Pub Date : 2023-11-29DOI: 10.1109/TAFE.2023.3329849
Rakiba Rayhana;Zhenyu Ma;Zheng Liu;Gaozhi Xiao;Yuefeng Ruan;Jatinder S. Sangha
Agriculture production is one of the fundamental contributors to a nation's economic development. Every year, plant diseases result in significant crop losses that threaten the global food supply chain. Early estimation of plant diseases could play an essential role in safeguarding crops and fostering economic growth. Recently, hyperspectral imaging techniques have emerged as powerful tools for early disease detection, as they have demonstrated capabilities to detect plant diseases from tissue to canopy levels. This article provides an extensive overview of the principles, types, and operating platforms of hyperspectral image sensors. Furthermore, this article delves into the specifics of these sensors' application in plant disease detection, including disease identification, classification, severity analysis, and understanding genetic resistance. In addition, this article addresses the current challenges in the field and suggests potential solutions to mitigate these pressing issues. Finally, this article outlines the promising future trends and directions of hyperspectral imaging in plant disease detection and analysis. With continuous improvement and application, these imaging techniques have great potential to revolutionize plant disease management, thereby enhancing agricultural productivity and ensuring food security.
{"title":"A Review on Plant Disease Detection Using Hyperspectral Imaging","authors":"Rakiba Rayhana;Zhenyu Ma;Zheng Liu;Gaozhi Xiao;Yuefeng Ruan;Jatinder S. Sangha","doi":"10.1109/TAFE.2023.3329849","DOIUrl":"https://doi.org/10.1109/TAFE.2023.3329849","url":null,"abstract":"Agriculture production is one of the fundamental contributors to a nation's economic development. Every year, plant diseases result in significant crop losses that threaten the global food supply chain. Early estimation of plant diseases could play an essential role in safeguarding crops and fostering economic growth. Recently, hyperspectral imaging techniques have emerged as powerful tools for early disease detection, as they have demonstrated capabilities to detect plant diseases from tissue to canopy levels. This article provides an extensive overview of the principles, types, and operating platforms of hyperspectral image sensors. Furthermore, this article delves into the specifics of these sensors' application in plant disease detection, including disease identification, classification, severity analysis, and understanding genetic resistance. In addition, this article addresses the current challenges in the field and suggests potential solutions to mitigate these pressing issues. Finally, this article outlines the promising future trends and directions of hyperspectral imaging in plant disease detection and analysis. With continuous improvement and application, these imaging techniques have great potential to revolutionize plant disease management, thereby enhancing agricultural productivity and ensuring food security.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"1 2","pages":"108-134"},"PeriodicalIF":0.0,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138739535","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 : 2023-08-28DOI: 10.1109/TAFE.2023.3303177
Michael D. O'Toole;Marcin Glowacz;Anthony J. Peyton
Avocado fruit is a popular, nutritious, and commercially valuable product that, with a short window of ripeness and heterogeneous maturity, presents particular challenges when bringing to market. There is significant value in being able to measure avocado fruit ripeness and maturity, especially nondestructively, with the prospect of improvements in consignment management, food loss, and consumer satisfaction. In this article, we explore the bioimpedance spectra of avocado fruit. Bioimpedance has been found to correlate with ripeness in avocado fruit over a frequency range termed the $beta$ -dispersion where cell polarization effects are significant. Our contribution is to use magnetic induction spectroscopy to measure conductivity across this range, an entirely noncontact method that uses eddy currents induced in the fruit flesh by magnetic fields rather than penetrative or surface electrodes as in previous work. We were able to measure a clear $beta$ -dispersion curve, finding fruit conductivity rising from $sim$ 0.6 mS/cm at 100 kHz to $sim$ 4 mS/cm at 10 MHz. This agrees with the literature at higher and lower frequencies, and completes a gap in the spectra not previously reported. Further, we find evidence of changes to the conductivity spectra as the fruit ages and ripens, with the spectra broadly flattening according to a set of identified trends. This indicates a relation between bioimpedance spectra and ripeness, although high intersample variability precludes the spectra as a direct estimation technique at this stage.
{"title":"Bioimpedance Measurement of Avocado Fruit Using Magnetic Induction Spectroscopy","authors":"Michael D. O'Toole;Marcin Glowacz;Anthony J. Peyton","doi":"10.1109/TAFE.2023.3303177","DOIUrl":"10.1109/TAFE.2023.3303177","url":null,"abstract":"Avocado fruit is a popular, nutritious, and commercially valuable product that, with a short window of ripeness and heterogeneous maturity, presents particular challenges when bringing to market. There is significant value in being able to measure avocado fruit ripeness and maturity, especially nondestructively, with the prospect of improvements in consignment management, food loss, and consumer satisfaction. In this article, we explore the bioimpedance spectra of avocado fruit. Bioimpedance has been found to correlate with ripeness in avocado fruit over a frequency range termed the \u0000<inline-formula><tex-math>$beta$</tex-math></inline-formula>\u0000-dispersion where cell polarization effects are significant. Our contribution is to use magnetic induction spectroscopy to measure conductivity across this range, an entirely noncontact method that uses eddy currents induced in the fruit flesh by magnetic fields rather than penetrative or surface electrodes as in previous work. We were able to measure a clear \u0000<inline-formula><tex-math>$beta$</tex-math></inline-formula>\u0000-dispersion curve, finding fruit conductivity rising from \u0000<inline-formula><tex-math>$sim$</tex-math></inline-formula>\u00000.6 mS/cm at 100 kHz to \u0000<inline-formula><tex-math>$sim$</tex-math></inline-formula>\u00004 mS/cm at 10 MHz. This agrees with the literature at higher and lower frequencies, and completes a gap in the spectra not previously reported. Further, we find evidence of changes to the conductivity spectra as the fruit ages and ripens, with the spectra broadly flattening according to a set of identified trends. This indicates a relation between bioimpedance spectra and ripeness, although high intersample variability precludes the spectra as a direct estimation technique at this stage.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"1 2","pages":"99-107"},"PeriodicalIF":0.0,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75790846","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 : 2023-08-02DOI: 10.1109/TAFE.2023.3295383
Kang Eun Jeon;Tsz Ngai Lin;James She;Simon Wong;Rajesh Govindan;Tareq Al-Ansari;Bo Wang
Greenhouse farming is a trending practice to secure food production in desert environments. Such a practice often requires sensing systems to monitor the greenhouse microclimate. However, traditional monitoring systems are often limited by their feature size, energy consumption, and maintenance cost. To address these issues, this article introduces a luXSensing beacon—an energy harvesting sensing device empowered with Bluetooth communication technology to perform continuous environmental sensing. To enable long-lasting or even batteryless operation of the sensing device, we propose a novel and generic design methodology to suggest minimum energy harvesting hardware requirements, namely the photovoltaic panel's area and supercapacitor's size for energy storage. In addition, a lifetime model is also proposed to calculate the extended lifetime of a hybrid energy harvesting device if it is equipped with a backup battery. Based on the proposed methodology, a prototype system is developed, deployed, and tested in a desert greenhouse. The luXSensing beacon demonstrated its capability of monitoring air temperature and illuminance continuously in a 24/7 manner. The comparative compactness and low-energy consumption of the system are advantageous not only to its deployment in greenhouses but also to the reduction of energy budget and the maintenance cost of greenhouse farming.
{"title":"LuXSensing Beacon: Batteryless IoT Sensor, Design Methodology, and Field Test for Sustainable Greenhouse Monitoring","authors":"Kang Eun Jeon;Tsz Ngai Lin;James She;Simon Wong;Rajesh Govindan;Tareq Al-Ansari;Bo Wang","doi":"10.1109/TAFE.2023.3295383","DOIUrl":"10.1109/TAFE.2023.3295383","url":null,"abstract":"Greenhouse farming is a trending practice to secure food production in desert environments. Such a practice often requires sensing systems to monitor the greenhouse microclimate. However, traditional monitoring systems are often limited by their feature size, energy consumption, and maintenance cost. To address these issues, this article introduces a luXSensing beacon—an energy harvesting sensing device empowered with Bluetooth communication technology to perform continuous environmental sensing. To enable long-lasting or even batteryless operation of the sensing device, we propose a novel and generic design methodology to suggest minimum energy harvesting hardware requirements, namely the photovoltaic panel's area and supercapacitor's size for energy storage. In addition, a lifetime model is also proposed to calculate the extended lifetime of a hybrid energy harvesting device if it is equipped with a backup battery. Based on the proposed methodology, a prototype system is developed, deployed, and tested in a desert greenhouse. The luXSensing beacon demonstrated its capability of monitoring air temperature and illuminance continuously in a 24/7 manner. The comparative compactness and low-energy consumption of the system are advantageous not only to its deployment in greenhouses but also to the reduction of energy budget and the maintenance cost of greenhouse farming.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"1 2","pages":"86-98"},"PeriodicalIF":0.0,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77620631","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 : 2023-07-07DOI: 10.1109/TAFE.2023.3286699
Yuan Li;Bangsong Du;Lin Luo;Yusheng Luo;Xing Yang;Ye Liu;Lei Shu
The combination of solar insecticidal lamps (SILs) and wireless sensor networks has spawned a green and efficient solution for agricultural pest control, called solar insecticidal lamps Internet of Things (SIL-IoTs). In realistic large-scale SIL-IoTs deployment scenarios, the integrated pest information collected across the network enables effective localization of pest-dense areas. However, the problem of asymmetric links caused by various factors, such as irregular wireless communication range and discharge interference of nodes, is the main obstacle to the deployment of SIL-IoTs. Motivated by this problem, the pest-dense area localization strategy (PALS) based on asymmetric links is proposed. First, the asymmetric nodes in the network are judged by analyzing the one-hop and two-hop information of SIL nodes. Subsequently, the Gabriel graph or relative neighborhood graph planarization algorithm is used to planarize the symmetric links in the network. Then, the quick rejection method and straddle experiment are used to remove the cross sections after planarization. Finally, by counting the number of SIL node discharges and facilitating the left-hand rule, PALS successfully reduces the difference between the calculated and actual pest-dense areas. The experiments showed that PALS achieves an average improvement of 15% and a maximum improvement of up to 42.2% across the four experimental settings, indicating its higher accuracy and robustness compared with the state-of-the-art algorithms.
太阳能杀虫灯（SIL）与无线传感器网络的结合为农业害虫控制提供了一种绿色高效的解决方案，即太阳能杀虫灯物联网（SIL-IoTs）。在现实的大规模 SIL-IoTs 部署场景中，通过网络收集的综合害虫信息可以有效定位害虫密集区域。然而，无线通信距离不固定、节点放电干扰等各种因素导致的非对称链路问题是 SIL-IoTs 部署的主要障碍。受此问题的启发，本文提出了基于非对称链路的害虫密集区定位策略（PALS）。首先，通过分析 SIL 节点的一跳和两跳信息来判断网络中的非对称节点。然后，使用 Gabriel 图或相对邻域图平面化算法对网络中的对称链路进行平面化处理。然后，使用快速剔除法和跨距实验去除平面化后的横截面。最后，通过计算 SIL 节点的放电次数和促进左手定则，PALS 成功缩小了计算得出的害虫密集区与实际密集区之间的差异。实验结果表明，在四种实验设置中，PALS 平均提高了 15%，最高提高了 42.2%，这表明与最先进的算法相比，PALS 具有更高的准确性和鲁棒性。
{"title":"A Scheme for Pest-Dense Area Localization With Solar Insecticidal Lamps Internet of Things Under Asymmetric Links","authors":"Yuan Li;Bangsong Du;Lin Luo;Yusheng Luo;Xing Yang;Ye Liu;Lei Shu","doi":"10.1109/TAFE.2023.3286699","DOIUrl":"10.1109/TAFE.2023.3286699","url":null,"abstract":"The combination of solar insecticidal lamps (SILs) and wireless sensor networks has spawned a green and efficient solution for agricultural pest control, called solar insecticidal lamps Internet of Things (SIL-IoTs). In realistic large-scale SIL-IoTs deployment scenarios, the integrated pest information collected across the network enables effective localization of pest-dense areas. However, the problem of asymmetric links caused by various factors, such as irregular wireless communication range and discharge interference of nodes, is the main obstacle to the deployment of SIL-IoTs. Motivated by this problem, the pest-dense area localization strategy (PALS) based on asymmetric links is proposed. First, the asymmetric nodes in the network are judged by analyzing the one-hop and two-hop information of SIL nodes. Subsequently, the Gabriel graph or relative neighborhood graph planarization algorithm is used to planarize the symmetric links in the network. Then, the quick rejection method and straddle experiment are used to remove the cross sections after planarization. Finally, by counting the number of SIL node discharges and facilitating the left-hand rule, PALS successfully reduces the difference between the calculated and actual pest-dense areas. The experiments showed that PALS achieves an average improvement of 15% and a maximum improvement of up to 42.2% across the four experimental settings, indicating its higher accuracy and robustness compared with the state-of-the-art algorithms.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"1 2","pages":"71-85"},"PeriodicalIF":0.0,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74062987","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}
A step forward in smart agriculture is moving to direct monitoring plants and crops instead of their environment. Understanding plant status is crucial in improving food production and reducing the usage of water and chemicals in agriculture. Here, we propose a “plant-wearable,” low-cost, and low-power method to measure in-vivo green plant stem frequency as the indicator for plant watering stress status. Our method is based on measuring the frequency of a digital signal obtained with a relaxation oscillator where the plant is a part of the feedback loop. The frequency was correlated with the soil water potential, used as a critical indicator of plant water stress, and an 85% correlation was found. In this way, the measuring system matches all the requirements of smart agriculture and Internet of Things (IoT): ultra-low-cost, low-complexity, ultra-low-power, and small sizes, introducing the concept of wearability in plant monitoring. The proposed solution exploits the plant and the soil as a communication channel: the signal carrying the plant watering stress status information is transmitted to a receiving system connected to a different plant. The system's current consumption is lower than 50 $bm {mu }$ A during the transmission in the plant and 40 mA for wireless communication. During inactivity periods, the total current consumption is lower than 15 $bm {mu }$ A. Another important aspect is that the system has to be energy autonomous. Our proposal is based on energy harvesting solutions from multiple sources: solar cells and plant microbial fuel cells. This way, the system is batteryless, thanks to supercapacitors as a storage element. The system can be deployed in the fields and used to monitor plants directly in their environment.
{"title":"A “Plant-Wearable System” for Its Health Monitoring by Intra- and Interplant Communication","authors":"Umberto Garlando;Stefano Calvo;Mattia Barezzi;Alessandro Sanginario;Paolo Motto Ros;Danilo Demarchi","doi":"10.1109/TAFE.2023.3284563","DOIUrl":"10.1109/TAFE.2023.3284563","url":null,"abstract":"A step forward in smart agriculture is moving to direct monitoring plants and crops instead of their environment. Understanding plant status is crucial in improving food production and reducing the usage of water and chemicals in agriculture. Here, we propose a “plant-wearable,” low-cost, and low-power method to measure in-vivo green plant stem frequency as the indicator for plant watering stress status. Our method is based on measuring the frequency of a digital signal obtained with a relaxation oscillator where the plant is a part of the feedback loop. The frequency was correlated with the soil water potential, used as a critical indicator of plant water stress, and an 85% correlation was found. In this way, the measuring system matches all the requirements of smart agriculture and Internet of Things (IoT): ultra-low-cost, low-complexity, ultra-low-power, and small sizes, introducing the concept of wearability in plant monitoring. The proposed solution exploits the plant and the soil as a communication channel: the signal carrying the plant watering stress status information is transmitted to a receiving system connected to a different plant. The system's current consumption is lower than 50 \u0000<inline-formula><tex-math>$bm {mu }$</tex-math></inline-formula>\u0000A during the transmission in the plant and 40 mA for wireless communication. During inactivity periods, the total current consumption is lower than 15 \u0000<inline-formula><tex-math>$bm {mu }$</tex-math></inline-formula>\u0000A. Another important aspect is that the system has to be energy autonomous. Our proposal is based on energy harvesting solutions from multiple sources: solar cells and plant microbial fuel cells. This way, the system is batteryless, thanks to supercapacitors as a storage element. The system can be deployed in the fields and used to monitor plants directly in their environment.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"1 2","pages":"60-70"},"PeriodicalIF":0.0,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82836745","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 : 2023-06-21DOI: 10.1109/TAFE.2023.3282059
{"title":"IEEE Circuits and Systems Society Information","authors":"","doi":"10.1109/TAFE.2023.3282059","DOIUrl":"https://doi.org/10.1109/TAFE.2023.3282059","url":null,"abstract":"","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"1 1","pages":"C2-C2"},"PeriodicalIF":0.0,"publicationDate":"2023-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9787768/9906904/10159162.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50225506","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 : 2023-06-21DOI: 10.1109/TAFE.2023.3282061
{"title":"IEEE Circuits and Systems Society Information","authors":"","doi":"10.1109/TAFE.2023.3282061","DOIUrl":"https://doi.org/10.1109/TAFE.2023.3282061","url":null,"abstract":"","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"1 1","pages":"C3-C3"},"PeriodicalIF":0.0,"publicationDate":"2023-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9787768/9906904/10159163.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50225509","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 : 2023-06-06DOI: 10.1109/TAFE.2023.3277790
Matteo Bruno Lodi;Claudia Macciò;Nicola Curreli;Andrea Melis;Giuseppe Mazzarella;Alessandro Fanti
Carasau bread is a traditional product from Sardinia (IT). This flat bread is experiencing industrial advancement, through automation, and has great market potential. However, there is lack of understanding of how the composition (water content, salt, and yeast concentration) affects the product quality. In this work, a microwave dielectric spectroscopy study is performed to investigate how the composition of Carasau bread doughs influences the spectra of this food product up to 20 GHz. A third-order Cole–Cole model was used for the physical and quantitative understanding of the electromagnetic properties of this food product. Then, it has been studied how salt, yeast, and water variations affected the model parameters. This work could pave the route to the development of non-destructive, contactless microwave sensors for Carasau bread quality assessment.
{"title":"Effects of Carasau Dough Composition on the Microwave Dielectric Spectra up to 20 GHz","authors":"Matteo Bruno Lodi;Claudia Macciò;Nicola Curreli;Andrea Melis;Giuseppe Mazzarella;Alessandro Fanti","doi":"10.1109/TAFE.2023.3277790","DOIUrl":"https://doi.org/10.1109/TAFE.2023.3277790","url":null,"abstract":"Carasau bread is a traditional product from Sardinia (IT). This flat bread is experiencing industrial advancement, through automation, and has great market potential. However, there is lack of understanding of how the composition (water content, salt, and yeast concentration) affects the product quality. In this work, a microwave dielectric spectroscopy study is performed to investigate how the composition of Carasau bread doughs influences the spectra of this food product up to 20 GHz. A third-order Cole–Cole model was used for the physical and quantitative understanding of the electromagnetic properties of this food product. Then, it has been studied how salt, yeast, and water variations affected the model parameters. This work could pave the route to the development of non-destructive, contactless microwave sensors for Carasau bread quality assessment.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"1 1","pages":"50-57"},"PeriodicalIF":0.0,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9787768/9906904/10144938.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50225507","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 : 2023-04-19DOI: 10.1109/TAFE.2023.3262748
Amirhossein Zaji;Zheng Liu;Gaozhi Xiao;Pankaj Bhowmik;Jatinder S. Sangha;Yuefeng Ruan
There is a positive correlation between wheat plant height and lodging, yield, and biomass. So, in precision agriculture, a high-throughput estimation of the wheat plant's height in terms of its spikes is essential. This study aims to develop a straightforward, cost-effective method for measuring the height of wheat plants using stereo cameras. To collect the required datasets, we conducted an experiment in which we collected RGB images along with their depth layer using two renowned stereo cameras, OAKD and D455. Then, we used a deep learning model called mask region-based convolutional neural networks to localize and distinguish the spikes in the collected images. In this study, we localized the wheat spikes using object detection (OD) and instance segmentation (IS) models. The advantage of the OD model over the IS model is that its bounding box annotation procedure in the data preparation phase is significantly faster than the IS model's polygon annotation. However, the disadvantage of OD is that there are many background pixels in each predicted bounding box, which reduces the performance of height estimation. To facilitate the annotation process of the collected datasets, we also developed a hybrid scale-invariant feature transform random forest-based active learning algorithm to transfer the annotations of one camera to the other. The results show that the OAKD camera performs better than the D455 camera for wheat plant height estimation due to its higher RGB quality and better matching of the mono camera images. Using the OAKD camera and IS model, the algorithm proposed in this study is able to estimate wheat height with mean absolute percentage error values of 0.75% and 0.67% at the spike and plot levels, respectively.
{"title":"Wheat Spikes Height Estimation Using Stereo Cameras","authors":"Amirhossein Zaji;Zheng Liu;Gaozhi Xiao;Pankaj Bhowmik;Jatinder S. Sangha;Yuefeng Ruan","doi":"10.1109/TAFE.2023.3262748","DOIUrl":"https://doi.org/10.1109/TAFE.2023.3262748","url":null,"abstract":"There is a positive correlation between wheat plant height and lodging, yield, and biomass. So, in precision agriculture, a high-throughput estimation of the wheat plant's height in terms of its spikes is essential. This study aims to develop a straightforward, cost-effective method for measuring the height of wheat plants using stereo cameras. To collect the required datasets, we conducted an experiment in which we collected RGB images along with their depth layer using two renowned stereo cameras, OAKD and D455. Then, we used a deep learning model called mask region-based convolutional neural networks to localize and distinguish the spikes in the collected images. In this study, we localized the wheat spikes using object detection (OD) and instance segmentation (IS) models. The advantage of the OD model over the IS model is that its bounding box annotation procedure in the data preparation phase is significantly faster than the IS model's polygon annotation. However, the disadvantage of OD is that there are many background pixels in each predicted bounding box, which reduces the performance of height estimation. To facilitate the annotation process of the collected datasets, we also developed a hybrid scale-invariant feature transform random forest-based active learning algorithm to transfer the annotations of one camera to the other. The results show that the OAKD camera performs better than the D455 camera for wheat plant height estimation due to its higher RGB quality and better matching of the mono camera images. Using the OAKD camera and IS model, the algorithm proposed in this study is able to estimate wheat height with mean absolute percentage error values of 0.75% and 0.67% at the spike and plot levels, respectively.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"1 1","pages":"15-28"},"PeriodicalIF":0.0,"publicationDate":"2023-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50425692","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 : 2023-03-05DOI: 10.1109/TAFE.2023.3267617
Jui-Feng Yeh;Kuei-Mei Lin;Chen-Yu Lin;Jen-Chun Kang
In this article, the grades of mangoes were classified using an AlexNet–spatial pyramid pooling network (SPP-Net) with a segmentation algorithm based on a Mask region-based convolutional neural network (R-CNN). Computer vision technologies have begun to be used for fruit grade classification, and this is a major topic of interest in agricultural automation. However, because insufficient fruit grade classification accuracy is achieved with these technologies, manual processing must be performed. The accuracy of fruit grade classification can be enhanced using a Mask R-CNN, SPP-Net, and specific background processing. The designed mango grade classification system contains four modules: 1) a user interface module, 2) an object detection module, 3) an image preprocessing module, and 4) a fruit grade classification module. A camera is used to capture images of mangoes for display on the user interface. The object segmentation module generates a mango shape mask and bounding box by using a Mask R-CNN. The image preprocessing module uses the generated bounding box and mango shape mask to crop the mango and color the background blue. Finally, AlexNet–SPP-Net outputs the fruit grade. We validated the proposed approach by implementing it in mango grade classification and comparing its accuracy with that of relevant existing methods from the literature. According to the experimental results, the proposed approach outperforms the traditional AlexNet-based approach.
{"title":"Intelligent Mango Fruit Grade Classification Using AlexNet-SPP With Mask R-CNN-Based Segmentation Algorithm","authors":"Jui-Feng Yeh;Kuei-Mei Lin;Chen-Yu Lin;Jen-Chun Kang","doi":"10.1109/TAFE.2023.3267617","DOIUrl":"https://doi.org/10.1109/TAFE.2023.3267617","url":null,"abstract":"In this article, the grades of mangoes were classified using an AlexNet–spatial pyramid pooling network (SPP-Net) with a segmentation algorithm based on a Mask region-based convolutional neural network (R-CNN). Computer vision technologies have begun to be used for fruit grade classification, and this is a major topic of interest in agricultural automation. However, because insufficient fruit grade classification accuracy is achieved with these technologies, manual processing must be performed. The accuracy of fruit grade classification can be enhanced using a Mask R-CNN, SPP-Net, and specific background processing. The designed mango grade classification system contains four modules: 1) a user interface module, 2) an object detection module, 3) an image preprocessing module, and 4) a fruit grade classification module. A camera is used to capture images of mangoes for display on the user interface. The object segmentation module generates a mango shape mask and bounding box by using a Mask R-CNN. The image preprocessing module uses the generated bounding box and mango shape mask to crop the mango and color the background blue. Finally, AlexNet–SPP-Net outputs the fruit grade. We validated the proposed approach by implementing it in mango grade classification and comparing its accuracy with that of relevant existing methods from the literature. According to the experimental results, the proposed approach outperforms the traditional AlexNet-based approach.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"1 1","pages":"41-49"},"PeriodicalIF":0.0,"publicationDate":"2023-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50425705","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}
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