Early detection of cotton droughtstress using a hybrid GMR–Hallinverse-magnetostrictive microforce sensor

Abstract

Background : The inherent physiological response of cotton plants to early drought stress manifests as minute, continuous changes in stem tissue tension. Accurately quantifying these subtle mechanical cues is paramount for the timely identification of water deficits and the implementation of precise irrigation strategies. Nevertheless, current weak-force sensing methodologies are frequently limited by insufficient sensitivity and stability, particularly when confronted with pervasive electromagnetic interference from agricultural machinery and significant diurnal temperature fluctuations. Methods : To address these limitations, this study introduces a micro-force sensing paradigm that integrates the inverse magnetostrictive (Villari) effect with a heterogeneous GMR-Hall fusion architecture. A spatial differential processing method was formulated to physically suppress common-mode electromagnetic noise and thermal drift, thereby enabling robust extraction of weak magnetoelectric signals induced by stem micro-tension. Results : Experimental validation established a static sensitivity of 152.1 mV/N and a mean absolute error of 0.016 mN for force inversion across a 0–0.5 N operational range. During in vivo cotton drought stress trials, the detection system consistently captured minute variations in stem micro-tension. The heterogeneous architecture demonstrated substantial robustness amidst fluctuating environmental temperatures and complex stray magnetic fields, thereby enabling the precise demarcation of the onset of the early drought transitional stage. Conclusions : This methodology establishes a high-precision, non-invasive technological pathway for real-time plant water status monitoring. By effectively mitigating environmental interference constraints, the proposed hybrid sensing system provides critical biophysical data for early drought diagnosis, significantly advancing the analytical capabilities of precision agriculture.