An Entire-and-Partial Feature Transfer Learning Approach for Detecting the Frequency of Pest Occurrence

Abstract

Detecting the frequency of the pest occurrence is always a time consuming and laborious task for agriculture. This paper attempts to solve the problem through the combination of deep learning and pest detection. We propose an entire-and-partial feature transfer learning architecture to perform pest detection, classification and counting tasks which can reach the final goal for detecting the frequency of pest occurrence. In the partial-feature transfer learning, different fine-grained feature map are strengthened by using the weighting scheme of the entire-feature transfer learning. Finally, the cross-layer of the entire-feature network is combined with the multi-scale feature map. The entire-feature transfer learning approach enhances the feature map by creating a shortcut topology for the input and output layers to reduce the gradient disappearance problem which is common to deep networks. The experimental results show that the detection accuracy can be significantly improved and the accuracy can reach 90.2%.