稻种子精度检测算法。该算法利用 Faster-Net 神经网络，取代了作为原算法骨干的 CSPDarknet53 网络。此外，该算法还纳入了 CARAFE 算子，引入了 Soft-NMS-CIOU 技术（一种软性非最大抑制形式），并集成了 CBAM 注意机制模块。这些改进提高了模型对水稻种子图像的特征提取能力，从而能够在水稻气动种子计量装置内的黑暗环境中实时检测小粒水稻种子。这提高了识别小粒水稻种子图像的准确性，并降低了错误检测的概率。通过与不同算法的比较分析，测试结果表明，与其他算法相比，该算法具有更高的通过率和更快的响应时间。还进行了验证测试，以评估不同吸种板转速下的识别准确率。在吸种板转速为 10、20、30、40 和 50 r/min，吸种负压为 1.6 kPa 时，检测精度分别为 96%、96%、98.65%、88.8% 和 91%。根据实验结果，该算法符合播种检测的要求，可作为进一步研究水稻气动种子计量装置播种精度检测算法的基础。

{"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 , Yikai Wan , Peng Zhong , Junjiang Lin , Mingtao Huang , Ru Yang , 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}

引用次数: 0

Sustainable smart system for vegetables plant disease detection: Four vegetable case studies 用于检测蔬菜病害的可持续智能系统：四种蔬菜案例研究

IF 7.7 1区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Computers and Electronics in Agriculture

Pub Date : 2024-11-21 DOI: 10.1016/j.compag.2024.109672

Ahmed M. Ali , Adam Słowik , Ibrahim M. Hezam , Mohamed Abdel-Basset

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.

农业是国家经济的支柱。人们依靠农业获得粮食和出口创收。然而，农业面临着影响蔬菜数量和质量的各种病害。因此，提出一个检测蔬菜病害的模型非常重要。本研究提出了一种用于蔬菜病害检测和分类的可持续智能系统。该系统可检测番茄、马铃薯、莴苣和黄瓜等常见蔬菜的早期蔬菜病害。该研究采用了深度学习（DL）模型来检测和分类蔬菜病害。卷积神经网络（CNN）是一种用于图像分类的深度学习模型。本研究利用 CNN 及其他扩展（如 VGG16 和 MobileNet）进行植物图像分类。在番茄病害分类、马铃薯病害分类、莴苣病害分类和黄瓜病害分类的四个数据集上训练了三个 DL 模型。结果表明，三个模型在番茄病害数据集上的准确率为 84.49%，在黄瓜病害数据集上的准确率为 97.65%，在马铃薯病害数据集上的准确率为 97%，在莴苣病害数据集上的准确率为 99.9%。所提出的系统可以帮助农民在蔬菜病害蔓延之前及早发现病害，并通过提高产品的质量和数量来改善农业。

{"title":"Sustainable smart system for vegetables plant disease detection: Four vegetable case studies","authors":"Ahmed M. Ali , Adam Słowik , Ibrahim M. Hezam , 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}

引用次数: 0

A high-throughput method for monitoring growth of lettuce seedlings in greenhouses based on enhanced Mask2Former 基于增强型 Mask2Former 的高通量温室莴苣幼苗生长监测方法

IF 7.7 1区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Computers and Electronics in Agriculture

Pub Date : 2024-11-21 DOI: 10.1016/j.compag.2024.109681

Xiao Wei , Yue Zhao , Xianju Lu , Minggang Zhang , Jianjun Du , Xinyu Guo , Chunjiang Zhao

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 (R²) 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 R² above 0.99, while leaf compactness R² was 0.9698, and leaf count R² 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.

监测植物生长对栽培管理至关重要。农艺师可根据监测结果评估莴苣幼苗的健康状况，从而实施相关管理措施，提高莴苣幼苗的质量和产量。本研究开发了一种非破坏性的高通量生长监测方法，适用于大规模评估苗圃中莴苣秧苗的质量。该方法利用植物高通量表型平台获取 10 天时间序列图像数据。通过多维协同关注机制增强的 Mask2Former 网络模型，结合滑动窗口和形态学运算，以渐进的方式实现了对秧盘、品种和单株秧苗的精确识别和定位。在单株秧苗定位和分割结果的基础上，该方法估算了每个品种的出苗数量和出苗率，并进一步实现了单株秧苗叶片的实例分割和计数，创新性地构建了整个秧苗盘中不同品种的叶片分割结果。该方法应用于时间序列图像，自动监测了 1,086 个生菜品种的出苗变化和生长趋势。在监测这些品种时，该方法的出苗数估算决定系数 (R2) 达到 0.96。对所有六个关键表型参数的提取均显示出极高的相关性：投影面积、投影周长、凸壳面积和凸壳周长的 R2 均高于 0.99，而叶片紧密度的 R2 为 0.9698，叶片数的 R2 为 0.91。结果表明，这种高通量、可靠的方法能有效监测大规模生菜育苗的生长状况，为生菜育苗质量评估提供技术支持。

{"title":"A high-throughput method for monitoring growth of lettuce seedlings in greenhouses based on enhanced Mask2Former","authors":"Xiao Wei , Yue Zhao , Xianju Lu , Minggang Zhang , Jianjun Du , Xinyu Guo , 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>R<sup>2</sup></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>R<sup>2</sup></em> above 0.99, while leaf compactness <em>R<sup>2</sup></em> was 0.9698, and leaf count <em>R<sup>2</sup></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}

引用次数: 0

Rethinking lightweight sheep face recognition via network latency-accuracy tradeoff 通过网络延迟与准确性权衡反思轻量级羊脸识别

IF 7.7 1区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Computers and Electronics in Agriculture

Pub Date : 2024-11-19 DOI: 10.1016/j.compag.2024.109662

Xiaopeng Li, Yichi Zhang, Shuqin Li

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.

深度学习极大地提高了羊脸识别的性能，但现有的识别方法通常采用更深、更宽的网络来获得更好的性能，导致计算负担沉重、推理速度缓慢。本文提出了一种非常轻量级的羊脸识别网络，简称VLFaceNet，它实现了最先进的（SOTA）延迟-精度权衡。VLFaceNet 的基本模块是 VL，它使用廉价的线性运算来补充冗余特征，并在推理过程中通过结构重参数化来减小模型大小和计算复杂度，从而提高推理速度。VLDBlock 由 VL 和 ECA 信道关注串联而成，以提高信道级特征提取的有效性。VLFaceNet 由 VL 和 VLDBlock 叠加而成。通过融合 VLFaceNet 不同尺度的特征，可以识别不同尺度的羊脸，提高模型的识别性能。针对白色羊脸相似度高、难以区分的问题，本文提出了一种缩放特征增强方法 SFE，通过改变羊脸图像的颜色分布和纹理，提高羊脸图像之间的可区分性，从而提高 VLFaceNet 的识别性能。多个识别模型的识别性能提升证明了 SFE 的有效性。在自建数据集上，VLFaceNet 实现了最佳的延迟-准确性权衡，推理延迟为 2.58 ms，识别准确率为 97.75 %。这项研究有望推动基于深度学习的识别方法在家畜育种领域的应用。

{"title":"Rethinking lightweight sheep face recognition via network latency-accuracy tradeoff","authors":"Xiaopeng Li, Yichi Zhang, 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}

引用次数: 0

A review of the current status and common key technologies for agricultural field robots 农用田间机器人的现状和通用关键技术综述

IF 7.7 1区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Computers and Electronics in Agriculture

Pub Date : 2024-11-19 DOI: 10.1016/j.compag.2024.109630

Lei Liu , Fan Yang , Xiangyi Liu, Yuefeng Du, Xiaoyu Li, Guorun Li, Du Chen, Zhongxiang Zhu, Zhenghe Song

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.

工业革命 4.0 的到来，以及人工智能（AI）、云计算和物联网（IoT）的快速发展，引发了一场新的农业技术革命。田间机器人作为农业机器人的重要分支，在保障粮食安全、抢占全球农业制高点、实现农业可持续发展等方面具有不可替代的作用。在本研究中，我们首先明确了田间机器人的定义，并回顾了农业机器人的不同发展阶段。其次，我们从 Science Direct、Web of Science 等电子数据库中检索和筛选了 678 篇相关文献，并从发表年份、国家和关键词三个维度对所有文献进行了定量分析。然后，我们系统地介绍了犁地-播种、除草、监测、收割以及信息采集、作物保护和农业飞防机器人的研究现状。此外，全球田间机器人的发展呈现出快速增长和技术升级的趋势，我们提出了田间机器人的共性关键技术，包括智能感知、智能决策与控制、灵巧的手臂和手部操作、自主稳定行走、云-端融合智能操作等。最后，我们总结了机器人面临的挑战，如传感精度低、可操作性差、运行效率低等。因此，我们试图为野外机器人的未来发展提供新的机遇，如非结构化信息分析、5G、人工智能、数字双胞胎、机器人群协作等新技术，从而实现全自主感知的野外作业场景。

{"title":"A review of the current status and common key technologies for agricultural field robots","authors":"Lei Liu , Fan Yang , Xiangyi Liu, Yuefeng Du, Xiaoyu Li, Guorun Li, Du Chen, Zhongxiang Zhu, 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}

引用次数: 0