| Summary: | 碩士 === 國立臺北大學 === 資訊工程學系 === 107 === The frequency of pest occurrence has always been a task of agricultural time and labor. This paper attempts to solve the above problems through the combination of deep learning and agriculture. We propose an entire-and-partial feature transfer learning scheme to perform pest detection, classification and counting, to offer the result of pest occurrence frequency.
In the partial-feature transfer learning, the fine-grained feature map of the partial-feature transfer learning is used to strengthened the entire-feature transfer learning.
Finally, different fine-grained feature map are strengthened to the entire-feature transfer learning use weight scheme and the cross-layer of the entire-feature network is combined with multi-scale feature map. The entire-feature transfer learning approach enhances the feature by creating a shortcut topology using cross layer mechanism to reduce the gradient disappearance problem.
The experimental results shows that the detection and classification of the entire-and partial feature transfer learning mechanism can be significantly improved, and the method can reach 90.2%.
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