Titlebook: Computer Vision and Machine Learning in Agriculture, Volume 2; Mohammad Shorif Uddin,Jagdish Chand Bansal Book 2022 The Editor(s) (if appl

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书目名称Computer Vision and Machine Learning in Agriculture, Volume 2影响因子(影响力)




书目名称Computer Vision and Machine Learning in Agriculture, Volume 2影响因子(影响力)学科排名




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书目名称Computer Vision and Machine Learning in Agriculture, Volume 2网络公开度学科排名




书目名称Computer Vision and Machine Learning in Agriculture, Volume 2被引频次




书目名称Computer Vision and Machine Learning in Agriculture, Volume 2被引频次学科排名




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书目名称Computer Vision and Machine Learning in Agriculture, Volume 2年度引用学科排名




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书目名称Computer Vision and Machine Learning in Agriculture, Volume 2读者反馈学科排名

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Virtues

Advanced Component Architecture,d coriander) and two different orchards (loquat and peach). The developed system outperformed its competitors with 91.3% mean average precision (mAP) and a processing time of 0.235 s. Thus, the proposed framework provided an excellent potential to be deployed on autonomous systems (UAVs, robots, etc

Bridle

abject

救护车

Customizing Forms and Core Templates,essment. It can provide qualitative and quantitative data under single analysis. This chapter ensures a critical review on spectroscopic and imaging techniques combined chemo metric analysis, which achieves better accuracy of 99% for food quality analysis, role of machine learning and deep learning

救护车

Using JSPs and Servlets in Stellent,detects and classifies input plant leaf data as healthy or diseased using SVM and kNN classifier, where SVM gives better accuracy of 93.67%. The obtained results indicate that the proposed methodology outperforms the other algorithms in obtaining good classification accuracy.

Minatory

粗糙

Lasting

Customizing Forms and Core Templates, CNN-SVM classifier is shown to be a fast, extremely efficient method for classifying specific imaging features into desired disease classes, as well as giving preferable results over the plain CNN and other classifiers, such as the support vector machine (SVM) for large datasets. Finally, the exper