Agricultural productivity has a critical role in maintaining economies, especially in nations where a significant proportion of the population is engaged in farming. Plant diseases are a serious risk to agricultural p...
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The identification and classification of cassava infections are paramount due to their detrimental impact on agricultural productivity. This study conducts a comparative analysis to assess the effectiveness of various...
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ISBN:
(纸本)9798350359299
The identification and classification of cassava infections are paramount due to their detrimental impact on agricultural productivity. This study conducts a comparative analysis to assess the effectiveness of various deep learning techniques in classifying cassava leaf diseases. Transformer-Embedded ResNet, EfficientNetV2 with visual attention, and a mobile-based model are explored to address the challenges posed by an imbalanced dataset. Leveraging deep Convolutional Neural Networks (CNNs) and attention mechanisms, these models exhibit enhanced precision and effectiveness. Evaluations measure accuracy, precision, recall, and F1-score, considering feasibility and computational complexity. Results indicate that the proposed models effectively control cassava infections compared to current methods. This study underscores the precision and efficacy of plant disease identification using deep learning techniques, presenting modern methodologies for thorough evaluation. With agriculture supporting a significant portion of the world's population, AI-powered automation offers solutions to production challenges. AI enhances agricultural precision, monitors crop health, identifies diseases, and forecasts weather conditions, particularly advantageous in addressing manpower shortages. Automated detection is crucial for mitigating agricultural disease risks and ensuring food security. Focusing on the examination of CNNs and neural networks, this study utilizes deep learning techniques to identify cassava leaf diseases using the Kaggle dataset and real-time photographs. Performance evaluation and enhancement are discussed, with a specific focus on the impact of disease on the Thai cassava crop. Experimental trials demonstrate the effectiveness of deep learning in automating cassava disease categorization, particularly in detecting brown streak virus illness, yielding notable F-measure and accuracy. This study advances disease classification and calls for further research in the
Over the past decades, integration of wireless sensor networks (WSNs) and computer vision (CV) technology has shown promising results in mitigating crop losses caused by wild animal attacks. Studies have demonstrated ...
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Over the past decades, integration of wireless sensor networks (WSNs) and computer vision (CV) technology has shown promising results in mitigating crop losses caused by wild animal attacks. Studies have demonstrated the effectiveness of these technologies in providing real-time monitoring and early detection of animal intrusions into agricultural fields. By deploying WSNs equipped with motion sensors and cameras, farmers can receive instant alerts when wild animals enter their fields, allowing for timely intervention to prevent crop damage. Furthermore, advancements in CV algorithms possess made possible to automatically detect and classify the animal species, facilitating targeted response strategies. For example, sophisticated image processing techniques can differentiate between harmless birds and destructive mammals, allowing farmers to focus their efforts on deterring the most damaging species. Field trials and pilot projects implementing WSN-CV systems have reported significant reductions in crop losses attributed to wild animal raids. By leveraging data collected through sensor networks and analyzed using computer vision algorithms, farmers can make informed decisions regarding pest and insect management strategies. This data-driven approach has led to more efficient utilization of resources, such as targeted application of insecticides and pesticides, resulting in both economic and environmental benefits. Moreover, the integration of WSN-CV technology has enabled the development of innovative deterrent systems that leverage artificial intelligence and automation. These systems can deploy non-lethal methods, such as sound or light-based repellents, to deter wild animals without causing harm to the environment or wildlife populations. Overall, the combination of wireless sensor networks and computer vision technology provides the promising resolution to the long-standing issue of wild animal-related losses in agriculture. By harnessing the power of data and a
Robots have emerged as versatile tools with significant potential to enhance teaching and learning environments, including classrooms, laboratories, play homes, crèches, and even at home. Their engaging nature ca...
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The challenge of identifying fake news through text classification has been met with extensive experimentation to find the best mix of preprocessing techniques and algorithms. Despite efforts, an effective fake news d...
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The commonly used trial-and-error method of biodegradable Zn alloys is costly and *** this study,based on the self-built database of biodegradable Zn alloys,two machine learning models are established by the first tim...
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The commonly used trial-and-error method of biodegradable Zn alloys is costly and *** this study,based on the self-built database of biodegradable Zn alloys,two machine learning models are established by the first time to predict the ultimate tensile strength(UTS)and immersion corrosion rate(CR)of biodegradable Zn alloys.A real-time visualization interface has been established to design Zn-Mn based alloys;a representative alloy is *** tensile mechanical properties and immersion corrosion rate tests,its UTS reaches 420 MPa,and the prediction error is only 0.95%.CR is 73μm/a and the prediction error is 5.5%,which elevates 50 MPa grade of UTS and owns appropriate corrosion ***,influences of the selected features on UTS and CR are discussed in *** combined application of UTS and CR model provides a new strategy for synergistically regulating comprehens-ive properties of biodegradable Zn alloys.
December 2019 witnessed the outbreak of a novel coronavirus, thought to have started in the Chinese city of Wuhan. The situation worsened owing to its quick spread across the globe, leading to a worldwide pandemic tha...
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Recommender systems have become pervasive in guiding users through a lot of choices available in today’s digital landscape. This paper presents a content-based recommender system focused specifically for movie recomm...
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The Olympic games are considered one of the most prestigious and renowned games across the globe. The first modern Olympics was held in Athens in 1896 and since then all the countries and their players have shown keen...
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The AI-Enhanced Learning Assistant Platform is a revolutionary system designed to enhance learning, with cutting-edge features like question and answer generation, answer evaluation, identification of weak areas, recu...
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