XAI for Early Crop Classification

TL;DR

This paper introduces an explainable AI approach for early crop classification by identifying key time periods that balance accuracy and timeliness, enabling faster decision-making with minimal accuracy loss.

Contribution

It presents a novel method using layer-wise relevance propagation to pinpoint important time steps for early crop classification, optimizing the trade-off between speed and accuracy.

Findings

Identified the period from April 21 to August 9 as optimal for early classification.

Achieved only 0.75% accuracy loss using the selected timeframe.

LRP highlights input details that differentiate crop classes.

Abstract

We propose an approach for early crop classification through identifying important timesteps with eXplainable AI (XAI) methods. Our approach consists of training a baseline crop classification model to carry out layer-wise relevance propagation (LRP) so that the salient time step can be identified. We chose a selected number of such important time indices to create the bounding region of the shortest possible classification timeframe. We identified the period 21st April 2019 to 9th August 2019 as having the best trade-off in terms of accuracy and earliness. This timeframe only suffers a 0.75% loss in accuracy as compared to using the full timeseries. We observed that the LRP-derived important timesteps also highlight small details in input values that differentiates between different classes and