Machine Learning-based Early Detection of Potato Sprouting Using Electrophysiological Signals

2025-07-01

AI Summary

This study addresses post-harvest losses in potato storage caused by delayed detection of sprouting. We propose a novel early sprouting prediction method based on tuber electrophysiological signals. Custom-designed sensors acquire electrophysiological data from stored tubers; wavelet transform is applied to extract time–frequency domain features; and a supervised machine learning model—integrated with uncertainty quantification—is developed to predict sprouting onset prior to visible eye emergence. Unlike conventional vision-based approaches, our method overcomes temporal limitations, enabling prediction several days in advance, with mean absolute error within an acceptable range. The key innovation lies in the first integration of tuber electrophysiological responses, wavelet-domain feature modeling, and predictive uncertainty quantification. This work establishes a practical, non-invasive paradigm for intelligent, real-time post-harvest storage management.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Economics, Online Markets and Human Computation: The sharing economy

Abstract

Accurately predicting potato sprouting before the emergence of any visual signs is critical for effective storage management, as sprouting degrades both the commercial and nutritional value of tubers. Effective forecasting allows for the precise application of anti-sprouting chemicals (ASCs), minimizing waste and reducing costs. This need has become even more pressing following the ban on Isopropyl N-(3-chlorophenyl) carbamate (CIPC) or Chlorpropham due to health and environmental concerns, which has led to the adoption of significantly more expensive alternative ASCs. Existing approaches primarily rely on visual identification, which only detects sprouting after morphological changes have occurred, limiting their effectiveness for proactive management. A reliable early prediction method is therefore essential to enable timely intervention and improve the efficiency of post-harvest storage strategies, where early refers to detecting sprouting before any visible signs appear. In this work, we address the problem of early prediction of potato sprouting. To this end, we propose a novel machine learning (ML)-based approach that enables early prediction of potato sprouting using electrophysiological signals recorded from tubers using proprietary sensors. Our approach preprocesses the recorded signals, extracts relevant features from the wavelet domain, and trains supervised ML models for early sprouting detection. Additionally, we incorporate uncertainty quantification techniques to enhance predictions. Experimental results demonstrate promising performance in the early detection of potato sprouting by accurately predicting the exact day of sprouting for a subset of potatoes and while showing acceptable average error across all potatoes. Despite promising results, further refinements are necessary to minimize prediction errors, particularly in reducing the maximum observed deviations.

Problem

• Predict potato sprouting before visual signs appear
• Reduce reliance on banned anti-sprouting chemicals
• Improve storage efficiency with early detection

Innovation

• ML-based early sprouting detection using electrophysiological signals
• Wavelet domain feature extraction for signal analysis
• Uncertainty quantification to enhance prediction reliability