Information Processing in Agriculture最新文献_第2页

Fuzzy PID control system optimization and verification for oxygen-supplying management in live fish waterless transportation 活鱼无水运输供氧管理的模糊PID控制系统优化与验证

IF 7.7

Information Processing in Agriculture Pub Date : 2024-12-01 DOI: 10.1016/j.inpa.2023.06.001

Yongjun Zhang , Xinqing Xiao

{"title":"Fuzzy PID control system optimization and verification for oxygen-supplying management in live fish waterless transportation","authors":"Yongjun Zhang , Xinqing Xiao","doi":"10.1016/j.inpa.2023.06.001","DOIUrl":"10.1016/j.inpa.2023.06.001","url":null,"abstract":"<div><div>Live fish waterless transportation could be recognized as an essential supplement for water-based transportation due to its low oxygen consumption and less waste water pollution. The critical problem to maintaining the fish survival quality under such a unique transport strategy is accurately controlling the oxygen concentration in the container to be constantly at stable and high levels. This paper aims to propose an improved fuzzy PID control system based on the grey model with residual rectification by improved particle swarm optimized Gated Recurrent Unit (GM-IPSO-GRU) to realize advanced oxygen level control. In addition, it is also reinforced by adopting the improved grey wolf optimization (IGWO) for the majorization of control parameters (quantization factors, scale factors) with full consideration of fish size features. In this study, Turbot (Scophthalmus maximus) is taken as the test subject to verify the integrated control performance of the optimized fuzzy PID controller through simulated waterless live transportation under low-temperature conditions. The proposed control system is validated as more efficient than the traditional proportional integral derivative (PID) and fuzzy PID algorithms for handling its nonlinear, time-varying, and time lag problems well. In summary, the control group experiment shows that the newly-designed control system has the advantages of shorter stabilization time, minor overshoot, and strong anti-interference ability for oxygen level adjustment. Finally, applying this novel control technology can effectively improve oxygen adjustment efficiency and provide feasible quality control support for the deep optimization of the live fish circulation industry.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"11 4","pages":"Pages 421-437"},"PeriodicalIF":7.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42570678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Model-based quantitative analysis in two-time-scale decomposed on–off optimal control of greenhouse cultivation 基于模型的温室栽培双时间尺度分解开关最优控制定量分析

IF 7.7

Information Processing in Agriculture Pub Date : 2024-12-01 DOI: 10.1016/j.inpa.2023.08.001

Dan Xu , Yanfeng Li , Anguo Dai , Shumei Zhao , Weitang Song

{"title":"Model-based quantitative analysis in two-time-scale decomposed on–off optimal control of greenhouse cultivation","authors":"Dan Xu , Yanfeng Li , Anguo Dai , Shumei Zhao , Weitang Song","doi":"10.1016/j.inpa.2023.08.001","DOIUrl":"10.1016/j.inpa.2023.08.001","url":null,"abstract":"<div><div>Greenhouse climate is crucial for crop growth. Traditional climate control techniques are carried out through on–off actuators based on growers’ experience. Advanced control algorithms usually track setpoints through continuous control inputs. These setpoints cannot guarantee maximum profit, which can be treated as the control objective of the optimal control algorithm. This paper investigated on–off optimal control algorithms based on two-time-scale decomposition. Mixed-integer nonlinear dynamic programming is used in the fast subproblem to quantify the influence of restricting different control inputs to be integers on the control objective and the CPU time. Results show that compared with continuous control inputs, a decrease of 2.21 ¥·m<sup>−2</sup> in the control objective and an increase of 7.84·10<sup>3</sup> s in the CPU time can be found when defining all control inputs to be integers with 12 collocation points in one day. The methods of sorting and pulse width modulation are used to simulate the receding horizon optimal control in the whole growing period. Results show that compared with continuous control inputs, decreases of 83.54 ¥·m<sup>−2</sup> and 4.45 ¥·m<sup>−2</sup> can be found with the methods of sorting and pulse width modulation. Moreover, the method of pulse width modulation cannot guarantee state constraint satisfaction. This paper suggests modifying actuators to supply continuous control inputs before implementing optimal control algorithms for maximum profit.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"11 4","pages":"Pages 488-498"},"PeriodicalIF":7.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48146231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Attention-based generative adversarial networks for aquaponics environment time series data imputation 基于注意力的鱼菜共生环境时间序列数据输入生成对抗网络

IF 7.7

Information Processing in Agriculture Pub Date : 2024-12-01 DOI: 10.1016/j.inpa.2023.10.001

Keyang Zhong , Xueqian Sun , Gedi Liu , Yifeng Jiang , Yi Ouyang , Yang Wang

{"title":"Attention-based generative adversarial networks for aquaponics environment time series data imputation","authors":"Keyang Zhong , Xueqian Sun , Gedi Liu , Yifeng Jiang , Yi Ouyang , Yang Wang","doi":"10.1016/j.inpa.2023.10.001","DOIUrl":"10.1016/j.inpa.2023.10.001","url":null,"abstract":"<div><div>Environmental parameter data collected by sensors for monitoring the environment of agricultural facility operations are usually incomplete due to external environmental disturbances and device failures. And the missing of collected data is completely at random. In practice, missing data could create biased estimations and make multivariate time series predictions of environmental parameters difficult, leading to imprecise environmental control. A multivariate time series imputation model based on generative adversarial networks and multi-head attention (ATTN-GAN) is proposed in this work to reducing the negative consequence of missing data. ATTN-GAN can capture the temporal and spatial correlation of time series, and has a good capacity to learn data distribution. In the downstream experiments, we used ATTN-GAN and baseline models for data imputation, and predicted the imputed data, respectively. For the imputation of missing data, over the 20%, 50% and 80% missing rate, ATTN-GAN had the lowest RMSE, 0.1593, 0.2012 and 0.2688 respectively. For water temperature prediction, data processed with ATTN-GAN over MLP, LSTM, DA-RNN prediction methods had the lowest MSE, 0.6816, 0.8375 and 0.3736 respectively. Those results revealed that ATTN-GAN outperformed all baseline models in terms of data imputation accuracy. The data processed by ATTN-GAN is the best for time series prediction.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"11 4","pages":"Pages 542-551"},"PeriodicalIF":7.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136009786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IoT based Agriculture (Ag-IoT): A detailed study on Architecture, Security and Forensics 基于物联网的农业（Ag-IoT）：架构、安全和取证的详细研究

IF 7.7

Information Processing in Agriculture Pub Date : 2024-12-01 DOI: 10.1016/j.inpa.2023.09.002

Santoshi Rudrakar, Parag Rughani

{"title":"IoT based Agriculture (Ag-IoT): A detailed study on Architecture, Security and Forensics","authors":"Santoshi Rudrakar, Parag Rughani","doi":"10.1016/j.inpa.2023.09.002","DOIUrl":"10.1016/j.inpa.2023.09.002","url":null,"abstract":"<div><div>IoT based agriculture (Ag-IoT) is an emerging communication technology that is widely adopted by agricultural entrepreneurs and farmers to perform agricultural agro-chores in the farm to improve productivity, for better monitoring, and to reduce labor costs. However, the use of the Internet in Ag-IoT facilitates real-time functionality in an agriculture system, it can increase the risk of security breaches and cyber attacks that would cause the Ag-IoT system to malfunction and can affect its productivity. Ag-IoT is overlooked in cyber security parameters, which can have severe impacts on its trustworthiness and adoption by agricultural communities. To address this gap, this article presents a systematic study of the literature published between 2001 and 2023 that discusses advances in Ag-IoT technology. The subjects included in the study on Ag-IoT are emerging applications, different IoT architectures, suspected cyber attacks and cyber crimes, and challenges in incident response and digital forensics. The findings of this study encourage the reader to explore future potential research avenues related to the security risks and challenges of Ag-IoT, as well as the readiness for incident response and forensic investigation in the smart agricultural sector. The main conclusion of this study is that security must be ensured in Ag-IoT environments to offer uninterrupted services and also there is a need for forensic readiness for effective investigation in the event of unanticipated security incidents.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"11 4","pages":"Pages 524-541"},"PeriodicalIF":7.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42340537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluation of the applicability of a metal oxide semiconductor gas sensor for methane emissions from agriculture 金属氧化物半导体气体传感器对农业甲烷排放的适用性评估

IF 7.7

Information Processing in Agriculture Pub Date : 2024-12-01 DOI: 10.1016/j.inpa.2023.11.001

Bastiaan Molleman , Enrico Alessi , Fabio Passaniti , Karen Daly

{"title":"Evaluation of the applicability of a metal oxide semiconductor gas sensor for methane emissions from agriculture","authors":"Bastiaan Molleman , Enrico Alessi , Fabio Passaniti , Karen Daly","doi":"10.1016/j.inpa.2023.11.001","DOIUrl":"10.1016/j.inpa.2023.11.001","url":null,"abstract":"<div><div>This work investigated the potential of metal oxide semiconductor (MOS) gas sensors for environmental monitoring of methane. Calibrations were performed under controlled conditions in the lab, and under semi-controlled conditions in the field, using a modified head space chamber set-up. Concentrations up to ±300 ppm methane were tested. The relationship between sensor conductance and methane concentrations could be very well described using principles from adsorption theory. The adjustable parameters were background conductance G<sub>0</sub>, a sensitivity constant S and a non-ideality coefficient n, where n has a non-rational value between 0 and 1. Sensor behaviour was very different in dry air than in humid air, with the background conductance increasing approximately tenfold and sensitivity decreasing between 20 fold and 80 fold, while the non-ideality coefficient increased from ±0.4 to ±0.6. Nevertheless, at high methane concentrations comparable conductance values were recorded in dry and humid air. The standard deviation of predicted values was 1.6 μS.for the least well described dataset. Using the corresponding calibration curve, a detection limit of 11 ppm is calculated for humid ambient air. This values suggests that MOS sensor are adequately sensitive to be used for methane detection in an agricultural context.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"11 4","pages":"Pages 573-580"},"PeriodicalIF":7.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135454758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Artificial intelligence solutions to reduce information asymmetry for Colombian cocoa small-scale farmers 减少哥伦比亚可可小农户信息不对称的人工智能解决方案

IF 7.7

Information Processing in Agriculture Pub Date : 2024-09-01 DOI: 10.1016/j.inpa.2023.03.001

{"title":"Artificial intelligence solutions to reduce information asymmetry for Colombian cocoa small-scale farmers","authors":"","doi":"10.1016/j.inpa.2023.03.001","DOIUrl":"10.1016/j.inpa.2023.03.001","url":null,"abstract":"<div><p>The lack of information creates problems for Colombian small-scale farmers, as it impedes them from selling at fair prices and knowing efficient production techniques. Around the world, many technological interventions have proven helpful in reducing information asymmetries. Therefore, we proposed a technological scheme based on a genetic algorithm and a natural language processor (NLP) that enables producers to obtain knowledge through information processing. Also, we ran fieldwork in twenty municipalities and a survey among 500 Colombian cocoa small-scale farmers in different regions in Colombia. This fieldwork helps us determine small-scale farmers' necessities, market conditions, and the relevance of an Artificial Intelligence (AI) tool. The results have shown that AI methodologies could improve the economic conditions of small farmers by providing access to information on prices, weather, and production techniques. The fieldwork evidence that a technological tool is a good option only if there are dynamic trade cycles. AI tools could transmit and process information to become producers' knowledge and help them evolve into collective strategies. The methodology, which combines genetic algorithms, NLP, and fieldwork for cocoa farming, is a novelty that contributes to information asymmetry reduction. We contributed to the literature about adopting AI tools to develop cocoa small-scale farming better.</p></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"11 3","pages":"Pages 310-324"},"PeriodicalIF":7.7,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2214317323000458/pdfft?md5=fc59c81b0d445fce4bff213f690d8056&pid=1-s2.0-S2214317323000458-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41628896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Detection and counting method of juvenile abalones based on improved SSD network 基于改进SSD网络的鲍鱼幼鱼检测计数方法

IF 7.7

Information Processing in Agriculture Pub Date : 2024-09-01 DOI: 10.1016/j.inpa.2023.03.002

{"title":"Detection and counting method of juvenile abalones based on improved SSD network","authors":"","doi":"10.1016/j.inpa.2023.03.002","DOIUrl":"10.1016/j.inpa.2023.03.002","url":null,"abstract":"<div><p>Detection and counting of abalones is one of key technologies of abalones breeding density estimation. The abalones in the breeding stage are small in size, densely distributed, and occluded between individuals, so the existing object detection algorithms have low precision for detecting the abalones in the breeding stage. To solve this problem, a detection and counting method of juvenile abalones based on improved SSD network is proposed in this research. The innovation points of this method are: Firstly, the multi-layer feature dynamic fusion method is proposed to obtain more color and texture information and improve detection precision of juvenile abalones with small size; secondly, the multi-scale attention feature extraction method is proposed to highlight shape and edge feature information of juvenile abalones and increase detection precision of juvenile abalones with dense distribution and individual coverage; finally, the loss feedback training method is used to increase the diversity of data and the pixels of juvenile abalones in the images to get the even higher detection precision of juvenile abalones with small size. The experimental results show that the [email protected] value, [email protected] value and [email protected] value of the detection results of the proposed method are 91.14%, 89.90% and 80.14%, respectively. The precision and recall rates of the counting results are 99.59% and 97.74%, respectively, which are superior to the counting results of SSD, FSSD, MutualGuide, EfficientDet and VarifocalNet models. The proposed method can provide support for real-time monitoring of aquaculture density for juvenile abalones.</p></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"11 3","pages":"Pages 325-336"},"PeriodicalIF":7.7,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S221431732300046X/pdfft?md5=0e659a821a078f0956cfc5f7356a7af0&pid=1-s2.0-S221431732300046X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43549565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A low-cost digital 3D insect scanner 一种低成本的数字3D昆虫扫描仪

IF 7.7

Information Processing in Agriculture Pub Date : 2024-09-01 DOI: 10.1016/j.inpa.2023.03.003

引用次数: 0

Key technologies and applications of rural energy internet in China 中国农村能源互联网的关键技术及应用

IF 7.7

Information Processing in Agriculture Pub Date : 2024-09-01 DOI: 10.1016/j.inpa.2022.03.001

引用次数: 0

Pig face recognition based on improved YOLOv4 lightweight neural network 基于改进的YOLOv4轻量级神经网络的猪人脸识别

IF 7.7

Information Processing in Agriculture Pub Date : 2024-09-01 DOI: 10.1016/j.inpa.2023.03.004

{"title":"Pig face recognition based on improved YOLOv4 lightweight neural network","authors":"","doi":"10.1016/j.inpa.2023.03.004","DOIUrl":"10.1016/j.inpa.2023.03.004","url":null,"abstract":"<div><p>With the vigorous development of intelligence agriculture, the progress of automated large-scale and intensive pig farming has accelerated significantly. As a biological feature, the pig face has important research significance for precise breeding of pigs and traceability of health. In the management of live pigs, many managers adopt traditional methods, including color marking and RFID identification, but there will be problems such as off-label, mixed-label and waste of manpower. This work proposes a non-invasive way to study the identification of multiple individuals in pigs. The model was to first replace the original backbone network of YOLOv4 with MobileNet-v3, a popular lightweight network. Then depth-wise separable convolution was adopted in YOLOv4′s feature extraction network SPP and PANet to further reduce network parameters. Moreover, CBAM attention mechanism formed by the concatenation of CAM and SAM was added to PANet to ensure the network accuracy while reducing the model weight. The introduction of multi-attention mechanism selectively strengthened key areas of pig face and filtered out weak correlation features, so as to improve the overall model effect. Finally, an improved MobileNetv3-YOLOv4-PACNet (M-YOLOv4-C) network model was proposed to identify individual sows. The mAP were 98.15 %, the detection speed FPS were 106.3frames/s, and the model parameter size was only 44.74 MB, which can be well implanted into the small-volume pig house management sensors and applied to the pig management system in a lightweight, fast and accurate manner. This model will provide model support for subsequent pig behavior recognition and posture analysis.</p></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"11 3","pages":"Pages 356-371"},"PeriodicalIF":7.7,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2214317323000483/pdfft?md5=15cedd90f8b826def2e4ca0a3a7b3834&pid=1-s2.0-S2214317323000483-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46956825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0