To improve food security and environmental sustainability amid the global crisis of climate change and nutrition quality requirements as well as low-cost agricultural needs and electricity issues, particularly in developing countries like India, it is essential to combine autonomy and newer energy storage methods with traditional agriculture. Existing field robotic mechanisms, path planning methods, and battery energy management systems are designed independent of each other. To ensure energy efficient and safety aware operation of autonomous agricultural robots, coordination between aforementioned techniques is necessary. With the aim to provide such solution, in this work we propose a framework to integrate robot mechanism, path planning, and battery management system. Simulations are performed to validate the performance of the algorithm.
{"title":"Energy- and Safety-Aware Operation of Battery-Powered Autonomous Robots in Agriculture","authors":"Shashank Dhananjay Vyas;Vigneshwar Kumutha Subash;Manav Mepani;Sai Venkatesh;Arpita Sinha;Anirban Guha;Satadru Dey","doi":"10.1109/TAFE.2024.3353597","DOIUrl":"https://doi.org/10.1109/TAFE.2024.3353597","url":null,"abstract":"To improve food security and environmental sustainability amid the global crisis of climate change and nutrition quality requirements as well as low-cost agricultural needs and electricity issues, particularly in developing countries like India, it is essential to combine autonomy and newer energy storage methods with traditional agriculture. Existing field robotic mechanisms, path planning methods, and battery energy management systems are designed independent of each other. To ensure energy efficient and safety aware operation of autonomous agricultural robots, coordination between aforementioned techniques is necessary. With the aim to provide such solution, in this work we propose a framework to integrate robot mechanism, path planning, and battery management system. Simulations are performed to validate the performance of the algorithm.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"2 1","pages":"51-58"},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140544157","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 : 2024-01-26DOI: 10.1109/TAFE.2024.3349566
Fan Yang;Lei Shu
As a special type of node, solar insecticidal lamps (SILs) require regular maintenance to ensure effective insecticidal performance and accurate collection of pest information. While hiring professionals for management and maintenance is a viable solution, it comes with the drawback of high maintenance costs. An effective approach to reducing these costs is deploying SILs along routes frequently traversed by agricultural workers (AWs), as these tasks can be easily incorporated into their routine. Therefore, inspired by the trajectory of high-density AWs, this article focuses on studying the constrained SIL Deployment Problem under coverage and maintenance cost considerations, referred to as cSILDP-CMC. In this problem, SIL nodes are deployed at a limited set of weighted candidate locations (CLs) situated on the ridges. The objective of cSILDP-CMC is to select a subset of CLs for SIL placement, maximizing coverage while keeping the total maintenance cost within the allocated budget. To begin, we propose a method for quantifying the maintenance cost of each CL and assign a weight to them accordingly. Subsequently, we formulate cSILDP-CMC as a budgeted maximum coverage problem and prove that it is NP-Hardness. We then introduce a two-phase algorithm (TPA) as an approximation algorithm to address the defined optimization problem. Finally, to assess the effectiveness of our design, we conduct theoretical analysis of TPA and perform extensive simulations. The simulation results clearly demonstrate that TPA outperforms three other algorithms in terms of coverage ratio. It achieves a minimum coverage ratio increase of 2% while maintaining the same fixed maintenance cost. Furthermore, TPA also stands out in terms of maintenance costs by reducing them at least 3.9% while maintaining a comparable coverage level.
{"title":"A Trajectory-Inspired Node Deployment Strategy in Solar Insecticidal Lamps Internet of Things Under Coverage and Maintenance Cost Considerations","authors":"Fan Yang;Lei Shu","doi":"10.1109/TAFE.2024.3349566","DOIUrl":"https://doi.org/10.1109/TAFE.2024.3349566","url":null,"abstract":"As a special type of node, solar insecticidal lamps (SILs) require regular maintenance to ensure effective insecticidal performance and accurate collection of pest information. While hiring professionals for management and maintenance is a viable solution, it comes with the drawback of high maintenance costs. An effective approach to reducing these costs is deploying SILs along routes frequently traversed by agricultural workers (AWs), as these tasks can be easily incorporated into their routine. Therefore, inspired by the trajectory of high-density AWs, this article focuses on studying the constrained SIL Deployment Problem under coverage and maintenance cost considerations, referred to as cSILDP-CMC. In this problem, SIL nodes are deployed at a limited set of weighted candidate locations (CLs) situated on the ridges. The objective of cSILDP-CMC is to select a subset of CLs for SIL placement, maximizing coverage while keeping the total maintenance cost within the allocated budget. To begin, we propose a method for quantifying the maintenance cost of each CL and assign a weight to them accordingly. Subsequently, we formulate cSILDP-CMC as a budgeted maximum coverage problem and prove that it is NP-Hardness. We then introduce a two-phase algorithm (TPA) as an approximation algorithm to address the defined optimization problem. Finally, to assess the effectiveness of our design, we conduct theoretical analysis of TPA and perform extensive simulations. The simulation results clearly demonstrate that TPA outperforms three other algorithms in terms of coverage ratio. It achieves a minimum coverage ratio increase of 2% while maintaining the same fixed maintenance cost. Furthermore, TPA also stands out in terms of maintenance costs by reducing them at least 3.9% while maintaining a comparable coverage level.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"2 1","pages":"28-42"},"PeriodicalIF":0.0,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140544304","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}
The pressure on agricultural efficiency is nowadays greater than ever, hence there has been an abrupt technological involvement in this sector in the last decade. In this article, a stand-alone, in situ, soil quality sensing system is presented for the first time, capable of monitoring chemical parameters in the soil without any human intervention. The system is capable of measuring potassium and nitrate concentrations with a sensitivity of $sim$ 0.6 $mu$ A/mM (R $^{2}$ = 0.9775) and $sim$ 2 $mu$ A/mM (R $^{2}$ = 0.9708) in the range of 0.1–10 mM, pH with a sensitivity of $sim$ −30 mV/pH (R $^{2}$ = 0.9068) in the range of 4–10, and temperature with a sensitivity of $sim$ 1.6 $Omega$ / $^{o}$ C (R $^{2}$ = 0.9999) in the range of −30 to 60 $^circ$ C. It includes an impedance probe for impedance measurements up to 1 MHz. A unique packaging was also developed to protect the array from the soil while allowing enough time for the sensors to take precise measurements. The thick-film multisensor array was connected to a stand-alone, electronic node, while the complete system was deployed in the field, taking measurements every 30 min, showing the capability to track watering and fertilizing times.
{"title":"A Stand-Alone, In Situ, Soil Quality Sensing System for Precision Agriculture","authors":"Marios Sophocleous;Andreas Karkotis;Antri Papasavva;Michale Goldberger;Loukia Vassiliou;Jose Vicente Ros-Lis;Yosi Shacham-Diamand;Julius Georgiou","doi":"10.1109/TAFE.2024.3351953","DOIUrl":"https://doi.org/10.1109/TAFE.2024.3351953","url":null,"abstract":"The pressure on agricultural efficiency is nowadays greater than ever, hence there has been an abrupt technological involvement in this sector in the last decade. In this article, a stand-alone, in situ, soil quality sensing system is presented for the first time, capable of monitoring chemical parameters in the soil without any human intervention. The system is capable of measuring potassium and nitrate concentrations with a sensitivity of \u0000<inline-formula><tex-math>$sim$</tex-math></inline-formula>\u00000.6 \u0000<inline-formula><tex-math>$mu$</tex-math></inline-formula>\u0000A/mM (R\u0000<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>\u0000 = 0.9775) and \u0000<inline-formula><tex-math>$sim$</tex-math></inline-formula>\u00002 \u0000<inline-formula><tex-math>$mu$</tex-math></inline-formula>\u0000A/mM (R\u0000<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>\u0000 = 0.9708) in the range of 0.1–10 mM, pH with a sensitivity of \u0000<inline-formula><tex-math>$sim$</tex-math></inline-formula>\u0000−30 mV/pH (R\u0000<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>\u0000 = 0.9068) in the range of 4–10, and temperature with a sensitivity of \u0000<inline-formula><tex-math>$sim$</tex-math></inline-formula>\u00001.6 \u0000<inline-formula><tex-math>$Omega$</tex-math></inline-formula>\u0000/\u0000<inline-formula><tex-math>$^{o}$</tex-math></inline-formula>\u0000C (R\u0000<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>\u0000 = 0.9999) in the range of −30 to 60 \u0000<inline-formula><tex-math>$^circ$</tex-math></inline-formula>\u0000C. It includes an impedance probe for impedance measurements up to 1 MHz. A unique packaging was also developed to protect the array from the soil while allowing enough time for the sensors to take precise measurements. The thick-film multisensor array was connected to a stand-alone, electronic node, while the complete system was deployed in the field, taking measurements every 30 min, showing the capability to track watering and fertilizing times.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"2 1","pages":"43-50"},"PeriodicalIF":0.0,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140544196","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 : 2023-12-29DOI: 10.1109/TAFE.2023.3348251
{"title":"2023 Index IEEE Transactions on AgriFood Electronics Vol. 1","authors":"","doi":"10.1109/TAFE.2023.3348251","DOIUrl":"https://doi.org/10.1109/TAFE.2023.3348251","url":null,"abstract":"","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"1 2","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10376199","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139060157","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 : 2023-12-18DOI: 10.1109/TAFE.2023.3339362
{"title":"IEEE Circuits and Systems Society Information","authors":"","doi":"10.1109/TAFE.2023.3339362","DOIUrl":"https://doi.org/10.1109/TAFE.2023.3339362","url":null,"abstract":"","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"1 2","pages":"C3-C3"},"PeriodicalIF":0.0,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10364903","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138739534","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 : 2023-12-18DOI: 10.1109/TAFE.2023.3337887
I-Te Chen;Chien-Chang Chen;Hong-Jie Dai;Babam Rianto;Si-Kai Huang;Chung-Hong Lee
Specialty coffee beans have a unique aroma and flavor. The aromas of coffee in the world are affected by several issues, including growing area, climate, postharvest processing (such as dry and wet methods), roasting treatment, etc. These issues significantly contribute to the development of coffee-bean aromas. Since humans have a limited ability to recognize the aroma of coffee, we need a reliable system to resolve the method of characterizing the world's coffee aroma. Therefore, in this article, we proposed an incremental learning method for digitizing the complexity of coffee aromas using an electronic nose (E-nose) system. We also developed a method to create coffee-aroma fingerprints to represent their aromatic features among different coffees. In our experiments, the incremental learning model achieved high accuracy, proving the authenticity of recognizing various world specialty coffee aromas. The approach leverages an E-nose system and coffee-aroma datasets to preserve specialty coffee aromas around the world. In addition, the ultimate goal of this method is to build a scalable database of various coffee aromas while improving the accuracy of system recognition.
{"title":"An Incremental Learning Method for Preserving World Coffee Aromas by Using an Electronic Nose and Accumulated Specialty Coffee Datasets","authors":"I-Te Chen;Chien-Chang Chen;Hong-Jie Dai;Babam Rianto;Si-Kai Huang;Chung-Hong Lee","doi":"10.1109/TAFE.2023.3337887","DOIUrl":"https://doi.org/10.1109/TAFE.2023.3337887","url":null,"abstract":"Specialty coffee beans have a unique aroma and flavor. The aromas of coffee in the world are affected by several issues, including growing area, climate, postharvest processing (such as dry and wet methods), roasting treatment, etc. These issues significantly contribute to the development of coffee-bean aromas. Since humans have a limited ability to recognize the aroma of coffee, we need a reliable system to resolve the method of characterizing the world's coffee aroma. Therefore, in this article, we proposed an incremental learning method for digitizing the complexity of coffee aromas using an electronic nose (E-nose) system. We also developed a method to create coffee-aroma fingerprints to represent their aromatic features among different coffees. In our experiments, the incremental learning model achieved high accuracy, proving the authenticity of recognizing various world specialty coffee aromas. The approach leverages an E-nose system and coffee-aroma datasets to preserve specialty coffee aromas around the world. In addition, the ultimate goal of this method is to build a scalable database of various coffee aromas while improving the accuracy of system recognition.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"2 1","pages":"12-27"},"PeriodicalIF":0.0,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140544214","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}
Pulses are one of the most important food crops in the world due to their higher protein content, approximately 21%–25%. Therefore, it is crucial to analyze the crop's quality and impurity levels. Stones, pebbles, marble chips, and synthetic dyes, such as lead chromate, metanil yellow, and artificial colors, are some of the impurities added to pulse products, accidentally or on purpose. The existing analysis techniques are mostly laboratory-based, time-consuming, costly, and require human examination. To address this issue, this article presents an intelligent system, FoodExpert, based on image processing that automatically uses an image of a pulse sample to identify the region of interest and essential attributes. Then, machine learning frameworks are used to predict pulse quality and adulteration levels based on the obtained parameters. On the test dataset, the suggested model had a 96% accuracy rate for pulse quality prediction and 94% accuracy for adulteration level prediction. The model was successfully deployed on a Raspberry Pi-based hardware prototype and mobile application.
豆类是世界上最重要的粮食作物之一，因为其蛋白质含量较高，约为 21%-25%。因此，分析作物的质量和杂质含量至关重要。石子、卵石、大理石碎屑和合成染料（如铬酸铅、偏苯胺黄和人工色素）是意外或有意添加到豌豆产品中的一些杂质。现有的分析技术大多以实验室为基础，耗时长、成本高，而且需要人工检测。为解决这一问题，本文介绍了一种基于图像处理的智能系统 FoodExpert，它能自动使用脉搏样品的图像来识别感兴趣的区域和基本属性。然后，使用机器学习框架根据获得的参数预测脉搏质量和掺假程度。在测试数据集上，建议的模型预测脉搏质量的准确率为 96%，预测掺假水平的准确率为 94%。该模型已成功部署在基于 Raspberry Pi 的硬件原型和移动应用程序上。
{"title":"FoodExpert: Portable Intelligent Device for Rapid Screening of Pulse Quality and Adulteration","authors":"Harsh Pandey;Subhanshu Arya;Debanjan Das;Venkanna Udutalapally","doi":"10.1109/TAFE.2023.3336441","DOIUrl":"https://doi.org/10.1109/TAFE.2023.3336441","url":null,"abstract":"Pulses are one of the most important food crops in the world due to their higher protein content, approximately 21%–25%. Therefore, it is crucial to analyze the crop's quality and impurity levels. Stones, pebbles, marble chips, and synthetic dyes, such as lead chromate, metanil yellow, and artificial colors, are some of the impurities added to pulse products, accidentally or on purpose. The existing analysis techniques are mostly laboratory-based, time-consuming, costly, and require human examination. To address this issue, this article presents an intelligent system, FoodExpert, based on image processing that automatically uses an image of a pulse sample to identify the region of interest and essential attributes. Then, machine learning frameworks are used to predict pulse quality and adulteration levels based on the obtained parameters. On the test dataset, the suggested model had a 96% accuracy rate for pulse quality prediction and 94% accuracy for adulteration level prediction. The model was successfully deployed on a Raspberry Pi-based hardware prototype and mobile application.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"2 1","pages":"2-11"},"PeriodicalIF":0.0,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140544169","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}
Precision agriculture involves the strategic utilization of resources, precise application of inputs, and continuous monitoring of crop health with the aim of enhancing productivity and sustainability in the field of agriculture. However, seed quality is difficult since natural differences among seed batches may affect germination rates, vigor, and crop performance. Hence, in this article, a novel fusion model for seed detection and germination is proposed. The proposed model combines the U-Net and CNN architectures for seed segmentation and classification, respectively. By harnessing U-Net's capabilities in image segmentation and CNN's strengths in classification, the proposed approach enables effective seed germination analysis. In addition, the model is specifically optimized for real-time processing and applications by implementing it on the NVIDIA Jetson Nano embedded GPU platform. The proposed fusion model achieved 0.91 pixel accuracy, 0.84 intersection over union, and 0.90 precision. The proposed model demonstrated excellent predictive ability when compared with the ResNet50, Inception, and LeNet. In addition, the proposed model requires less number of trainable parameters after LeNet. Further, the proposed model tested in real time and achieved 0.26 ms latency.
{"title":"Real-Time Seed Detection and Germination Analysis in Precision Agriculture: A Fusion Model With U-Net and CNN on Jetson Nano","authors":"Ramesh Reddy Donapati;Ramalingaswamy Cheruku;Prakash Kodali","doi":"10.1109/TAFE.2023.3332495","DOIUrl":"https://doi.org/10.1109/TAFE.2023.3332495","url":null,"abstract":"Precision agriculture involves the strategic utilization of resources, precise application of inputs, and continuous monitoring of crop health with the aim of enhancing productivity and sustainability in the field of agriculture. However, seed quality is difficult since natural differences among seed batches may affect germination rates, vigor, and crop performance. Hence, in this article, a novel fusion model for seed detection and germination is proposed. The proposed model combines the U-Net and CNN architectures for seed segmentation and classification, respectively. By harnessing U-Net's capabilities in image segmentation and CNN's strengths in classification, the proposed approach enables effective seed germination analysis. In addition, the model is specifically optimized for real-time processing and applications by implementing it on the NVIDIA Jetson Nano embedded GPU platform. The proposed fusion model achieved 0.91 pixel accuracy, 0.84 intersection over union, and 0.90 precision. The proposed model demonstrated excellent predictive ability when compared with the ResNet50, Inception, and LeNet. In addition, the proposed model requires less number of trainable parameters after LeNet. Further, the proposed model tested in real time and achieved 0.26 ms latency.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"1 2","pages":"145-155"},"PeriodicalIF":0.0,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138739462","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 : 2023-11-29DOI: 10.1109/TAFE.2023.3330583
James Reynolds;Matt Taggart;Devon Martin;Edgar Lobaton;Amanda Cardoso;Michael Daniele;Alper Bozkurt
Smart farming is the targeted use of phenotyping for the rapid, continuous, and accurate assessment of plant health in the field. Bioimpedance monitoring can play a role in smart farming as a phenotyping method, which is now accessible thanks to recent efforts to commoditize and miniaturize electronics. Here, we demonstrate that bioimpedance measurements reflect the physiological changes in live plant tissue with induced alterations in their environmental conditions. When plants were exposed to $-$ 1.0 MPa polyethylene glycol, to simulate drought conditions, the extracellular resistance was observed to increase prior to the intercellular resistance, where the low frequency bioimpedance measurements increased by 25% within one hour. Similar patterns were observed when drought stress was applied to the plants by water withholding, with a bioimpedance increase within a matter of a few hours. The bioimpedance measurements were also compared with leaf relative water content, imaging, and field transpirable soil water, which reinforced these findings. These preliminary results suggest that bioimpedance can function as a phenotyping tool for continuous and real time monitoring of plant stress to allow the development of strategies to prevent damage from environmental stresses such as drought.
{"title":"Rapid Drought Stress Detection in Plants Using Bioimpedance Measurements and Analysis","authors":"James Reynolds;Matt Taggart;Devon Martin;Edgar Lobaton;Amanda Cardoso;Michael Daniele;Alper Bozkurt","doi":"10.1109/TAFE.2023.3330583","DOIUrl":"https://doi.org/10.1109/TAFE.2023.3330583","url":null,"abstract":"Smart farming is the targeted use of phenotyping for the rapid, continuous, and accurate assessment of plant health in the field. Bioimpedance monitoring can play a role in smart farming as a phenotyping method, which is now accessible thanks to recent efforts to commoditize and miniaturize electronics. Here, we demonstrate that bioimpedance measurements reflect the physiological changes in live plant tissue with induced alterations in their environmental conditions. When plants were exposed to \u0000<inline-formula><tex-math>$-$</tex-math></inline-formula>\u00001.0 MPa polyethylene glycol, to simulate drought conditions, the extracellular resistance was observed to increase prior to the intercellular resistance, where the low frequency bioimpedance measurements increased by 25% within one hour. Similar patterns were observed when drought stress was applied to the plants by water withholding, with a bioimpedance increase within a matter of a few hours. The bioimpedance measurements were also compared with leaf relative water content, imaging, and field transpirable soil water, which reinforced these findings. These preliminary results suggest that bioimpedance can function as a phenotyping tool for continuous and real time monitoring of plant stress to allow the development of strategies to prevent damage from environmental stresses such as drought.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"1 2","pages":"135-144"},"PeriodicalIF":0.0,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138739548","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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