Non-Invasive Multimodal and Multiscale Bioelectrical Sensor System for Proactive Holistic Plant Assessment

Abstract

Global crop losses of 20–40% continue because traditional plant assessment methods are either invasive, damaging plant tissues, or reactive, detecting stress only after visible symptoms. Recent developments have remained fragmented, focusing on single modalities, individual organs, or limited frequency ranges. This study developed a unified bioelectrical sensor system capable of non-invasive, multimodal, multiscale, and integrative assessment by integrating capabilities that existing methods address only separately. The system combines spectroscopy and tomography within a single platform, enabling simultaneous evaluation of multiple organs. Unlike approaches confined to narrow frequencies, it captures complete physiological responses across scales. Validation on strawberry (Fragaria × ananassa ‘Sweet Charlie’) demonstrated comprehensive multi-organ assessment: 98.3% accuracy for fruit categorization, 95.8% for leaf water status, and 88.2% for stem productivity. Tomographic performance reached 2.6–2.8 mm resolution for 3D root mapping and 2.8–3.0 mm for 2D postharvest fruit sorting. Correlations with reference metrics were used exclusively for validation, confirming that the extracted features reflect genuine physiological variations. Importantly, the system detects stress before visible symptoms, enabling intervention within the reversible window. By unifying spectroscopy and tomography with complete frequency coverage and multi-organ capability, this platform overcomes existing fragmentation and establishes a foundation for proactive, comprehensive plant monitoring essential for sustainable agriculture.

Plant phenotyping relevance

植物の生理状態を非侵襲的に取得するマルチモーダル・マルチスケール生体電気センサー基盤を開発し、果実・葉・茎・根の評価と基準指標による検証を行っており、表現型取得法が研究の中心です。

Code and data availability

The supplied blocks describe the bioelectrical sensor system, strawberry measurement protocols, and analysis methods, but contain no data availability statement, no public dataset or code deposit, and no author-provided URLs. No paper-specific public asset can be identified.

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