Multi-objective Big Bang Big Crunch framework for reliable rice disease and variety classification with conditional calibration
Chatter Singh, Amar Singh, Sahraoui Dhelim
TL;DR
This paper introduces a new framework for classifying rice diseases and varieties that improves model reliability and efficiency for real-world use.
Contribution
The MO-BBBC framework introduces conditional temperature scaling to jointly optimize multiple deployment criteria for rice classification.
Findings
MO-BBBC achieves 90.6% disease accuracy and 97.9% variety accuracy.
The framework improves calibration to AECE=0.0138, significantly better than post-hoc baselines.
It supports real-time inference with low latency and energy consumption.
Abstract
Deploying rice disease detectors in the field remains challenging because models that are accurate in the lab are often poorly calibrated and provide limited uncertainty estimates, raising the risk of costly misclassification. This paper proposes a multi-objective Big-Bang Big-Crunch (MO-BBBC) framework that jointly performs disease detection and variety classification while optimizing six deployment-oriented criteria: classification error, calibration quality, uncertainty estimation, model size, inference latency, and energy consumption. The proposed framework presents conditional temperature scaling, an adaptive scheme that mitigates over-calibration and preserves reliability. The framework is implemented in Python on a lightweight two-headed classifier and evaluated on the Paddy Doctor dataset, MO-BBBC base framework achieves 90.6% disease accuracy and 97.9% variety accuracy;…
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Taxonomy
TopicsSmart Agriculture and AI · Biosensors and Analytical Detection · Remote Sensing in Agriculture