Pub Date : 2024-09-23DOI: 10.1109/TAFE.2024.3449214
David J. Daniels;Frank Podd;Anthony J. Peyton;Qiao Cheng
Optimization of the yield of crops is essential for the security of the food supply and the efficiency of farming. This paper examines some of the issues and challenges involved with the measurement of the potato tubers within the soil using ground penetrating radar (GPR) in the U.K. An order of magnitude assessment of the received signal levels from single or multiple groups of potatoes is provided. The antenna configurations are based on loaded dipole antennas near the potato ridge surface. Measurements of potato tubers at two test sites in the U.K. are described, as well as an approach to signal processing to optimize detectability. The article provides a systematic study of GPR techniques for the monitoring of tuber growth.
{"title":"Application of Ground Penetrating Radar to Potato Crop Assessment","authors":"David J. Daniels;Frank Podd;Anthony J. Peyton;Qiao Cheng","doi":"10.1109/TAFE.2024.3449214","DOIUrl":"https://doi.org/10.1109/TAFE.2024.3449214","url":null,"abstract":"Optimization of the yield of crops is essential for the security of the food supply and the efficiency of farming. This paper examines some of the issues and challenges involved with the measurement of the potato tubers within the soil using ground penetrating radar (GPR) in the U.K. An order of magnitude assessment of the received signal levels from single or multiple groups of potatoes is provided. The antenna configurations are based on loaded dipole antennas near the potato ridge surface. Measurements of potato tubers at two test sites in the U.K. are described, as well as an approach to signal processing to optimize detectability. The article provides a systematic study of GPR techniques for the monitoring of tuber growth.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"2 2","pages":"596-605"},"PeriodicalIF":0.0,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142408756","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 : 2024-09-18DOI: 10.1109/TAFE.2024.3454109
Tanushree Dey;Somnath Bera;Lakshman Prasad Latua;Milan Parua;Anwesha Mukherjee;Debashis De
This article proposes a crop yield prediction and recommendation system for agriculture 5.0 based on edge computing, machine learning (ML), and steganography. In comparison with the existing crop yield prediction and recommendation frameworks, for the first time we are integrating steganography with edge computing and ML to provide a secure crop yield prediction and recommendation system. In the proposed system, an edge device is used for data preprocessing, and the private cloud server referred to as agri-server is maintained for data analysis and storage. For protecting data privacy during transmission, modified least significant bit-based image steganography is used. For data analysis, six ML approaches are used and compared based on their performance. The experimental results demonstrate that each ML approach achieves above 90% accuracy in crop yield prediction. The results also present that the proposed framework achieves highest prediction accuracy of 99.9% which is better than the existing crop yield prediction frameworks. The results also demonstrate that the proposed framework reduces the latency and energy consumption by $sim$ 10% compared to the remote cloud-based crop yield prediction framework.
本文提出了一种基于边缘计算、机器学习（ML）和隐写技术的农业 5.0 农作物产量预测和推荐系统。与现有的作物产量预测和推荐框架相比，我们首次将隐写术与边缘计算和 ML 相结合，提供了一个安全的作物产量预测和推荐系统。在提议的系统中，边缘设备用于数据预处理，而被称为农业服务器的私有云服务器则用于数据分析和存储。为了在传输过程中保护数据隐私，使用了基于最小有效位的修正图像隐写术。在数据分析方面，使用了六种 ML 方法，并根据其性能进行了比较。实验结果表明，每种 ML 方法在作物产量预测方面的准确率都超过了 90%。结果还表明，拟议框架的预测准确率最高，达到 99.9%，优于现有的作物产量预测框架。结果还表明，与基于云的远程作物产量预测框架相比，拟议框架减少了 10% 的延迟和能耗。
{"title":"iCrop: An Intelligent Crop Recommendation System for Agriculture 5.0","authors":"Tanushree Dey;Somnath Bera;Lakshman Prasad Latua;Milan Parua;Anwesha Mukherjee;Debashis De","doi":"10.1109/TAFE.2024.3454109","DOIUrl":"https://doi.org/10.1109/TAFE.2024.3454109","url":null,"abstract":"This article proposes a crop yield prediction and recommendation system for agriculture 5.0 based on edge computing, machine learning (ML), and steganography. In comparison with the existing crop yield prediction and recommendation frameworks, for the first time we are integrating steganography with edge computing and ML to provide a secure crop yield prediction and recommendation system. In the proposed system, an edge device is used for data preprocessing, and the private cloud server referred to as agri-server is maintained for data analysis and storage. For protecting data privacy during transmission, modified least significant bit-based image steganography is used. For data analysis, six ML approaches are used and compared based on their performance. The experimental results demonstrate that each ML approach achieves above 90% accuracy in crop yield prediction. The results also present that the proposed framework achieves highest prediction accuracy of 99.9% which is better than the existing crop yield prediction frameworks. The results also demonstrate that the proposed framework reduces the latency and energy consumption by \u0000<inline-formula><tex-math>$sim$</tex-math></inline-formula>\u000010% compared to the remote cloud-based crop yield prediction framework.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"2 2","pages":"587-595"},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142409063","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}
Climate change, adverse weather conditions, and illegitimate farming practices have caused severe damage to the agricultural ecosystem, resulting in significant crop loss in the last decade. One of the major challenges is the breakout of plant diseases that harm the crop in the field. To address this issue, several artificial intelligence and Internet of Things-based systems have been developed for crop monitoring and containment of plant diseases at early stages. In this article, a real-time plant disease identification system is designed using drone-based surveillance and farmer's input. A lightweight plant disease classification model is deployed in the proposed system using a fusion of a vision transformer and a convolutional neural network. The proposed model deploys conditional attention with a statistical squeeze-and-excitation module to efficiently learn the plant disease patterns from images captured under normal and challenging weather conditions. With only 0.95 million trainable parameters, the performance of the proposed plant disease classification model surpasses that of seven state-of-the-art techniques on five public datasets and an in-house developed maize dataset from drone camera-captured images under varying environmental conditions. To provide a better learning experience of real-world data to the model, a generative adversarial network, C3GAN, inspired by cycleGAN, is proposed for data augmentation of the collected maize dataset. The system keeps updating the model parameters based on the feedback of agriculture experts and farmers when new diseases break out or the model's performance deteriorates on unseen data during the surveillance over a period of time.
{"title":"Real-Time Plant Disease Identification: Fusion of Vision Transformer and Conditional Convolutional Network With C3GAN-Based Data Augmentation","authors":"Poornima Singh Thakur;Shubhangi Chaturvedi;Pritee Khanna;Tanuja Sheorey;Aparajita Ojha","doi":"10.1109/TAFE.2024.3447792","DOIUrl":"https://doi.org/10.1109/TAFE.2024.3447792","url":null,"abstract":"Climate change, adverse weather conditions, and illegitimate farming practices have caused severe damage to the agricultural ecosystem, resulting in significant crop loss in the last decade. One of the major challenges is the breakout of plant diseases that harm the crop in the field. To address this issue, several artificial intelligence and Internet of Things-based systems have been developed for crop monitoring and containment of plant diseases at early stages. In this article, a real-time plant disease identification system is designed using drone-based surveillance and farmer's input. A lightweight plant disease classification model is deployed in the proposed system using a fusion of a vision transformer and a convolutional neural network. The proposed model deploys conditional attention with a statistical squeeze-and-excitation module to efficiently learn the plant disease patterns from images captured under normal and challenging weather conditions. With only 0.95 million trainable parameters, the performance of the proposed plant disease classification model surpasses that of seven state-of-the-art techniques on five public datasets and an in-house developed maize dataset from drone camera-captured images under varying environmental conditions. To provide a better learning experience of real-world data to the model, a generative adversarial network, C3GAN, inspired by cycleGAN, is proposed for data augmentation of the collected maize dataset. The system keeps updating the model parameters based on the feedback of agriculture experts and farmers when new diseases break out or the model's performance deteriorates on unseen data during the surveillance over a period of time.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"2 2","pages":"576-586"},"PeriodicalIF":0.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142408783","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 : 2024-09-06DOI: 10.1109/TAFE.2024.3444730
Moteaal Asadi Shirzi;Mehrdad R. Kermani
In this article, we propose a new algorithm to improve plant recognition through the use of feature descriptors. The accurate results from this identification method are essential for enabling autonomous tasks, such as stem-stake coupling, in precision agriculture. The proposed method divides the input seedling color image into subimages within the International Commission on Illumination, for three color axes, L for lightness, A for the green-red component, and B for the blue-yellow component, color space and extracts seven key feature descriptors for each subimage. It then uses feature descriptors to create a matrix, which is employed to train an artificial neural network to determine optimized cutoff values. This network suggests cutoff values for a multilevel threshold segmentation for plant recognition. The method provides robust and real-time adaptive segmentation adaptable to various seedlings, backgrounds, and lighting conditions. By enabling accurate segmentation of the plant, morphological image processing can more effectively eliminate leaves to locate the seedling stem. This methodology automates image analysis in seedling propagation facilities and greenhouses and enables a wide range of precision agricultural tasks.
{"title":"Adaptive Feature-Based Plant Recognition","authors":"Moteaal Asadi Shirzi;Mehrdad R. Kermani","doi":"10.1109/TAFE.2024.3444730","DOIUrl":"https://doi.org/10.1109/TAFE.2024.3444730","url":null,"abstract":"In this article, we propose a new algorithm to improve plant recognition through the use of feature descriptors. The accurate results from this identification method are essential for enabling autonomous tasks, such as stem-stake coupling, in precision agriculture. The proposed method divides the input seedling color image into subimages within the International Commission on Illumination, for three color axes, L for lightness, A for the green-red component, and B for the blue-yellow component, color space and extracts seven key feature descriptors for each subimage. It then uses feature descriptors to create a matrix, which is employed to train an artificial neural network to determine optimized cutoff values. This network suggests cutoff values for a multilevel threshold segmentation for plant recognition. The method provides robust and real-time adaptive segmentation adaptable to various seedlings, backgrounds, and lighting conditions. By enabling accurate segmentation of the plant, morphological image processing can more effectively eliminate leaves to locate the seedling stem. This methodology automates image analysis in seedling propagation facilities and greenhouses and enables a wide range of precision agricultural tasks.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"2 2","pages":"335-346"},"PeriodicalIF":0.0,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142430880","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 : 2024-09-05DOI: 10.1109/TAFE.2024.3445119
Baofeng Ji;Haoyu Li;Xin Jin;Ji Zhang;Fazhan Tao;Peng Li;Jianhua Wang;Huitao Fan
Timely detection and prevention of tomato leaf diseases are crucial for improving tomato yields. To address the issue of low efficiency in detecting tomato leaf diseases, this article proposes a lightweight tomato leaf disease recognition method. First, enhanced intersection over union is introduced in the you only look once v8 (YOLOv8) model to replace the complete intersection over union loss function, enhancing the accuracy of bounding box localization. To solve the problem of fixed sample shapes and square convolution kernels not adapting well to different targets, lightweight alterable Kernel convolution (AKConv) is introduced, providing arbitrary parameters and shapes for the convolution kernel. Inspired by the lightweight characteristics of AKConv, the C2f module is improved by integrating AKConv to reduce floating-point operations and computational complexity during the convolution process. Second, as it is not feasible to construct a lightweight model with a large depth to achieve sufficient accuracy, a new lightweight convolution technique is introduced. GSConv, combining the GS bottleneck and the efficient cross stage partial block (VoV-GSCSP), replaces the feature fusion layer to achieve lightweight feature enrichment. To test and train the model, a tomato leaf disease dataset was constructed. The improved model demonstrated higher accuracy and fewer parameters on the tomato leaf disease dataset. The improved model achieved an mean average precision 50 (mAP50) of 94.9 $%$ and an mAP50:95 of 75.6 $%$ , representing increases of 1.9 $%$ and 2.8 $%$ over the original model, respectively. The number of parameters is only 2 322 262, a reduction of 22.8 $%$ compared to the original model. This method meets the daily needs of tomato leaf disease detection, providing technical support for agricultural spraying robots to quickly and accurately detect tomato leaf diseases and precisely spray pesticides.
及时发现和预防番茄叶病对提高番茄产量至关重要。针对番茄叶病检测效率低的问题，本文提出了一种轻量级番茄叶病识别方法。首先，在 You only look once v8（YOLOv8）模型中引入了增强的交乘联合（enhanced intersection over union）损失函数，取代了完全交乘联合损失函数，提高了边界框定位的准确性。为了解决固定样本形状和方形卷积核不能很好地适应不同目标的问题，引入了轻量级可改变卷积核（AKConv），为卷积核提供任意参数和形状。受 AKConv 轻量级特性的启发，C2f 模块通过整合 AKConv 进行了改进，以减少卷积过程中的浮点运算和计算复杂度。其次，由于构建大深度的轻量级模型无法达到足够的精度，因此引入了一种新的轻量级卷积技术。GSConv 结合了 GS 瓶颈和高效的跨阶段部分块（VoV-GSCSP），取代了特征融合层，实现了轻量级的特征丰富。为了测试和训练该模型，构建了一个番茄叶病数据集。改进后的模型在番茄叶病数据集上表现出更高的精确度和更少的参数。改进模型的平均精确度 50 (mAP50) 为 94.9%，mAP50:95 为 75.6%，分别比原始模型提高了 1.9%和 2.8%。参数数仅为 2 322 262，比原始模型减少了 22.8%。该方法满足了番茄叶片病害检测的日常需求，为农业喷洒机器人快速准确地检测番茄叶片病害、精准喷洒农药提供了技术支持。
{"title":"Lightweight Tomato Leaf Intelligent Disease Detection Model Based on Adaptive Kernel Convolution and Feature Fusion","authors":"Baofeng Ji;Haoyu Li;Xin Jin;Ji Zhang;Fazhan Tao;Peng Li;Jianhua Wang;Huitao Fan","doi":"10.1109/TAFE.2024.3445119","DOIUrl":"https://doi.org/10.1109/TAFE.2024.3445119","url":null,"abstract":"Timely detection and prevention of tomato leaf diseases are crucial for improving tomato yields. To address the issue of low efficiency in detecting tomato leaf diseases, this article proposes a lightweight tomato leaf disease recognition method. First, enhanced intersection over union is introduced in the you only look once v8 (YOLOv8) model to replace the complete intersection over union loss function, enhancing the accuracy of bounding box localization. To solve the problem of fixed sample shapes and square convolution kernels not adapting well to different targets, lightweight alterable Kernel convolution (AKConv) is introduced, providing arbitrary parameters and shapes for the convolution kernel. Inspired by the lightweight characteristics of AKConv, the C2f module is improved by integrating AKConv to reduce floating-point operations and computational complexity during the convolution process. Second, as it is not feasible to construct a lightweight model with a large depth to achieve sufficient accuracy, a new lightweight convolution technique is introduced. GSConv, combining the GS bottleneck and the efficient cross stage partial block (VoV-GSCSP), replaces the feature fusion layer to achieve lightweight feature enrichment. To test and train the model, a tomato leaf disease dataset was constructed. The improved model demonstrated higher accuracy and fewer parameters on the tomato leaf disease dataset. The improved model achieved an mean average precision 50 (mAP50) of 94.9\u0000<inline-formula><tex-math>$%$</tex-math></inline-formula>\u0000 and an mAP50:95 of 75.6\u0000<inline-formula><tex-math>$%$</tex-math></inline-formula>\u0000, representing increases of 1.9\u0000<inline-formula><tex-math>$%$</tex-math></inline-formula>\u0000 and 2.8\u0000<inline-formula><tex-math>$%$</tex-math></inline-formula>\u0000 over the original model, respectively. The number of parameters is only 2 322 262, a reduction of 22.8\u0000<inline-formula><tex-math>$%$</tex-math></inline-formula>\u0000 compared to the original model. This method meets the daily needs of tomato leaf disease detection, providing technical support for agricultural spraying robots to quickly and accurately detect tomato leaf diseases and precisely spray pesticides.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"2 2","pages":"563-575"},"PeriodicalIF":0.0,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142408745","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 assumption of climate homogeneity is no longer acceptable in greenhouse farming since it can result in less-than-ideal decisions. At the same time, installing a sensor in each area of interest is costly and unsuitable for field operations. In this article, we address this problem by putting forth the idea of virtual sensors; their behavior is modeled by a context-aware recurrent neural network trained through the contextual relationships between a small set of permanent monitoring stations and a set of temporary sensors placed in specific points of interest for a short period. More precisely, we consider not only space location but also temporal features and distance with respect to the permanent sensors. This article shows the complete pipeline to configure the recurrent neural network, perform training, and deploy the resulting model into an embedded system for on-site application execution.
{"title":"Estimating Greenhouse Climate Through Context-Aware Recurrent Neural Networks Over an Embedded System","authors":"Claudio Tomazzoli;Elia Brentarolli;Davide Quaglia;Sara Migliorini","doi":"10.1109/TAFE.2024.3441470","DOIUrl":"https://doi.org/10.1109/TAFE.2024.3441470","url":null,"abstract":"The assumption of climate homogeneity is no longer acceptable in greenhouse farming since it can result in less-than-ideal decisions. At the same time, installing a sensor in each area of interest is costly and unsuitable for field operations. In this article, we address this problem by putting forth the idea of virtual sensors; their behavior is modeled by a context-aware recurrent neural network trained through the contextual relationships between a small set of permanent monitoring stations and a set of temporary sensors placed in specific points of interest for a short period. More precisely, we consider not only space location but also temporal features and distance with respect to the permanent sensors. This article shows the complete pipeline to configure the recurrent neural network, perform training, and deploy the resulting model into an embedded system for on-site application execution.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"2 2","pages":"554-562"},"PeriodicalIF":0.0,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142408786","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}
Crop yield prediction is a crucial task in agricultural science, involving the classification of potential yield into various levels. This is vital for both farmers and policymakers. The features considered for this task are diverse, including weather, soil, and historical yield data. Recently, plant images captured in different modalities, such as red–green–blue, infrared, and multispectral bands, have also been utilized. Most of these data are inherently temporal. Integrating such multimodal and temporal data is advantageous for yield classification. In this work, a deep learning framework based on meta-transformers and temporal graph neural networks has been proposed to achieve this goal. Meta-Transformers allow the modeling of multimodal interactions, while temporayel graph neural networks enable the utilization of time sequences. Experimental results on the publicly available EPFL multimodal dataset demonstrate that the proposed framework achieves a high classification accuracy of nearly 97%, surpassing other state-of-the-art models, such as long short-term memory networks, 1-D convolutional neural networks, and Transformers. In addition, the proposed model excels in accuracy metrics, with a precision of approximately 98%, an F1-Score of 91%, and a recall of 94% in crop yield prediction.
{"title":"Crop Yield Prediction Using Multimodal Meta-Transformer and Temporal Graph Neural Networks","authors":"Somrita Sarkar;Anamika Dey;Ritam Pradhan;Upendra Mohan Sarkar;Chandranath Chatterjee;Arijit Mondal;Pabitra Mitra","doi":"10.1109/TAFE.2024.3438330","DOIUrl":"https://doi.org/10.1109/TAFE.2024.3438330","url":null,"abstract":"Crop yield prediction is a crucial task in agricultural science, involving the classification of potential yield into various levels. This is vital for both farmers and policymakers. The features considered for this task are diverse, including weather, soil, and historical yield data. Recently, plant images captured in different modalities, such as red–green–blue, infrared, and multispectral bands, have also been utilized. Most of these data are inherently temporal. Integrating such multimodal and temporal data is advantageous for yield classification. In this work, a deep learning framework based on meta-transformers and temporal graph neural networks has been proposed to achieve this goal. Meta-Transformers allow the modeling of multimodal interactions, while temporayel graph neural networks enable the utilization of time sequences. Experimental results on the publicly available EPFL multimodal dataset demonstrate that the proposed framework achieves a high classification accuracy of nearly 97%, surpassing other state-of-the-art models, such as long short-term memory networks, 1-D convolutional neural networks, and Transformers. In addition, the proposed model excels in accuracy metrics, with a precision of approximately 98%, an F1-Score of 91%, and a recall of 94% in crop yield prediction.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"2 2","pages":"545-553"},"PeriodicalIF":0.0,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142408785","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 evolving landscape of precision agriculture, the integration of remote pest traps with deep learning technologies marks a critical step forward in remote pest detection, with the potential to substantially improve traditional pest monitoring methods. This article provides a comprehensive review of the developments, challenges, and innovative solutions in creating sensor-based electronic traps and applying deep learning for efficient and autonomous pest identification. By addressing the complexities of sensor integration, data collection, and the need for adaptive algorithms capable of classifying a wide range of insect pests, this review highlights the effective combination of electronic trap advancements with the precision offered by convolutional neural networks. An in-depth analysis of the technological advancements in electronic pest trap development is presented, highlighting improvements in design, efficiency, and sustainability while referring to ongoing and future challenges. Moreover, this article explores deep learning techniques, emphasizing on dataset enhancement and model optimization to overcome traditional challenges such as data scarcity and to improve the robustness of pest detection models. A thorough evaluation of various trap types against 85 unique pests is conducted, with the delta trap emerging as the most versatile, showcasing compatibility with multiple sensors and effectiveness against various pests. This review equips researchers, practitioners, and agricultural developers with critical insights and methodologies that can significantly enhance pest monitoring efficiency, reduce pesticide usage, and support sustainable agricultural practices.
{"title":"Insect Pest Trap Development and DL-Based Pest Detection: A Comprehensive Review","authors":"Athanasios Passias;Karolos-Alexandros Tsakalos;Nick Rigogiannis;Dionisis Voglitsis;Nick Papanikolaou;Maria Michalopoulou;George Broufas;Georgios Ch. Sirakoulis","doi":"10.1109/TAFE.2024.3436470","DOIUrl":"https://doi.org/10.1109/TAFE.2024.3436470","url":null,"abstract":"In the evolving landscape of precision agriculture, the integration of remote pest traps with deep learning technologies marks a critical step forward in remote pest detection, with the potential to substantially improve traditional pest monitoring methods. This article provides a comprehensive review of the developments, challenges, and innovative solutions in creating sensor-based electronic traps and applying deep learning for efficient and autonomous pest identification. By addressing the complexities of sensor integration, data collection, and the need for adaptive algorithms capable of classifying a wide range of insect pests, this review highlights the effective combination of electronic trap advancements with the precision offered by convolutional neural networks. An in-depth analysis of the technological advancements in electronic pest trap development is presented, highlighting improvements in design, efficiency, and sustainability while referring to ongoing and future challenges. Moreover, this article explores deep learning techniques, emphasizing on dataset enhancement and model optimization to overcome traditional challenges such as data scarcity and to improve the robustness of pest detection models. A thorough evaluation of various trap types against 85 unique pests is conducted, with the \u0000<italic>delta trap</i>\u0000 emerging as the most versatile, showcasing compatibility with multiple sensors and effectiveness against various pests. This review equips researchers, practitioners, and agricultural developers with critical insights and methodologies that can significantly enhance pest monitoring efficiency, reduce pesticide usage, and support sustainable agricultural practices.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"2 2","pages":"323-334"},"PeriodicalIF":0.0,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142430749","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 : 2024-08-05DOI: 10.1109/TAFE.2024.3434309
Kamlesh S. Patle;Neha Sharma;Priyanka Khaparde;Harsh Varshney;Gulafsha Bhatti;Yash Agrawal;Vinay S. Palaparthy
Prediction of plant diseases is essential to reduce crop loss. Early disease prediction models have been investigated for this purpose, where data on leaf wetness duration (LWD) is one of the key components. Leaf wetness sensors (LWSs) are used to better understand how foliar wetness affects plant disease cycles and epidemic development. LWS can be fabricated on printed circuit boards (PCBs), where interdigitated electrode patterns are widely used. However, it is important to understand the efficacy of these patterns for in-situ measurements. For this purpose, in this work, we have fabricated three different patterns viz. circular, oval, and rectangular on the PCB and tested their efficacy during lab and field measurements. Lab measurements indicate that the circular patterned LWS offers a sensitivity of about 1600% over the dry-to-wet range, which is about 2 and 1.5 times more than oval and rectangular patterns, respectively. Besides this, circular patterned LWS offers the hysteresis of about 2%, whereas the oval and rectangular patterned LWS show about 3% and 7%, respectively. Field measurement results specify that circular patterned LWS and commercial LWS Phytos 31 indicate the same number of LWD events. However, oval and rectangular patterned LWS shows extra false events.
{"title":"Impact of Electrode Patterns Variation on the Response Characteristic of Leaf Wetness Sensors","authors":"Kamlesh S. Patle;Neha Sharma;Priyanka Khaparde;Harsh Varshney;Gulafsha Bhatti;Yash Agrawal;Vinay S. Palaparthy","doi":"10.1109/TAFE.2024.3434309","DOIUrl":"https://doi.org/10.1109/TAFE.2024.3434309","url":null,"abstract":"Prediction of plant diseases is essential to reduce crop loss. Early disease prediction models have been investigated for this purpose, where data on leaf wetness duration (LWD) is one of the key components. Leaf wetness sensors (LWSs) are used to better understand how foliar wetness affects plant disease cycles and epidemic development. LWS can be fabricated on printed circuit boards (PCBs), where interdigitated electrode patterns are widely used. However, it is important to understand the efficacy of these patterns for in-situ measurements. For this purpose, in this work, we have fabricated three different patterns viz. circular, oval, and rectangular on the PCB and tested their efficacy during lab and field measurements. Lab measurements indicate that the circular patterned LWS offers a sensitivity of about 1600% over the dry-to-wet range, which is about 2 and 1.5 times more than oval and rectangular patterns, respectively. Besides this, circular patterned LWS offers the hysteresis of about 2%, whereas the oval and rectangular patterned LWS show about 3% and 7%, respectively. Field measurement results specify that circular patterned LWS and commercial LWS Phytos 31 indicate the same number of LWD events. However, oval and rectangular patterned LWS shows extra false events.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"2 2","pages":"536-544"},"PeriodicalIF":0.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142408784","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 : 2024-07-31DOI: 10.1109/TAFE.2024.3432837
Marwane Rezzouki;Guillaume Ferré
In recent years, connecting fishing nets underwater has received much interest in carrying out fishing activities efficiently and protecting the ocean from pollution, especially from ghost fishing. In this article, we propose a hybrid acoustic system for communication and localization underwater. This system offers fishers the ability to enhance their fishing activities by establishing a reliable data link and facilitating the tracking of the fishing nets. The proposed system is based on a technique called differential chirp spread spectrum to connect fishing nets underwater. Moreover, multiple synchronized hydrophones are used at the receiver to calculate the time differential of arrival and then estimate the location of the acoustic sources. The communication performance of the proposed system is evaluated in terms of bit error rate using simulation and ocean experiments, whereas the localization performance is presented as a root-mean-square error using Bellhop-based channel modeling for network simulation.
{"title":"Sustainable Fishing: Chirp-Based Signals for Underwater Acoustic Communication and Localization","authors":"Marwane Rezzouki;Guillaume Ferré","doi":"10.1109/TAFE.2024.3432837","DOIUrl":"https://doi.org/10.1109/TAFE.2024.3432837","url":null,"abstract":"In recent years, connecting fishing nets underwater has received much interest in carrying out fishing activities efficiently and protecting the ocean from pollution, especially from ghost fishing. In this article, we propose a hybrid acoustic system for communication and localization underwater. This system offers fishers the ability to enhance their fishing activities by establishing a reliable data link and facilitating the tracking of the fishing nets. The proposed system is based on a technique called differential chirp spread spectrum to connect fishing nets underwater. Moreover, multiple synchronized hydrophones are used at the receiver to calculate the time differential of arrival and then estimate the location of the acoustic sources. The communication performance of the proposed system is evaluated in terms of bit error rate using simulation and ocean experiments, whereas the localization performance is presented as a root-mean-square error using Bellhop-based channel modeling for network simulation.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"2 2","pages":"527-535"},"PeriodicalIF":0.0,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142408781","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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