## 1 Introduction  
Automated monitoring of plant development is becoming a key enabler of for optimized agricultural production [1, 2]. The development of advanced technologies and digitalization introduces strong potential to revolutionize all sectors of activity, including agriculture and especially controlled condition crop production such as greenhouse systems. Controlled growth condition systems are typically used to grow high-value crops such as vegetables, spices or ornamental plants. These systems are highly intensive and the most capital-demanding agricultural production systems; therefore, any reduction in their productivity has an important and immediate financial impact. Many factors contribute to the quality and quantity of crop yield, including supply of the correct amount of irrigation and nutrients, as well as the prevention of losses from pests and diseases. For successful control, early detection and identification of problems is vital. Therefore, access to a tool providing an alert of a possible \"danger\" to plants' health would allow prompt interventions and improved regulation of growing conditions which would help reduce crop losses. In addition, early diagnosis would lead to reduced application of agrochemicals and an increased use of environmentally friendly practices, such as biological control agents. In other words, automated plant health monitoring could lead to significantly improved yields and efficient and environmentally sustainable crop protection. Plants, as sessile organisms, are responding and acclimating to changes in environmental conditions for survival. They have developed different signaling systems that allow integration of environmental cues to coordinate molecular processes associated to both early development and adult plant physiology [3]. Among the different signaling pathways, electrical signals, known as electrophysiology, are a widely observed phenomenon and the most efficient for rapid transfer of information over long distances. It is well established that both plants and animals utilize long-range electrical signaling to transduce environmental information to the whole body [3, 4]. The reaction of plants to either biotic or abiotic stress can be identified by electrical potential variations resulting from the changes in the underlying physiological process that the stressor triggers in a plant [5, 6, 7]. Hence, the electrophysiological signals naturally occurring within plants have strong potential for identifying the plant health status. Several studies employing machine learning techniques have demonstrated that the electrophysiological plant response encodes signal patterns discriminating plant status related to the applied stressor [8, 9, 10, 11, 12]. Electrophysiological signals related to various stimuli, such as environmental stressors [8], pollutants [9, 10], salt abundance [11], and fungal infection [12], have been analyzed in tomato, cucumber, soybeans, cabbage and wheat. Often these investigations were conducted strictly in controlled laboratory conditions using Faraday cages, limiting an understanding of their applicability in normal agricultural environments. Recent work [13] has demonstrated the possibility of acquiring continuous and stable long-term plant electrophysiology signals in typical greenhouse conditions. Further studies have shown that the signal recordings encode discriminative patterns for stress related to drought [13] and to the presence of spider mites [14]. To the best of our knowledge, there have not yet been reported studies based on plant electrical responses, evaluating the stressed state of the plant in a broad sense without relating it to a specific stressor. An automated identification of a general stress in plants would automatically indicate to growers that there is something wrong with their crop long in advance of visual symptoms. It could therefore lead to more effective scouting, lower costs and significantly improved monitoring of crop health.