Pub Date : 2024-07-25DOI: 10.1109/TAFE.2024.3415124
Basma Alsaid;Tracy Saroufil;Romaissa Berim;Sohaib Majzoub;Abir J. Hussain
Physical contamination of food is a prevalent issue within the food production industry. Contamination can occur at any stage of the food processing line. Many techniques are used in the literature for the detection of physical contamination in food. However, these techniques have some limitations when applied to fresh food products, particularly, when samples are characterized by diverse shapes and sizes. In addition, some of these techniques fail to detect hidden contaminants. In this work, we propose a novel approach to detect hidden physical contamination in fresh food products, including plastic fragments, stone fragments, and other foreign food objects, such as different food types that might inadvertently contaminate the sample. Electrical impedance tomography (EIT) is utilized to capture the impedance image of the sample to be used for contamination detection. Four deep learning models are trained using the EIT images to perform binary classification to identify contaminated samples. Three of the models are developed to detect the contaminants, each on its own, while the fourth model is used to detect any of the contaminates put together. The trained models achieved promising results with the accuracy of 85%, 92.9%, and 85.7% detecting plastic, stones, and other food types, respectively. The obtained accuracy when all contaminants put together was 78%. This performance shows the efficacy of the proposed approach over the existing techniques in the field.
食品的物理污染是食品生产行业普遍存在的问题。污染可能发生在食品加工生产线的任何阶段。文献中使用了许多检测食品物理污染的技术。然而，当这些技术应用于新鲜食品时，尤其是当样品的形状和大小各不相同时，就会受到一些限制。此外，其中一些技术还无法检测到隐藏的污染物。在这项工作中，我们提出了一种新方法来检测新鲜食品中隐藏的物理污染，包括塑料碎片、石块碎片和其他异物，如可能无意中污染样品的不同食物类型。电阻抗层析成像（EIT）被用来捕捉样品的阻抗图像，以用于污染检测。使用 EIT 图像训练了四个深度学习模型，以执行二元分类来识别受污染的样品。其中三个模型用于单独检测污染物，而第四个模型则用于综合检测任何污染物。经过训练的模型在检测塑料、石块和其他食物类型方面分别取得了 85%、92.9% 和 85.7% 的准确率，取得了可喜的成果。当所有杂质加在一起时，准确率为 78%。这一结果表明，与该领域的现有技术相比，所提出的方法非常有效。
{"title":"Food Physical Contamination Detection Using AI-Enhanced Electrical Impedance Tomography","authors":"Basma Alsaid;Tracy Saroufil;Romaissa Berim;Sohaib Majzoub;Abir J. Hussain","doi":"10.1109/TAFE.2024.3415124","DOIUrl":"https://doi.org/10.1109/TAFE.2024.3415124","url":null,"abstract":"Physical contamination of food is a prevalent issue within the food production industry. Contamination can occur at any stage of the food processing line. Many techniques are used in the literature for the detection of physical contamination in food. However, these techniques have some limitations when applied to fresh food products, particularly, when samples are characterized by diverse shapes and sizes. In addition, some of these techniques fail to detect hidden contaminants. In this work, we propose a novel approach to detect hidden physical contamination in fresh food products, including plastic fragments, stone fragments, and other foreign food objects, such as different food types that might inadvertently contaminate the sample. Electrical impedance tomography (EIT) is utilized to capture the impedance image of the sample to be used for contamination detection. Four deep learning models are trained using the EIT images to perform binary classification to identify contaminated samples. Three of the models are developed to detect the contaminants, each on its own, while the fourth model is used to detect any of the contaminates put together. The trained models achieved promising results with the accuracy of 85%, 92.9%, and 85.7% detecting plastic, stones, and other food types, respectively. The obtained accuracy when all contaminants put together was 78%. This performance shows the efficacy of the proposed approach over the existing techniques in the field.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"2 2","pages":"518-526"},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142408760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this work, modeling, simulation, and experimental measurements of a LoRaWAN network aimed at implementing a dense farm-area network (FAN) in the agrifood industry are presented. First, the network is modeled for a farm of the future, with as many sensors as would be useful, for the four main productive chains in Uruguay as a study case: livestock, timber, agriculture, and dairy industries. To this end, a survey of commercial sensors was conducted, a few farms were visited, and managers and partners in agrocompanies were interviewed. A LoRaWAN network with a single gateway was simulated to estimate the efficiency (related to data packets lost), in the case of a 1000 ha cattle field with more than 1500 sensors and some cameras sharing the network. Finally, the network efficiency was measured, using 30–40 LoRa modules @ 915 MHz, transmitting at pseudorandom times to emulate up to thousands of LoRa sensor nodes. The simulated and measured results are very similar, reaching > 92% efficiency in all cases. Sites bigger than 1000 ha on the four main productive chains were also simulated. Additionally, energy consumption and transmission distance measurements of LoRaWAN modules are presented, as well as an overview of the economic aspects related to the deployment of the network to corroborate them fit the requirements of a FAN in the agribusiness.
{"title":"A Model for a Dense LoRaWAN Farm-Area Network in the Agribusiness","authors":"Alfredo Arnaud;Matías Miguez;María Eugenia Araújo;Ariel Dagnino;Joel Gak;Aarón Jimenz;José Job Flores;Nicolas Calarco;Luis Arturo Soriano","doi":"10.1109/TAFE.2024.3422843","DOIUrl":"https://doi.org/10.1109/TAFE.2024.3422843","url":null,"abstract":"In this work, modeling, simulation, and experimental measurements of a LoRaWAN network aimed at implementing a dense farm-area network (FAN) in the agrifood industry are presented. First, the network is modeled for a farm of the future, with as many sensors as would be useful, for the four main productive chains in Uruguay as a study case: livestock, timber, agriculture, and dairy industries. To this end, a survey of commercial sensors was conducted, a few farms were visited, and managers and partners in agrocompanies were interviewed. A LoRaWAN network with a single gateway was simulated to estimate the efficiency (related to data packets lost), in the case of a 1000 ha cattle field with more than 1500 sensors and some cameras sharing the network. Finally, the network efficiency was measured, using 30–40 LoRa modules @ 915 MHz, transmitting at pseudorandom times to emulate up to thousands of LoRa sensor nodes. The simulated and measured results are very similar, reaching > 92% efficiency in all cases. Sites bigger than 1000 ha on the four main productive chains were also simulated. Additionally, energy consumption and transmission distance measurements of LoRaWAN modules are presented, as well as an overview of the economic aspects related to the deployment of the network to corroborate them fit the requirements of a FAN in the agribusiness.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"2 2","pages":"284-292"},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142430881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-12DOI: 10.1109/TAFE.2024.3421238
Bernardita Štitić;Luca Urbinati;Giuseppe Di Guglielmo;Luca P. Carloni;Mario R. Casu
Combining data-driven machine learning (ML) with microwave sensing (MWS) makes it possible to analyze packaged food in real time without any contact and spot low-density contaminants (e.g., plastics or glass splinters) that current industrial food safety methods cannot detect. This is achieved by training ML classifiers on the scattered signal reflected by the target food product exposed to MWs. In this article, we present an enhanced ML flow to boost foreign body detection accuracy of ML classifiers in MWS systems. Innovations include assessing the performance of a multiclass classifier, training it with MW frequency pairs as features, data augmentation, a new preprocessing scaler suitable for the feature distributions in the datasets, quantization, and pruning. The final test results, obtained using our previously designed MWS system and collected dataset of contaminated hazelnut-cocoa spread jars, show a multiclass accuracy for the floating-point model of 96.5%, which corresponds to a binary-equivalent accuracy of 97.3%. This is an improvement of +3.3% against the binary classifier of the previous work. The quantized and pruned model, instead, reached a multiclass accuracy of 94.2%, or a binary accuracy of 95.4%, thus still improving the previous work by +1.4%. Also, we achieved a latency of 26 $mu$ s on an AMD/Xilinx Kria K26 field programmable gate array (FPGA), a result which is ideal for high-throughput food production lines. Furthermore, we expand our latest work with supplementary details and experiments to further validate the proposed ML flow, including a comparative analysis against our prior results. Lastly, we share our datasets publicly on OpenML.
将数据驱动的机器学习（ML）与微波传感（MWS）相结合，可以对包装食品进行无接触实时分析，并发现当前工业食品安全方法无法检测的低密度污染物（如塑料或玻璃碎片）。这是通过对暴露在微波中的目标食品反射的散射信号训练 ML 分类器来实现的。在本文中，我们介绍了一种增强型 ML 流程，以提高 MWS 系统中 ML 分类器的异物检测精度。创新之处包括：评估多类分类器的性能、将微波频率对作为特征对其进行训练、数据增强、适合数据集特征分布的新预处理扩展器、量化和剪枝。最终测试结果显示，浮点模型的多类准确率为 96.5%，相当于二元等效准确率 97.3%。与之前的二进制分类器相比，提高了 3.3%。而经过量化和剪枝处理的模型的多分类准确率为 94.2%，二进制准确率为 95.4%，因此仍比之前的工作提高了 +1.4%。此外，我们还在 AMD/Xilinx Kria K26 现场可编程门阵列 (FPGA) 上实现了 26 美元/分钟的延迟，这一结果非常适合高通量食品生产线。此外，我们还通过补充细节和实验扩展了我们的最新工作，以进一步验证所提出的 ML 流程，包括与我们之前的结果进行比较分析。最后，我们在 OpenML 上公开分享我们的数据集。
{"title":"Enhanced Machine-Learning Flow for Microwave-Sensing Systems for Contaminant Detection in Food","authors":"Bernardita Štitić;Luca Urbinati;Giuseppe Di Guglielmo;Luca P. Carloni;Mario R. Casu","doi":"10.1109/TAFE.2024.3421238","DOIUrl":"https://doi.org/10.1109/TAFE.2024.3421238","url":null,"abstract":"Combining data-driven machine learning (ML) with microwave sensing (MWS) makes it possible to analyze packaged food in real time without any contact and spot low-density contaminants (e.g., plastics or glass splinters) that current industrial food safety methods cannot detect. This is achieved by training ML classifiers on the scattered signal reflected by the target food product exposed to MWs. In this article, we present an enhanced ML flow to boost foreign body detection accuracy of ML classifiers in MWS systems. Innovations include assessing the performance of a multiclass classifier, training it with MW frequency pairs as features, data augmentation, a new preprocessing scaler suitable for the feature distributions in the datasets, quantization, and pruning. The final test results, obtained using our previously designed MWS system and collected dataset of contaminated hazelnut-cocoa spread jars, show a multiclass accuracy for the floating-point model of 96.5%, which corresponds to a binary-equivalent accuracy of 97.3%. This is an improvement of +3.3% against the binary classifier of the previous work. The quantized and pruned model, instead, reached a multiclass accuracy of 94.2%, or a binary accuracy of 95.4%, thus still improving the previous work by +1.4%. Also, we achieved a latency of 26 \u0000<inline-formula><tex-math>$mu$</tex-math></inline-formula>\u0000s on an AMD/Xilinx Kria K26 field programmable gate array (FPGA), a result which is ideal for high-throughput food production lines. Furthermore, we expand our latest work with supplementary details and experiments to further validate the proposed ML flow, including a comparative analysis against our prior results. Lastly, we share our datasets publicly on OpenML.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"2 2","pages":"181-189"},"PeriodicalIF":0.0,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142430797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-12DOI: 10.1109/TAFE.2024.3420028
Bhabesh Deka;Debarun Chakraborty
Traditional methods of manual litchi fruit counting are labor-intensive, time-consuming, and prone to errors. Moreover, due to its complex growth structures, such as occlusion with leaves and branches, overlapping, and uneven color, it becomes more challenging for the current baseline detection and instance segmentation models to accurately identify the litchi fruits. The advancement of deep learning architecture and modern sensing technology, such as unmanned aerial vehicle (UAV), had shown great potential for improving fruit counting accuracy and efficiency. In this article, we propose a modified Mask-region-based convolutional neural network-based instance segmentation model using channel attention to detect and count litchis in complex natural environments using UAV. In addition, we build a UAV-Litchi dataset consisting of 1000 images with 31 000 litchi annotations, collected by the DJI Phantom 4 with RGB sensor and labeled with a LabelImg annotation tool. Experimental results show that the proposed model with the squeeze-and-excitation block improves the detection accuracy of litchi fruits, achieving a mean average precision, recall, and F1 score of 81.47%, 92.81%, and 88.40%, respectively, with an average inference time of 7.72 s. The high accuracy and efficiency of the proposed model demonstrate its potential for precise and accurate litchi detection in precision agriculture.
{"title":"UAV Sensing-Based Litchi Segmentation Using Modified Mask-RCNN for Precision Agriculture","authors":"Bhabesh Deka;Debarun Chakraborty","doi":"10.1109/TAFE.2024.3420028","DOIUrl":"https://doi.org/10.1109/TAFE.2024.3420028","url":null,"abstract":"Traditional methods of manual litchi fruit counting are labor-intensive, time-consuming, and prone to errors. Moreover, due to its complex growth structures, such as occlusion with leaves and branches, overlapping, and uneven color, it becomes more challenging for the current baseline detection and instance segmentation models to accurately identify the litchi fruits. The advancement of deep learning architecture and modern sensing technology, such as unmanned aerial vehicle (UAV), had shown great potential for improving fruit counting accuracy and efficiency. In this article, we propose a modified Mask-region-based convolutional neural network-based instance segmentation model using channel attention to detect and count litchis in complex natural environments using UAV. In addition, we build a UAV-Litchi dataset consisting of 1000 images with 31 000 litchi annotations, collected by the DJI Phantom 4 with RGB sensor and labeled with a LabelImg annotation tool. Experimental results show that the proposed model with the squeeze-and-excitation block improves the detection accuracy of litchi fruits, achieving a mean average precision, recall, and F1 score of 81.47%, 92.81%, and 88.40%, respectively, with an average inference time of 7.72 s. The high accuracy and efficiency of the proposed model demonstrate its potential for precise and accurate litchi detection in precision agriculture.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"2 2","pages":"509-517"},"PeriodicalIF":0.0,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142408887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the realm of agricultural automation, the precise identification of crop stress holds immense significance for enhancing crop productivity. Existing methods primarily focus on controlled environments, which may not accurately reflect field conditions. Field-based leaf image analysis poses challenges due to varying image quality and sunlight intensity. Moreover, the complexity of crop stress images, with their random lesion distribution, diverse symptoms, and complex backgrounds, further complicates the analysis. To overcome these limitations, a lightweight hybrid convolutional neural network has been developed. This system integrates the powerful three-deep blocks model with an autoencoder running in parallel to highlight regions of crop stress effectively. To support this approach, we have introduced the Indian Rice Disease Dataset (IRDD) with labeled images. The proposed system reports an average true positive rate (TPR) of 0.8766 and an average positive predicted value of 0.8720 on IRDD, which are higher than other state-of-the-art crop disease detection models. The system is validated on benchmark datasets, yielding significant results: TPR of 0.9870 (rice), 0.9985 (tomato), and 0.8559 (corn). Furthermore, the proposed model outperforms recent state-of-the-art works on the benchmark PlantDoc dataset, showing its effectiveness in generalizing plant disease identification tasks. Finally, an ablation study has been carried out to explore the importance of the two parallel branches. Overall, this study acts as a bridge between advanced science and practical application, showcasing how interdisciplinary automation could revolutionize crop disease identification, improve agricultural efficiency, and reshape broader industrial practices.
{"title":"Robust Deep Convolutional Solutions for Identifying Biotic Crop Stress in Wild Environments","authors":"Chiranjit Pal;Imon Mukherjee;Sanjay Chatterji;Sanjoy Pratihar;Pabitra Mitra;Partha Pratim Chakrabarti","doi":"10.1109/TAFE.2024.3422187","DOIUrl":"https://doi.org/10.1109/TAFE.2024.3422187","url":null,"abstract":"In the realm of agricultural automation, the precise identification of crop stress holds immense significance for enhancing crop productivity. Existing methods primarily focus on controlled environments, which may not accurately reflect field conditions. Field-based leaf image analysis poses challenges due to varying image quality and sunlight intensity. Moreover, the complexity of crop stress images, with their random lesion distribution, diverse symptoms, and complex backgrounds, further complicates the analysis. To overcome these limitations, a lightweight hybrid convolutional neural network has been developed. This system integrates the powerful three-deep blocks model with an autoencoder running in parallel to highlight regions of crop stress effectively. To support this approach, we have introduced the Indian Rice Disease Dataset (IRDD) with labeled images. The proposed system reports an average true positive rate (TPR) of 0.8766 and an average positive predicted value of 0.8720 on IRDD, which are higher than other state-of-the-art crop disease detection models. The system is validated on benchmark datasets, yielding significant results: TPR of 0.9870 (rice), 0.9985 (tomato), and 0.8559 (corn). Furthermore, the proposed model outperforms recent state-of-the-art works on the benchmark PlantDoc dataset, showing its effectiveness in generalizing plant disease identification tasks. Finally, an ablation study has been carried out to explore the importance of the two parallel branches. Overall, this study acts as a bridge between advanced science and practical application, showcasing how interdisciplinary automation could revolutionize crop disease identification, improve agricultural efficiency, and reshape broader industrial practices.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"2 2","pages":"497-508"},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142408885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wireless sensor networks can be a low-cost and efficient solution for monitoring environmental parameters in agriculture. In this work, we analyze the potentialities of using a network based on a general-purpose Internet of Things protocol such as OpenThread and on low-cost general-purpose physical and chemical sensors. This article aims to test and verify the platform functionalities when monitoring environmental parameters such as temperature, relative humidity, and visible and infrared irradiance in a real environment. We designed wireless sensor nodes and a data collection and visualization dashboard. We tested the sensors' response under controlled settings, conducted connectivity and network topology tests, validated the system functionality via in-field measurements, and discussed the main issues and potential capabilities of these sensors and network architecture.
{"title":"Network Connectivity and Sensor Response of an OpenThread WSN Platform for Crop Monitoring","authors":"Alessandro Checco;Vasco Fabiani;Maurizio Palmisano;Davide Polese","doi":"10.1109/TAFE.2024.3420648","DOIUrl":"https://doi.org/10.1109/TAFE.2024.3420648","url":null,"abstract":"Wireless sensor networks can be a low-cost and efficient solution for monitoring environmental parameters in agriculture. In this work, we analyze the potentialities of using a network based on a general-purpose Internet of Things protocol such as OpenThread and on low-cost general-purpose physical and chemical sensors. This article aims to test and verify the platform functionalities when monitoring environmental parameters such as temperature, relative humidity, and visible and infrared irradiance in a real environment. We designed wireless sensor nodes and a data collection and visualization dashboard. We tested the sensors' response under controlled settings, conducted connectivity and network topology tests, validated the system functionality via \u0000<italic>in-field</i>\u0000 measurements, and discussed the main issues and potential capabilities of these sensors and network architecture.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"2 2","pages":"218-225"},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10592646","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142430801","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}
Pub Date : 2024-07-09DOI: 10.1109/TAFE.2024.3416414
V. Cabrera;A. Delbuggio;H. Cardoso;D. Fraga;A. Gómez;M. Pedemonte;R. Ungerfeld;J. Oreggioni
Sheep production in extensive conditions faces several challenges. These challenges could be addressed with behavior monitoring systems, contributing to animal well-being, enhancing animal research, and improving productivity. This article presents the design, manufacture, and test of an online sheep behavior monitoring system for extensive conditions. It comprises a wearable electronic collar device and a cloud server (deployed with Amazon Web Services) for storing data and providing a web user interface. The collar has an Icarus Internet of Things (IoT) Board, allowing motion data collection with a three-axis accelerometer, global navigation satellite system (GNSS) location data acquisition, and narrowband IoT communication. The device has solar panels and a battery. Our application acquires accelerometer data at 25 Hz, location data every 10–30 s, and battery level and cellular signal strength every 50 s. We encoded accelerometer samples to reduce the transmitted data. We manufactured 30 collars that collect and transmit data to the cloud server. Our system facilitates data processing, both collar and server side. We introduce a preliminary Random Forest algorithm for behavior classification on the device that identifies “still,” “walking,” and “running” with a 78% general accuracy. The device's autonomy exceeds ten days in continuous operation (streaming raw and processed data) while if the device transmits only processed data and GNSS data every 4 h, autonomy rises to 100 days. This allows us to glimpse the application of this system in long-term research experiments and farming production.
{"title":"Harnessing Technology for Livestock Research: An Online Sheep Behavior Monitoring System","authors":"V. Cabrera;A. Delbuggio;H. Cardoso;D. Fraga;A. Gómez;M. Pedemonte;R. Ungerfeld;J. Oreggioni","doi":"10.1109/TAFE.2024.3416414","DOIUrl":"https://doi.org/10.1109/TAFE.2024.3416414","url":null,"abstract":"Sheep production in extensive conditions faces several challenges. These challenges could be addressed with behavior monitoring systems, contributing to animal well-being, enhancing animal research, and improving productivity. This article presents the design, manufacture, and test of an online sheep behavior monitoring system for extensive conditions. It comprises a wearable electronic collar device and a cloud server (deployed with Amazon Web Services) for storing data and providing a web user interface. The collar has an Icarus Internet of Things (IoT) Board, allowing motion data collection with a three-axis accelerometer, global navigation satellite system (GNSS) location data acquisition, and narrowband IoT communication. The device has solar panels and a battery. Our application acquires accelerometer data at 25 Hz, location data every 10–30 s, and battery level and cellular signal strength every 50 s. We encoded accelerometer samples to reduce the transmitted data. We manufactured 30 collars that collect and transmit data to the cloud server. Our system facilitates data processing, both collar and server side. We introduce a preliminary Random Forest algorithm for behavior classification on the device that identifies “still,” “walking,” and “running” with a 78% general accuracy. The device's autonomy exceeds ten days in continuous operation (streaming raw and processed data) while if the device transmits only processed data and GNSS data every 4 h, autonomy rises to 100 days. This allows us to glimpse the application of this system in long-term research experiments and farming production.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"2 2","pages":"306-313"},"PeriodicalIF":0.0,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142430692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-08DOI: 10.1109/TAFE.2024.3416415
Ngoc-Bao-Van Le;Jun-Ho Huh
The development of Internet of Things (IoTs) technology in agriculture, notably aquaculture, has increased over the years due to empowering real-time monitoring and improved environmental sustainability. To ensure the development and survival of aquatic life, farm employees must constantly check and take prompt action to protect the sustainable habitat in ponds. It is also crucial for providing technical assistance to farmers during the growing season. To address these issues, we present the building of a smart aquaculture assistant system integrated with IoT and Big Data. The main components of this system include the IoT layer assistant layer. First, tracking the pond environment functions through sensor systems set up in the pond, such as turbidity, pH, and temperature sensors, which are built for the IoT layer. Our assistant can make suggestions for farmers by visualizing farm environment conditions and outdoor weather analysis. The assistant layer uses a fine-tuning NLP model GPT 3.5 for our aquaculture farming 500 frequently asked questions and crawled knowledge dataset. The chatbot can provide naturally logical replies and knowledge related to farming activities. The system is implemented as mobile and desktop applications using React Native and Python to monitor or manipulate administrative tasks. Thus, thanks to our smart assistant, prompt preventive action can be taken to reduce losses, increase productivity, and enhance farmers’ knowledge related to farming.
{"title":"AgTech: Building Smart Aquaculture Assistant System Integrated IoT and Big Data Analysis","authors":"Ngoc-Bao-Van Le;Jun-Ho Huh","doi":"10.1109/TAFE.2024.3416415","DOIUrl":"https://doi.org/10.1109/TAFE.2024.3416415","url":null,"abstract":"The development of Internet of Things (IoTs) technology in agriculture, notably aquaculture, has increased over the years due to empowering real-time monitoring and improved environmental sustainability. To ensure the development and survival of aquatic life, farm employees must constantly check and take prompt action to protect the sustainable habitat in ponds. It is also crucial for providing technical assistance to farmers during the growing season. To address these issues, we present the building of a smart aquaculture assistant system integrated with IoT and Big Data. The main components of this system include the IoT layer assistant layer. First, tracking the pond environment functions through sensor systems set up in the pond, such as turbidity, pH, and temperature sensors, which are built for the IoT layer. Our assistant can make suggestions for farmers by visualizing farm environment conditions and outdoor weather analysis. The assistant layer uses a fine-tuning NLP model GPT 3.5 for our aquaculture farming 500 frequently asked questions and crawled knowledge dataset. The chatbot can provide naturally logical replies and knowledge related to farming activities. The system is implemented as mobile and desktop applications using React Native and Python to monitor or manipulate administrative tasks. Thus, thanks to our smart assistant, prompt preventive action can be taken to reduce losses, increase productivity, and enhance farmers’ knowledge related to farming.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"2 2","pages":"471-482"},"PeriodicalIF":0.0,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142408403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Effective crop health monitoring is essential for farmers to make informed decisions about managing their crops. In canola crop management, the rapid proliferation of flea beetle (FB) populations is a major concern, as these pests can cause significant crop damage. Traditional manual field monitoring for FBs is time consuming and error-prone due to its reliance on visual assessments of FB damage to small seedlings, making conducting frequent and accurate surveys difficult. One of the key pieces of information in assessing if control of FBs is required is the presence of live FBs in the canola crop. This article proposes a novel insect-monitoring framework that uses a solar-powered, intelligent trap called the smart insect trap (SIT), equipped with a high-resolution camera and a deep-learning-based object detection network. Using this SIT, coupled with a kairomonal lure, the FB population can be monitored hourly, and population increases can be identified quickly. The SIT processes images at the edge and sends results to the cloud every 40 min for FB monitoring and analysis. It uses a modified you look only once version 8 small (YOLOv8s) object detection network, FB-YOLO, to improve its ability to detect small FBs. The modification is implemented in the network's neck, which aggregates features from the deep and early pyramids of the backbone in the neck. Improved attention to small objects is achieved by incorporating spatially aware features from early pyramids. In addition, the network is integrated with an advanced box selection algorithm called confluence nonmax suppression (NMS-C) to prevent duplicate detections in highly overlapped clusters of FBs. The FB-YOLO achieved an average precision ( $text{mAP}@0.5$ ) of 89.97%, a 1.215% improvement over the YOLOv8s network with only 0.324 million additional parameters. Integrating NMS-C further improved the $text{mAP}@0.5$ by 0.19%, leading to an overall $text{mAP}@0.5$ of 90.16%.
{"title":"A Novel Computer Vision System for Efficient Flea Beetle Monitoring in Canola Crop","authors":"Muhib Ullah;Muhammad Shabbir Hasan;Abdul Bais;Tyler Wist;Shaun Sharpe","doi":"10.1109/TAFE.2024.3406329","DOIUrl":"https://doi.org/10.1109/TAFE.2024.3406329","url":null,"abstract":"Effective crop health monitoring is essential for farmers to make informed decisions about managing their crops. In canola crop management, the rapid proliferation of flea beetle (FB) populations is a major concern, as these pests can cause significant crop damage. Traditional manual field monitoring for FBs is time consuming and error-prone due to its reliance on visual assessments of FB damage to small seedlings, making conducting frequent and accurate surveys difficult. One of the key pieces of information in assessing if control of FBs is required is the presence of live FBs in the canola crop. This article proposes a novel insect-monitoring framework that uses a solar-powered, intelligent trap called the smart insect trap (SIT), equipped with a high-resolution camera and a deep-learning-based object detection network. Using this SIT, coupled with a kairomonal lure, the FB population can be monitored hourly, and population increases can be identified quickly. The SIT processes images at the edge and sends results to the cloud every 40 min for FB monitoring and analysis. It uses a modified you look only once version 8 small (YOLOv8s) object detection network, FB-YOLO, to improve its ability to detect small FBs. The modification is implemented in the network's neck, which aggregates features from the deep and early pyramids of the backbone in the neck. Improved attention to small objects is achieved by incorporating spatially aware features from early pyramids. In addition, the network is integrated with an advanced box selection algorithm called confluence nonmax suppression (NMS-C) to prevent duplicate detections in highly overlapped clusters of FBs. The FB-YOLO achieved an average precision (\u0000<inline-formula><tex-math>$text{mAP}@0.5$</tex-math></inline-formula>\u0000) of 89.97%, a 1.215% improvement over the YOLOv8s network with only 0.324 million additional parameters. Integrating NMS-C further improved the \u0000<inline-formula><tex-math>$text{mAP}@0.5$</tex-math></inline-formula>\u0000 by 0.19%, leading to an overall \u0000<inline-formula><tex-math>$text{mAP}@0.5$</tex-math></inline-formula>\u0000 of 90.16%.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"2 2","pages":"483-496"},"PeriodicalIF":0.0,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142408683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Agriculture is facing bigger challenges in the 21st century due to the scarcity of resources. Artificial intelligence is being integrated with agriculture to cater to people's needs, unlocking fresh avenues for sustainability and innovation. One of the crucial agricultural practices is plant growth monitoring to detect plant stress at an early stage. In the past, there have been preliminary attempts at plant growth monitoring using red–green–blue (RGB) and depth images. The major challenge of this approach is the unavailability of the depth camera at the farmers' end. In this work, we have developed a transformer-based plant age RGB-depth fusion knowledge distillation network (PA-RDFKNet), a multi-to-single modal teacher–student network, that exploits the combined knowledge of RGB-depth pairs at the training time to infer the growth using RGB images alone during test time. The model uses a distillation loss that combines response-based, feature-based, and relation-based knowledge distillation techniques in the offline scheme. The proposed knowledge distillation improves the mean squared error for RGB images from 2 to 0.14 weeks. The results are validated on three different datasets.
{"title":"PA-RDFKNet: Unifying Plant Age Estimation through RGB-Depth Fusion and Knowledge Distillation","authors":"Shreya Bansal;Malya Singh;Seema Barda;Neeraj Goel;Mukesh Saini","doi":"10.1109/TAFE.2024.3418818","DOIUrl":"https://doi.org/10.1109/TAFE.2024.3418818","url":null,"abstract":"Agriculture is facing bigger challenges in the 21st century due to the scarcity of resources. Artificial intelligence is being integrated with agriculture to cater to people's needs, unlocking fresh avenues for sustainability and innovation. One of the crucial agricultural practices is plant growth monitoring to detect plant stress at an early stage. In the past, there have been preliminary attempts at plant growth monitoring using red–green–blue (RGB) and depth images. The major challenge of this approach is the unavailability of the depth camera at the farmers' end. In this work, we have developed a transformer-based plant age RGB-depth fusion knowledge distillation network (PA-RDFKNet), a multi-to-single modal teacher–student network, that exploits the combined knowledge of RGB-depth pairs at the training time to infer the growth using RGB images alone during test time. The model uses a distillation loss that combines response-based, feature-based, and relation-based knowledge distillation techniques in the offline scheme. The proposed knowledge distillation improves the mean squared error for RGB images from 2 to 0.14 weeks. The results are validated on three different datasets.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"2 2","pages":"226-235"},"PeriodicalIF":0.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142430796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：