Deep learning based classification for paddy pests & diseases recognition

Pests and diseases are a threat to paddy production, especially in Indonesia, but identification remains to be a challenge in massive scale and automatically. Increasing smartphone usage and deep learning advance create an opportunity to answer this problem. Collecting 4,511 images from four language using search engines, and augment it to develop diverse data set. This dataset fed into CaffeNet model and processed with Caffe framework. Experiment result in the model achieved accuracy 87%, which is higher than random selection 7.6%.