Self-Supervised Learning Improves Agricultural Pest Classification

Soumyashree Kar, Koushik Nagasubramanian, Dinakaran Elango, Ajay Nair, Daren S. Mueller, Matthew E. O’Neal, Asheesh K Singh, Soumik Sarkar, Baskar Ganapathysubramanian, Arti Singh

Abstract: Globally, crop insect pests lead to 10 – 40% yield loss. However, crop insect pest detection and mitigation remain an extremely challenging task for the farmers, due to several factors. While supervised learning has achieved a remarkable feat in insect detection, it requires significant human intervention in labeling the input data, thereby making the downstream tasks tedious and sometimes infeasible. This is particularly the case for identifying insects in the field, where labeling is tedious. Here, we present a self-supervised learning (SSL) approach – Bootstrap your own latent (BYOL) to classify 12 types of agricultural insect pests using minimal labeling. Both raw and segmented images were separately fed to the BYOL SSL method, and the linear classification ac-curacies from the representations learned were examined. The results indicate that using segmented images as input to BYOL could lead up to 94% classification accuracy.