Image Classification on heavy occluded and background camouflage

Question

I am doing a project on image classification on classifying various species of bamboos. The problems on Kaggle are pretty well labeled, singluar and concise pictures. But the issue with bamboo is they appear in a cluster in most images sometimes with more than 1 species. Also there is a prevalence of heavy occlusion and background camouflage.

Besides there is not much training data available for this problem. So I have been making my own dataset by collecting the data from the internet and also clicking images from my DSLR.

My first approach was to use a weighted Mask RCNN for instance segmentation and then classifying it using VGGNet and GoogleNet.

My next approach is to test on Attention UNet, YOLO v3 and a new paper BCNet from ICLR 2021. And then classify on ResNext, GoogleNet and SENet then compare the results.

Any tips or better approach is much appreciated.