Today, the Logistic equations are widely applied to simulate the population growth across a range of fields, chiefly, demography and ecology. Based on an assumption that growth-regulating factors within the Logistic model, namely, the rate of increase (r) and carrying capacity (K), can be considered as the functions reflecting the combination of the organism- and environment-specific parameters, here, we discussed the possible application of modified synthetic Logistic equations to the simulation of the changes in (1) population (density per volume) of photosynthetically growing free-living algae and (2) size (mass per individual) of higher plants, by newly composing r value as a function reflecting the photosynthetic activities. Since higher plants are multi-cellular organisms, a novel concept for the carrying capacity K must also be introduced. We brought the a priori assumption that information sharing amongst cells strongly influences the physiology of multi-cellular structures eventually defining the maximum size of plants, into view. A simplest form of ‘synthetic organism’ conformed to test this assumption is a linear chain of cells, and the first physiological phenomenon, modeled in this way, is growth. This combination of information flow along a chain, with exponential growth, produces a simple allotropic relationship. This relationship is compared with results for plants and is found to have excellent predictive power. This theory shows that fast-growing organisms, or multicellular structures, remain small, because of their inability to share information sufficiently quickly and, also, predicts determinate growth. The success of this simple model suggests, firstly, that the inclusion of information flows in theoretical physiology models, which have been, to date, dominated by energetic or metabolic assumptions, will be improved by incorporating information flows. Secondly, the application of more complex information theories, such as those of Shannon, to biological systems will offer deep insights into the mechanisms and control of intercellular communication.

Herbivorous arthropods, such as spider mites, are one of the major causes of annual crop losses. They are usually hard to spot before a severe infestation takes place. When feeding, these insects cause external perturbation that triggers changes in the underlying physiological process of a plant, which are expressed by a generation of distinct variations of electrical potential. Therefore, plant electrophysiology data portray information of the plant state. Analyses involving machine learning techniques applied to plant electrical response triggered by spider mite infestation have not been previously reported. This study investigates plant electrophysiological signals recorded from 12 commercial tomatoes plants contaminated with spider mites and proposes a workflow based on Gradient Boosted Tree algorithm for an automated differentiation of the plant’s normal state from the stressed state caused by infestation. The classification model built using the signal samples recorded during daylight and employing a reduced feature subset performs with an accuracy of 80% in identifying the plant’s stressed state. Furthermore, the Hjorth complexity encloses the most relevant information for discrimination of the plant status. The obtained findings open novel access towards automated detection of insect infestation in greenhouse crops and, consequently, more optimal prevention and treatment approaches.

Automated monitoring of plant health is becoming a crucial component for optimizing agricultural production. Recently, several studies have shown that plant electrophysiology could be used as a tool to determine plant status related to applied stressors. However, to the best of our knowledge, there have been no studies relating electrical plant response to general stress responses as a proxy for plant health. This study models general stress of plants exposed to either biotic or abiotic stressors, namely drought, nutrient deficiencies or infestation with spider mites, using electrophysiological signals acquired from 36 plants. Moreover, in the signal processing procedure, the proposed workflow reuses information from the previous steps, therefore considerably reducing computation time regarding recent related approaches in the literature. Careful choice of the principal parameters leads to a classification of the general stress in plants with more than 80% accuracy. The main descriptive statistics measured together with the Hjorth complexity provide the most discriminative information for such classification. The presented findings open new paths to explore for improved monitoring of plant health.

Both action potentials and mechanosensitive signalling are an important communication mechanisms in plants. Considering an information-theoretic framework, this paper explores the effective range of multiple action potentials for a long chain of cells (i.e., up to 100) in different configurations, and introduces the study of multiple mechanosensitive activation signals (generated due to a mechanical stimulus) in plants. For both these signals, we find that the mutual information per cell and information propagation speed tends to increase up to a certain number of receiver cells. However, as the number of cells increase beyond 10 to 12, the mutual information per cell starts to decrease. To validate our model and results, we include an experimental verification of the theoretical model, using a PhytlSigns biosignal amplifier, allowing us to measure the magnitude of the voltage associated with the multiple AP’s and mechanosensitive activation signals induced by different stimulus in plants. Experimental data is used to calculate the mutual information and information propagation speed, which is compared with corresponding numerical results. Since these signals are used for a variety of important tasks within the plant, understanding them may lead to new bioengineering methods for plants.

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.

In this paper we show how inter-cellular molecular communication may change the overall levels of photosynthesis in plants. Individual plant cells respond to external stimuli, such as illumination levels, to regulate their photosynthetic output. Here, we present a mathematical model which shows that by sharing information internally using molecular communication, plants may increase overall photosynthate production. Numerical results show that higher mutual information between cells corresponds to an increase in overall photosynthesis by as much as 25 per cent. This suggests that molecular communication plays a vital role in maximising the photosynthesis in plants and therefore suggests new routes to influence plant development in agriculture and elsewhere.