Pub Date : 2024-11-06DOI: 10.1016/j.compag.2024.109601
Nueraili Aierken , Bo Yang , Yongke Li , Pingan Jiang , Gang Pan , Shijian Li
Cotton is one of the world’s most economically significant crops. Evaluating and monitoring cotton crop growth play vital roles in precision agriculture. Unmanned aerial vehicle (UAV) based remote sensing, when integrated with machine learning technologies, exhibits considerable promise for crop growth management. Despite these technologies’ substantial impact on cotton production, there exists a scarcity of consolidated information regarding various methods used. This paper offers a comprehensive review and analysis focused on methods for monitoring and evaluating cotton growth using UAV-based imagery combined with machine learning techniques. We synthesize the existing research from the past decade within this context, particularly discussing data acquisition strategies, preprocessing methods necessary for handling UAV-acquired images effectively, and a range of machine learning models applied. This investigation offers a comprehensive outlook that could guide future research efforts towards more efficient and sustainable agricultural practices in cotton production, leveraging state-of-the-art technology.
{"title":"A review of unmanned aerial vehicle based remote sensing and machine learning for cotton crop growth monitoring","authors":"Nueraili Aierken , Bo Yang , Yongke Li , Pingan Jiang , Gang Pan , Shijian Li","doi":"10.1016/j.compag.2024.109601","DOIUrl":"10.1016/j.compag.2024.109601","url":null,"abstract":"<div><div>Cotton is one of the world’s most economically significant crops. Evaluating and monitoring cotton crop growth play vital roles in precision agriculture. Unmanned aerial vehicle (UAV) based remote sensing, when integrated with machine learning technologies, exhibits considerable promise for crop growth management. Despite these technologies’ substantial impact on cotton production, there exists a scarcity of consolidated information regarding various methods used. This paper offers a comprehensive review and analysis focused on methods for monitoring and evaluating cotton growth using UAV-based imagery combined with machine learning techniques. We synthesize the existing research from the past decade within this context, particularly discussing data acquisition strategies, preprocessing methods necessary for handling UAV-acquired images effectively, and a range of machine learning models applied. This investigation offers a comprehensive outlook that could guide future research efforts towards more efficient and sustainable agricultural practices in cotton production, leveraging state-of-the-art technology.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109601"},"PeriodicalIF":7.7,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142592561","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}
Pub Date : 2024-11-06DOI: 10.1016/j.compag.2024.109622
Changjoo Lee , Simon Schätzle , Stefan Andreas Lang , Timo Oksanen
Achieving safe and reliable environmental perception is crucial for the success of highly automated or even autonomous agricultural machinery. However, developing such a system is challenging due to the inherent limitations of perception sensors. In certain conditions, these sensors may fail to capture accurate data, leading to erroneous perceptions of the environment and potentially compromising safety. Monitoring the functional insufficiencies of the measurement data is crucial for ensuring the safety and reliability of perception systems.
This article introduces ISO standards, which provide guidelines for ensuring functional safety in highly automated mobile machines and vehicles. It also proposes an Image Quality Safety Model (IQSM) for monitoring the safety of the intended functionality in perception systems. The IQSM estimates the confidence level with which a camera can safely perform a specific object detection task. If the confidence level falls below a predefined threshold, the IQSM can trigger actions, alert operators, and prevent potential safety hazards. The IQSM exhibits remarkable performance, achieving a validation accuracy of about 90%, demonstrating its ability to effectively distinguish the safety of the intended functionality under a variety of image quality conditions.
实现安全可靠的环境感知对于高度自动化甚至自主农业机械的成功至关重要。然而，由于感知传感器固有的局限性，开发这样的系统极具挑战性。在某些条件下，这些传感器可能无法捕捉到准确的数据，从而导致对环境的错误感知，并可能危及安全。监测测量数据的功能缺陷对于确保感知系统的安全性和可靠性至关重要。本文介绍了 ISO 标准，这些标准为确保高度自动化的移动机器和车辆的功能安全提供了指导。文章还提出了一种图像质量安全模型（IQSM），用于监控感知系统中预期功能的安全性。IQSM 可估算摄像头安全执行特定物体检测任务的置信度。如果置信度低于预定阈值，IQSM 就会触发行动，提醒操作人员并防止潜在的安全隐患。IQSM 性能卓越，验证准确率达到约 90%，证明了其在各种图像质量条件下有效区分预期功能安全性的能力。
{"title":"Image quality safety model for the safety of the intended functionality in highly automated agricultural machines","authors":"Changjoo Lee , Simon Schätzle , Stefan Andreas Lang , Timo Oksanen","doi":"10.1016/j.compag.2024.109622","DOIUrl":"10.1016/j.compag.2024.109622","url":null,"abstract":"<div><div>Achieving safe and reliable environmental perception is crucial for the success of highly automated or even autonomous agricultural machinery. However, developing such a system is challenging due to the inherent limitations of perception sensors. In certain conditions, these sensors may fail to capture accurate data, leading to erroneous perceptions of the environment and potentially compromising safety. Monitoring the functional insufficiencies of the measurement data is crucial for ensuring the safety and reliability of perception systems.</div><div>This article introduces ISO standards, which provide guidelines for ensuring functional safety in highly automated mobile machines and vehicles. It also proposes an Image Quality Safety Model (IQSM) for monitoring the safety of the intended functionality in perception systems. The IQSM estimates the confidence level with which a camera can safely perform a specific object detection task. If the confidence level falls below a predefined threshold, the IQSM can trigger actions, alert operators, and prevent potential safety hazards. The IQSM exhibits remarkable performance, achieving a validation accuracy of about 90%, demonstrating its ability to effectively distinguish the safety of the intended functionality under a variety of image quality conditions.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109622"},"PeriodicalIF":7.7,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142592563","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}
Field-road trajectory classification is a crucial task for agricultural machinery behavior mode recognition, aiming to distinguish field operation mode and road driving mode automatically. However, the imbalanced distribution of agricultural machine trajectories brings challenges for the field-road trajectory classification task. Additionally, most existing field-road trajectory classification methods have certain shortcomings. For instance, they encounter difficulties in accurately representing the state of agricultural machinery movement using the current features. The data transformation process often leads to information loss, and the model’s generalization capabilities are limited. The performance of the models is constrained by each of these elements. To address these shortcomings, this paper introduces a general image classification model for agricultural machinery trajectory mode recognition named ATRNet. First, to address the issue of imbalanced field-road proportions in agricultural machinery trajectory data, a Conditional Tabular Generative Adversarial Network (CTGAN) is employed to generate quasi trajectories, balancing the distribution of positive and negative samples in the data. This step aims to eliminate biases during the model training process. Second, to accurately characterize the motion status of agricultural machinery, we propose a multiangle feature enhancement method to extract rich spatiotemporal features from trajectory data. Finally, different from conventional field-road trajectory classification models that primarily rely on spatial and temporal information for identifying trajectories, we present a lossless trajectory data representation paradigm. This paradigm maps each trajectory point into a “feature map” and uses an image classification model to capture latent feature representations of trajectory points for the recognition of different behavior modes of agricultural machinery. This paradigm can generalize image classification networks to the field-road trajectory classification task, providing a general vision model solution for agricultural machinery trajectory mode recognition. To validate the effectiveness of the ATRNet model, experiments were conducted on real corn and wheat harvester trajectory datasets. The results demonstrate that the proposed model achieves remarkable performance improvements over the state-of-the-art (SOTA) models. In the corn harvester trajectory dataset, ATRNet achieves an accuracy of 92.36% and an F1-score of 92.34%, surpassing existing SOTA models by 3.12% and 12.46%, respectively. Similarly, in the wheat harvester trajectory dataset, ATRNet achieves an accuracy of 92.36% and an F1-score of 92.33%, outperforming the existing optimal algorithm by 4.76% and 18.18%, respectively.
{"title":"A general image classification model for agricultural machinery trajectory mode recognition","authors":"Weixin Zhai , Zhi Xu , Jiawen Pan , Zhou Guo , Caicong Wu","doi":"10.1016/j.compag.2024.109629","DOIUrl":"10.1016/j.compag.2024.109629","url":null,"abstract":"<div><div>Field-road trajectory classification is a crucial task for agricultural machinery behavior mode recognition, aiming to distinguish field operation mode and road driving mode automatically. However, the imbalanced distribution of agricultural machine trajectories brings challenges for the field-road trajectory classification task. Additionally, most existing field-road trajectory classification methods have certain shortcomings. For instance, they encounter difficulties in accurately representing the state of agricultural machinery movement using the current features. The data transformation process often leads to information loss, and the model’s generalization capabilities are limited. The performance of the models is constrained by each of these elements. To address these shortcomings, this paper introduces a general image classification model for agricultural machinery trajectory mode recognition named ATRNet. First, to address the issue of imbalanced field-road proportions in agricultural machinery trajectory data, a Conditional Tabular Generative Adversarial Network (CTGAN) is employed to generate quasi trajectories, balancing the distribution of positive and negative samples in the data. This step aims to eliminate biases during the model training process. Second, to accurately characterize the motion status of agricultural machinery, we propose a multiangle feature enhancement method to extract rich spatiotemporal features from trajectory data. Finally, different from conventional field-road trajectory classification models that primarily rely on spatial and temporal information for identifying trajectories, we present a lossless trajectory data representation paradigm. This paradigm maps each trajectory point into a “feature map” and uses an image classification model to capture latent feature representations of trajectory points for the recognition of different behavior modes of agricultural machinery. This paradigm can generalize image classification networks to the field-road trajectory classification task, providing a general vision model solution for agricultural machinery trajectory mode recognition. To validate the effectiveness of the ATRNet model, experiments were conducted on real corn and wheat harvester trajectory datasets. The results demonstrate that the proposed model achieves remarkable performance improvements over the state-of-the-art (SOTA) models. In the corn harvester trajectory dataset, ATRNet achieves an accuracy of 92.36% and an F1-score of 92.34%, surpassing existing SOTA models by 3.12% and 12.46%, respectively. Similarly, in the wheat harvester trajectory dataset, ATRNet achieves an accuracy of 92.36% and an F1-score of 92.33%, outperforming the existing optimal algorithm by 4.76% and 18.18%, respectively.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109629"},"PeriodicalIF":7.7,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142592565","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}
Pub Date : 2024-11-06DOI: 10.1016/j.compag.2024.109599
J. Chelotti , H. Atashi , M. Ferrero , C. Grelet , H. Soyeurt , L. Giovanini , H.L. Rufiner , N. Gengler
Regular monitoring of body condition score (BCS) changes during lactation is an essential management tool in dairy cattle; however, the current BCS measurements are often discontinuous and unevenly spaced in time. The imputation of BCS values is useful for two main reasons: i) achieving completeness of data is necessary to be able to relate BCS to other traits (e.g. milk yield and milk composition) that have been routinely recorded at different times and with a different frequency, and ii) having expected BCS values provides the possibility to trigger early warnings for animals with certain unexpected conditions. The contribution of this study was to propose and evaluate potential methods useful to smooth and impute device-based BCS values recorded during lactation in dairy cattle. In total, 26,207 BCS records were collected from 3,038 cows (9,199 and 14,462 BCS records on 1,546 Holstein and 1,211 Montbéliarde cows respectively, and the rest corresponded to other minority cattle breeds). Six methods were evaluated to predict BCS values: the traditional methods of test interval method (TIM), and multiple-trait procedure (MTP), and the machine learning (ML) methods of multi-layer perceptron (MLP), Elman network (Elman), long-short term memories (LSTM) and bi-directional LSTM (BiLSTM). The performance of each method was evaluated by a hold-out validation approach using statistics of the root mean squared error (RMSE) and Pearson correlation (r). TIM, MTP, MLP, and BiLSTM were assessed for the imputation of intermediate missing values, while MTP, Elman, and LSTM were evaluated for the forecasting of future BCS values. Regarding the machine learning methods, BiLSTM demonstrated the best performance for the intermediate value imputation task (RMSE = 0.295, r = 0.845), while LSTM demonstrated the best performance for the future value forecasting task (RMSE = 0.356, r = 0.751). Among the methods evaluated, MTP showed the best performance for imputation of intermediate missing values in terms of RMSE (0.288) and r (0.856). MTP also achieved the best performance for forecasting of future BCS values in terms of RMSE (0.348) and r (0.760). This study demonstrates the ability of MTP and machine learning methods to impute missing BCS data and provides a cost-effective solution for the application area.
{"title":"Assessing traditional and machine learning methods to smooth and impute device-based body condition score throughout the lactation in dairy cows","authors":"J. Chelotti , H. Atashi , M. Ferrero , C. Grelet , H. Soyeurt , L. Giovanini , H.L. Rufiner , N. Gengler","doi":"10.1016/j.compag.2024.109599","DOIUrl":"10.1016/j.compag.2024.109599","url":null,"abstract":"<div><div>Regular monitoring of body condition score (<strong>BCS</strong>) changes during lactation is an essential management tool in dairy cattle; however, the current BCS measurements are often discontinuous and unevenly spaced in time. The imputation of BCS values is useful for two main reasons: i) achieving completeness of data is necessary to be able to relate BCS to other traits (e.g. milk yield and milk composition) that have been routinely recorded at different times and with a different frequency, and ii) having expected BCS values provides the possibility to trigger early warnings for animals with certain unexpected conditions. The contribution of this study was to propose and evaluate potential methods useful to smooth and impute device-based BCS values recorded during lactation in dairy cattle. In total, 26,207 BCS records were collected from 3,038 cows (9,199 and 14,462 BCS records on 1,546 Holstein and 1,211 Montbéliarde cows respectively, and the rest corresponded to other minority cattle breeds). Six methods were evaluated to predict BCS values: the traditional methods of test interval method (<strong>TIM</strong>), and multiple-trait procedure (<strong>MTP</strong>), and the machine learning (<strong>ML</strong>) methods of multi-layer perceptron (<strong>MLP</strong>), Elman network (<strong>Elman</strong>), long-short term memories (<strong>LSTM)</strong> and bi-directional LSTM (<strong>BiLSTM</strong>). The performance of each method was evaluated by a hold-out validation approach using statistics of the root mean squared error (<strong>RMSE</strong>) and Pearson correlation (r). TIM, MTP, MLP, and BiLSTM were assessed for the imputation of intermediate missing values, while MTP, Elman, and LSTM were evaluated for the forecasting of future BCS values. Regarding the machine learning methods, BiLSTM demonstrated the best performance for the intermediate value imputation task (RMSE = 0.295, r = 0.845), while LSTM demonstrated the best performance for the future value forecasting task (RMSE = 0.356, r = 0.751). Among the methods evaluated, MTP showed the best performance for imputation of intermediate missing values in terms of RMSE (0.288) and r (0.856). MTP also achieved the best performance for forecasting of future BCS values in terms of RMSE (0.348) and r (0.760). This study demonstrates the ability of MTP and machine learning methods to impute missing BCS data and provides a cost-effective solution for the application area.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109599"},"PeriodicalIF":7.7,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142592566","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}
Pub Date : 2024-11-06DOI: 10.1016/j.compag.2024.109583
Yibo Li , Yuxin Hou , Tao Cui , Danielle S Tan , Yang Xu , Dongxing Zhang , Mengmeng Qiao , Lijian Xiong
Accurately differentiating maize mixtures and assessing grain cleaning loss contributes to improving the efficiency and sustainability of agricultural systems. This study proposes a novel detection method integrating time–frequency images of particle vibro-piezoelectric signals and machine learning to classify grain and impurity and assess maize cleaning loss. Specifically, an indie-developed vibro-piezoelectric detection setup is employed to capture the time-domain response signals of grain and impurity for building a database of maize collision signals. Using the Short-Time Fourier Transform (STFT) and Weighted Average Algorithm (WAA), 1D time-domain signals characterizing only the time-varying properties are converted into 2D time–frequency images possessing rich spectral feature information and energy distribution. Subsequently, 15 texture features are extracted from 2D time–frequency images with the Grey-Level-Gradient Co-ccurrence Matrix (GLGCM). After eliminating weakly-correlated features, eleven texture features are chosen and consolidated within the first four Principal Components (PCs). These four PCs and the traditional 1D time-domain signals are pre-processed and input into the Naive Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF) classifiers. The NB model with Savitzky-Golay (SG) pre-processing utilizing 2D time–frequency image features exhibits the highest accuracy of 95.74%, surpassing the optimal 1D time-domain classification model by 5.31 percentage points. Bench tests verify that the piezoelectric detection unit with the optimal NB model can control the absolute error in grain loss rate to within 0.43%. Notably, the proposed method also applies to the classification and cleaning loss detection of other typical crops by replacing the collision signal database.
准确区分玉米混合物和评估谷物清洗损失有助于提高农业系统的效率和可持续性。本研究提出了一种新型检测方法，将颗粒振动压电信号的时频图像与机器学习相结合，对谷物和杂质进行分类，并评估玉米的清洁损失。具体而言，利用自主研发的振动压电检测装置捕捉谷物和杂质的时域响应信号，建立玉米碰撞信号数据库。利用短时傅里叶变换 (STFT) 和加权平均算法 (WAA)，将仅描述时变特性的一维时域信号转换为具有丰富频谱特征信息和能量分布的二维时频图像。随后，利用灰色-梯度共生矩阵（GLGCM）从二维时频图像中提取 15 个纹理特征。剔除弱相关特征后，选出 11 个纹理特征，并将其合并到前四个主成分（PC）中。这四个 PC 和传统的一维时域信号经过预处理后，输入到 Naive Bayes（NB）、支持向量机（SVM）、决策树（DT）和随机森林（RF）分类器中。利用二维时频图像特征进行萨维茨基-戈莱（SG）预处理的 NB 模型准确率最高，达到 95.74%，比最佳一维时域分类模型高出 5.31 个百分点。工作台测试证实，采用最佳 NB 模型的压电检测单元可将晶粒损耗率的绝对误差控制在 0.43% 以内。值得注意的是，通过替换碰撞信号数据库，所提出的方法也适用于其他典型作物的分类和清选损失检测。
{"title":"Classifying grain and impurity to assess maize cleaning loss using time–frequency images of vibro-piezoelectric signals coupling machine learning","authors":"Yibo Li , Yuxin Hou , Tao Cui , Danielle S Tan , Yang Xu , Dongxing Zhang , Mengmeng Qiao , Lijian Xiong","doi":"10.1016/j.compag.2024.109583","DOIUrl":"10.1016/j.compag.2024.109583","url":null,"abstract":"<div><div>Accurately differentiating maize mixtures and assessing grain cleaning loss contributes to improving the efficiency and sustainability of agricultural systems. This study proposes a novel detection method integrating time–frequency images of particle vibro-piezoelectric signals and machine learning to classify grain and impurity and assess maize cleaning loss. Specifically, an indie-developed vibro-piezoelectric detection setup is employed to capture the time-domain response signals of grain and impurity for building a database of maize collision signals. Using the Short-Time Fourier Transform (STFT) and Weighted Average Algorithm (WAA), 1D time-domain signals characterizing only the time-varying properties are converted into 2D time–frequency images possessing rich spectral feature information and energy distribution. Subsequently, 15 texture features are extracted from 2D time–frequency images with the Grey-Level-Gradient Co-ccurrence Matrix (GLGCM). After eliminating weakly-correlated features, eleven texture features are chosen and consolidated within the first four Principal Components (PCs). These four PCs and the traditional 1D time-domain signals are pre-processed and input into the Naive Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF) classifiers. The NB model with Savitzky-Golay (SG) pre-processing utilizing 2D time–frequency image features exhibits the highest accuracy of 95.74%, surpassing the optimal 1D time-domain classification model by 5.31 percentage points. Bench tests verify that the piezoelectric detection unit with the optimal NB model can control the absolute error in grain loss rate to within 0.43%. Notably, the proposed method also applies to the classification and cleaning loss detection of other typical crops by replacing the collision signal database.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109583"},"PeriodicalIF":7.7,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593816","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}
Pub Date : 2024-11-06DOI: 10.1016/j.compag.2024.109609
Jiaying Fu, Da Liu, Yingchao He, Fang Cheng
In sea-based fish farms, biofouling and net damage are unavoidable challenges. To ensure safe, reliable, and sustainable fish production, timely monitoring of nets is crucial for detecting biofouling and net damage, along with providing decision support for subsequent maintenance and cleaning. In recent years, technological advancements have driven the automation of production processes, with a growing trend toward using robots instead of human labor for net operations in sea-based fish farms. However, there is a lack of a systematic review of autonomous net inspection and cleaning. This paper addresses this gap by reviewing and analyzing the current state of autonomous net inspection and cleaning in sea-based fish farms. Key technologies, including robot control, net inspection, and net cleaning, are summarized, along with their future development in practical applications. This paper also emphasizes Industry 4.0 technologies that support these advancements, such as sensors, robotics, artificial intelligence (AI), the Internet of Things (IoT), big data analytics, and the digital twin (DT). Furthermore, advanced robotic solutions currently used for autonomous net inspection and cleaning, as well as their potential benefits and drawbacks, are presented. Finally, the challenges and future research directions are highlighted, offering valuable insights for institutions and companies working to enhance the autonomy and intelligence of net operations in sea-based fish farms.
{"title":"Autonomous net inspection and cleaning in sea-based fish farms: A review","authors":"Jiaying Fu, Da Liu, Yingchao He, Fang Cheng","doi":"10.1016/j.compag.2024.109609","DOIUrl":"10.1016/j.compag.2024.109609","url":null,"abstract":"<div><div>In sea-based fish farms, biofouling and net damage are unavoidable challenges. To ensure safe, reliable, and sustainable fish production, timely monitoring of nets is crucial for detecting biofouling and net damage, along with providing decision support for subsequent maintenance and cleaning. In recent years, technological advancements have driven the automation of production processes, with a growing trend toward using robots instead of human labor for net operations in sea-based fish farms. However, there is a lack of a systematic review of autonomous net inspection and cleaning. This paper addresses this gap by reviewing and analyzing the current state of autonomous net inspection and cleaning in sea-based fish farms. Key technologies, including robot control, net inspection, and net cleaning, are summarized, along with their future development in practical applications. This paper also emphasizes Industry 4.0 technologies that support these advancements, such as sensors, robotics, artificial intelligence (AI), the Internet of Things (IoT), big data analytics, and the digital twin (DT). Furthermore, advanced robotic solutions currently used for autonomous net inspection and cleaning, as well as their potential benefits and drawbacks, are presented. Finally, the challenges and future research directions are highlighted, offering valuable insights for institutions and companies working to enhance the autonomy and intelligence of net operations in sea-based fish farms.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109609"},"PeriodicalIF":7.7,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142592560","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}
Pub Date : 2024-11-05DOI: 10.1016/j.compag.2024.109612
Zilong Wang , Ling Yang , Ruoxi Wang , Lian Lei , Hao Ding , Qiliang Yang
Panax notoginseng plays an important role in traditional Chinese medicine. However, diseases pose a significant threat to the quality and yield of P. notoginseng. The main challenge related to the identification of P. notoginseng leaf diseases is how to achieve good performance in the case of small diseased spots on P. notoginseng leaf, overlapping edges of diseased leaf and the difficulty in mobile deployment. A lightweight semantic segmentation model Window Efficient-DeepLabv3+ is proposed for segmentation and quantification of P. notoginseng leaf diseases. We propose the Window Attention-ASPP module and present a hierarchical stacking of features, which improves model accuracy for minor target lesions while reducing parameters. In addition, a lightweight backbone network MobileNetV2 is utilized as a feature extraction module. The decoding stage introduces the Efficient Channel Attention module, which effectively improves the accuracy of the segmentation of the blade contour. Experimental results yielded the Mean Intersection Over Union, Mean Precision, and Mean Recall metrics of WE-DeepLabV3+ network to be 82.0 %, 87.6 %, and 92.4 % respectively, outperforming other segmentation models such as UNet, PSPNet, CaraNet, SegNet, and BiSeNetV2. Moreover, the number of parameters has been reduced by 90.6 % with only 5.1 M parameters. Finally, the method is used to quantify the disease of P. notoginseng leaf, the error is only 1.15 % and 0.82 %, which proves that it can quantify the disease severity accurately. Thus, the proposed method holds great significance for raising the yield and quality of P. notoginseng, also providing reliable guidance for precise fertilization and drug control.
{"title":"WE-DeepLabV3+: A lightweight segmentation model for Panax notoginseng leaf diseases","authors":"Zilong Wang , Ling Yang , Ruoxi Wang , Lian Lei , Hao Ding , Qiliang Yang","doi":"10.1016/j.compag.2024.109612","DOIUrl":"10.1016/j.compag.2024.109612","url":null,"abstract":"<div><div><em>Panax notoginseng</em> plays an important role in traditional Chinese medicine. However, diseases pose a significant threat to the quality and yield of <em>P. notoginseng</em>. The main challenge related to the identification of <em>P. notoginseng</em> leaf diseases is how to achieve good performance in the case of small diseased spots on <em>P. notoginseng</em> leaf, overlapping edges of diseased leaf and the difficulty in mobile deployment. A lightweight semantic segmentation model Window Efficient-DeepLabv3+ is proposed for segmentation and quantification of <em>P. notoginseng</em> leaf diseases. We propose the Window Attention-ASPP module and present a hierarchical stacking of features, which improves model accuracy for minor target lesions while reducing parameters. In addition, a lightweight backbone network MobileNetV2 is utilized as a feature extraction module. The decoding stage introduces the Efficient Channel Attention module, which effectively improves the accuracy of the segmentation of the blade contour. Experimental results yielded the Mean Intersection Over Union, Mean Precision, and Mean Recall metrics of WE-DeepLabV3+ network to be 82.0 %, 87.6 %, and 92.4 % respectively, outperforming other segmentation models such as UNet, PSPNet, CaraNet, SegNet, and BiSeNetV2. Moreover, the number of parameters has been reduced by 90.6 % with only 5.1 M parameters. Finally, the method is used to quantify the disease of <em>P. notoginseng</em> leaf, the error is only 1.15 % and 0.82 %, which proves that it can quantify the disease severity accurately. Thus, the proposed method holds great significance for raising the yield and quality of <em>P. notoginseng</em>, also providing reliable guidance for precise fertilization and drug control.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109612"},"PeriodicalIF":7.7,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587287","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}
Agricultural data have great potential to improve decision-making, enhance operational efficiency, and drive innovation. Despite the growing acknowledgment of their value, there remains a gap in understanding how data value creation is perceived and implemented in agriculture. This study addresses this gap by investigating data value creation mechanisms, targets, and impacts through a structured literature review of 80 articles, including 13 core articles retrieved via targeted database searches and 67 additional articles identified through cross-reference snowballing. Key “value creation mechanisms” are categorized as transparency and access, discovery and experimentation, prediction and optimization, customization and targeting, learning and crowdsourcing, and monitoring and adaptation. The value creation mechanisms aim to enhance key “targets”, namely organizational performance, business process improvement, product and service innovation, and consumer and market experience. Organization performance was the most frequently addressed value target, appearing in approximately 85% of the core articles, followed by business process improvement, highlighted in approximately 77% of the articles. Together, the mechanisms and targets create “impact”, constructing the value of data. The findings reveal that all core articles (100%) emphasize the functional value of agricultural data, while 54% also explore their symbolic value, which enhances reputation and market positioning. A key takeaway is that, unlike many other assets, the value of agricultural data increases with reuse, which calls for a shift in focus from data ownership to ownership of the value derived from them. This study highlights the need for robust frameworks to fully realize the potential of agricultural data and calls for future research to further characterize and assess this value. These insights are essential for developing tools and methodologies that enhance productivity, sustainability, and profitability in agriculture.
{"title":"Data value creation in agriculture: A review","authors":"Havva Uyar , Ioannis Karvelas , Stamatia Rizou , Spyros Fountas","doi":"10.1016/j.compag.2024.109602","DOIUrl":"10.1016/j.compag.2024.109602","url":null,"abstract":"<div><div>Agricultural data have great potential to improve decision-making, enhance operational efficiency, and drive innovation. Despite the growing acknowledgment of their value, there remains a gap in understanding how data value creation is perceived and implemented in agriculture. This study addresses this gap by investigating data value creation mechanisms, targets, and impacts through a structured literature review of 80 articles, including 13 core articles retrieved via targeted database searches and 67 additional articles identified through cross-reference snowballing. Key “value creation mechanisms” are categorized as transparency and access, discovery and experimentation, prediction and optimization, customization and targeting, learning and crowdsourcing, and monitoring and adaptation. The value creation mechanisms aim to enhance key “targets”, namely organizational performance, business process improvement, product and service innovation, and consumer and market experience. Organization performance was the most frequently addressed value target, appearing in approximately 85% of the core articles, followed by business process improvement, highlighted in approximately 77% of the articles. Together, the mechanisms and targets create “impact”, constructing the value of data. The findings reveal that all core articles (100%) emphasize the functional value of agricultural data, while 54% also explore their symbolic value, which enhances reputation and market positioning. A key takeaway is that, unlike many other assets, the value of agricultural data increases with reuse, which calls for a shift in focus from data ownership to ownership of the value derived from them. This study highlights the need for robust frameworks to fully realize the potential of agricultural data and calls for future research to further characterize and assess this value. These insights are essential for developing tools and methodologies that enhance productivity, sustainability, and profitability in agriculture.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109602"},"PeriodicalIF":7.7,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587288","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}
Pub Date : 2024-11-05DOI: 10.1016/j.compag.2024.109554
Xiaojuan Li , Bo Liu , Yinggang Shi , Mingming Xiong , Dongyu Ren , Letian Wu , Xiangjun Zou
The three-dimensional reconstruction of fruit trees plays a crucial role in assessing their growth status, analyzing agronomic traits, and categorizing their organs. This is vital for implementing intelligent orchard management. This study aims to develop a cost-effective and efficient method for the three-dimensional reconstruction and skeleton extraction of fruit trees. The proposed method leverages the 3D geometric structure captured by Time-of-Flight (TOF) sensors and addresses common issues such as occlusion and perspective ambiguity. Firstly, the TOF sensor and its supporting components are used to build an acquisition platform to collect the full range point cloud of fruit trees in the key growth period. The noise information is filtered through the point cloud preprocessing operation to obtain the complete target point cloud and extract its structural invariant features. The IWOA-RANSAC-NDT algorithm is introduced for 3D model registration. Secondly, the Delaunay triangulation algorithm and Dijkstra shortest path algorithm are used to calculate the Minimum Spanning Tree. Branch segmentation is expedited using the Kd-tree data structure. The Levenberg Marquardt algorithm and the cylindrical fitting method are used to obtain the full fruit tree skeleton model. Finally, taking walnut tree as the experimental object, a high-precision fruit tree point cloud model is constructed, and the actual verification is carried out based on the measured data. Findings indicate that the proposed methodology can accurately construct both 3D point cloud and skeleton models of fruit trees with accuracy deviations from the measured data remaining within 7 %. The proposed method offers valuable data and technical support for the future development of highly autonomous, practical, and user-oriented fruit tree pruning systems.
{"title":"Efficient three-dimensional reconstruction and skeleton extraction for intelligent pruning of fruit trees","authors":"Xiaojuan Li , Bo Liu , Yinggang Shi , Mingming Xiong , Dongyu Ren , Letian Wu , Xiangjun Zou","doi":"10.1016/j.compag.2024.109554","DOIUrl":"10.1016/j.compag.2024.109554","url":null,"abstract":"<div><div>The three-dimensional reconstruction of fruit trees plays a crucial role in assessing their growth status, analyzing agronomic traits, and categorizing their organs. This is vital for implementing intelligent orchard management. This study aims to develop a cost-effective and efficient method for the three-dimensional reconstruction and skeleton extraction of fruit trees. The proposed method leverages the 3D geometric structure captured by Time-of-Flight (TOF) sensors and addresses common issues such as occlusion and perspective ambiguity. Firstly, the TOF sensor and its supporting components are used to build an acquisition platform to collect the full range point cloud of fruit trees in the key growth period. The noise information is filtered through the point cloud preprocessing operation to obtain the complete target point cloud and extract its structural invariant features. The IWOA-RANSAC-NDT algorithm is introduced for 3D model registration. Secondly, the Delaunay triangulation algorithm and Dijkstra shortest path algorithm are used to calculate the Minimum Spanning Tree. Branch segmentation is expedited using the Kd-tree data structure. The Levenberg Marquardt algorithm and the cylindrical fitting method are used to obtain the full fruit tree skeleton model. Finally, taking walnut tree as the experimental object, a high-precision fruit tree point cloud model is constructed, and the actual verification is carried out based on the measured data. Findings indicate that the proposed methodology can accurately construct both 3D point cloud and skeleton models of fruit trees with accuracy deviations from the measured data remaining within 7 %. The proposed method offers valuable data and technical support for the future development of highly autonomous, practical, and user-oriented fruit tree pruning systems.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109554"},"PeriodicalIF":7.7,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587286","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}
Pub Date : 2024-11-05DOI: 10.1016/j.compag.2024.109547
Isaya Kisekka , Srinivasa Rao Peddinti , Peter Savchik , Liyuan Yang , Mae Culumber , Khalid Bali , Luke Milliron , Erica Edwards , Mallika Nocco , Clarissa A. Reyes , Robert J. Mahoney , Kenneth Shackel , Allan Fulton
In the face of climate change, optimization of almond irrigation management is critical for ensuring the long-term sustainability of nut production and water resources. To achieve optimal irrigation management, continuous monitoring of the plant water status is critical in scheduling irrigation. It is a widely accepted practice to use stem water potential (SWP) as a measure of plant water status in woody perennials like almonds. However, the pressure chamber (PC) commonly used to make these measurements is labor-intensive and does not provide continuous data without significant additional labor. In this study, we evaluated two recently developed stem water potential sensors (Microtensiometer [MT], and Osmotic Cell [OC]), both of which can measure the SWP nearly continuously when embedded in stem sapwood tissue (typically in the trunk or branch of a tree). SWP sensors were evaluated in nine commercial almond orchards in the Central Valley of California. The SWP values obtained from both sensors were compared to the values measured using a PC using statistical software called FITEVAL. Overall, sensor performance varied from good to acceptable and from acceptable to unacceptable for MT and OC sensors respectively. The MT sensors demonstrated higher accuracy with a Nash-Sutcliff Coefficient of Efficiency (NSE) of 0.84 (95 % CI: 0.78–0.88) and a Root Mean Square Error (RMSE) of −0.24 MPa (95 % CI: −0.21 to −0.28 MPa), while the OC sensor had an NSE of 0.68 (95 % CI: 0.61–0.74) and an RMSE of −0.32 MPa (95 % CI: −0.29 to −0.35 MPa). MT sensors exhibited the added advantage of providing sub-hourly data and displaying tree recovery from water stress following irrigation, positioning them as potentially superior for precision almond orchard water management. If widely adopted, SWP sensors have the potential to optimize water use in almond production.
{"title":"Multisite evaluation of microtensiometer and osmotic cell stem water potential sensors in almond orchards","authors":"Isaya Kisekka , Srinivasa Rao Peddinti , Peter Savchik , Liyuan Yang , Mae Culumber , Khalid Bali , Luke Milliron , Erica Edwards , Mallika Nocco , Clarissa A. Reyes , Robert J. Mahoney , Kenneth Shackel , Allan Fulton","doi":"10.1016/j.compag.2024.109547","DOIUrl":"10.1016/j.compag.2024.109547","url":null,"abstract":"<div><div>In the face of climate change, optimization of almond irrigation management is critical for ensuring the long-term sustainability of nut production and water resources. To achieve optimal irrigation management, continuous monitoring of the plant water status is critical in scheduling irrigation. It is a widely accepted practice to use stem water potential (SWP) as a measure of plant water status in woody perennials like almonds. However, the pressure chamber (PC) commonly used to make these measurements is labor-intensive and does not provide continuous data without significant additional labor. In this study, we evaluated two recently developed stem water potential sensors (Microtensiometer [MT], and Osmotic Cell [OC]), both of which can measure the SWP nearly continuously when embedded in stem sapwood tissue (typically in the trunk or branch of a tree). SWP sensors were evaluated in nine commercial almond orchards in the Central Valley of California. The SWP values obtained from both sensors were compared to the values measured using a PC using statistical software called FITEVAL. Overall, sensor performance varied from good to acceptable and from acceptable to unacceptable for MT and OC sensors respectively. The MT sensors demonstrated higher accuracy with a Nash-Sutcliff Coefficient of Efficiency (NSE) of 0.84 (95 % CI: 0.78–0.88) and a Root Mean Square Error (RMSE) of −0.24 MPa (95 % CI: −0.21 to −0.28 MPa), while the OC sensor had an NSE of 0.68 (95 % CI: 0.61–0.74) and an RMSE of −0.32 MPa (95 % CI: −0.29 to −0.35 MPa). MT sensors exhibited the added advantage of providing sub-hourly data and displaying tree recovery from water stress following irrigation, positioning them as potentially superior for precision almond orchard water management. If widely adopted, SWP sensors have the potential to optimize water use in almond production.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109547"},"PeriodicalIF":7.7,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593815","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}
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