Nutrient deficiencies are one of the main causes of significant reductions in commercial crop production by affecting associated growth factors. Proper plant nutrition is crucial for crop quality and yield therefore, early and objective detection of nutrient deficiency is required. Recent literature has explored the real-time monitoring of plant electrical signal, called electrophysiology, applied on tomato crop cultivated in greenhouse. This sensor allows to identify the stressed state of a plant in the presence of different biotic and abiotic stressors by employing machine learning techniques. The aim of this study was to evaluate the potential of electrophysiology signal recordings acquired from tomato plants growing in a production greenhouse environment, to detect the stress of a plant triggered by the deficiency of several main nutrients. Based on a previously proposed workflow consisting of continuous acquisition of electrical signal then application of machine learning techniques, the minimum signal features was evaluated. This study presents classification models that are able to distinguish the plant’s stressed state with good accuracy, namely 78.5% for manganese, 78.1% for iron, 89.6% for nitrogen, and 78.1% for calcium deficiency, and therefore suggests a novel path to detect nutrient deficiencies at an early stage. This could constitute a novel practical tool to help and assist farmers in nutrition management.

养分缺乏是影响相关生长因子而导致经济作物产量大幅下降的主要原因之一。适当的植物营养对于作物质量和产量至关重要，因此需要及早客观地检测养分缺乏。最近的文献探索了植物电信号的实时监测，称为电生理学，应用于温室栽培的番茄作物。该传感器可以通过采用机器学习技术来识别植物在存在不同生物和非生物应激源时的应激状态。本研究的目的是评估从生产温室环境中生长的番茄植株获得的电生理信号记录的潜力，以检测由几种主要营养素缺乏引发的植物应激。基于先前提出的工作流程，包括连续采集电信号然后应用机器学习技术，评估最小信号特征。这项研究提出的分类模型能够以良好的精度区分植物的应激状态，即锰为78.5%，铁为78.1%，氮为89.6%，钙缺乏为78.1%，因此提出了一种检测养分的新途径早期阶段的缺陷。这可以成为帮助和协助农民进行营养管理的新颖实用工具。