Signature Feature Marking Enhanced IRM Framework for Drone Image Analysis in Precision Agriculture

This paper reports drone imagery-based precision agriculture application for coconut health management by detecting rhinoceros beetle infestation in coconut trees. Drone imagery is advantageous for its bird’s eye view of the farms helping in analysis of coconut crown from top. Locating and segmenting individual tree-crown is challenging, as every image contains up to 30 tree-crowns with complex backgrounds such as textured soil, shadows, companion planting. Dataset generated using 1,212 drone captured images containing 9727 individual coconut tree crowns. In this work, we are proposing enhancement to Invariant Risk Minimization (IRM) framework which is Signature Feature Marking (SFM) enhanced IRM for object classification. The proposed rhinoceros beetle infestation detection model is two stage process, (1) Applying existing IRM framework for crown detection and (2) SFM enhanced IRM for crown classification. IRM based crown detection model obtained 97.3% precision and 92% recall score and the SFM enhanced IRM classification model obtained accuracy of 85.03%. SFM learns relevant signature features from the images and IRM learns the causal correlation-based features. This demonstrates that drone imagery in precision agriculture using proposed approach can be effectively used to monitor the well-being of a large plantation.