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

實體

書目名稱Computer Vision and Machine Learning in Agriculture, Volume 2影響因子(影響力)




書目名稱Computer Vision and Machine Learning in Agriculture, Volume 2影響因子(影響力)學科排名




書目名稱Computer Vision and Machine Learning in Agriculture, Volume 2網(wǎng)絡公開度




書目名稱Computer Vision and Machine Learning in Agriculture, Volume 2網(wǎng)絡公開度學科排名




書目名稱Computer Vision and Machine Learning in Agriculture, Volume 2被引頻次




書目名稱Computer Vision and Machine Learning in Agriculture, Volume 2被引頻次學科排名




書目名稱Computer Vision and Machine Learning in Agriculture, Volume 2年度引用




書目名稱Computer Vision and Machine Learning in Agriculture, Volume 2年度引用學科排名




書目名稱Computer Vision and Machine Learning in Agriculture, Volume 2讀者反饋




書目名稱Computer Vision and Machine Learning in Agriculture, Volume 2讀者反饋學科排名

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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

救護車

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

救護車

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